System

Abstract

An object of a system according to an embodiment is to automate a process from test design to defect correction proposal.SOLUTION: A system includes a product specification reading part, a test design part, a test item creation part, a test execution part, a failure detection part, and a correction proposal part. The product specification reading unit reads a product specification. The test design unit analyzes the product specification read by the product specification reading unit and performs test design. The test item creation unit creates a test item based on the test designed by the test design unit. The test execution unit executes a test based on the test item created by the test item creation unit. The failure detection unit analyzes a result of the test executed by the test execution unit and detects a failure. The correction proposal unit proposes a correction portion based on the defect detected by the defect detection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the process from test design to proposing defect fixes was done manually, which was inefficient and time-consuming.

[0005] The system according to the embodiment aims to automate the process from test design to defect correction proposal. [Means for solving the problem]

[0006] The system according to the embodiment includes a product specification reading unit, a test design unit, a test item creation unit, a test implementation unit, a defect detection unit, and a modification proposal unit. The product specification reading unit reads the product specifications. The test design unit analyzes the product specifications read by the product specification reading unit and performs test design. The test item creation unit creates test items based on the tests designed by the test design unit. The test implementation unit implements tests based on the test items created by the test item creation unit. The defect detection unit analyzes the results of the tests implemented by the test implementation unit and detects defects. The modification proposal unit proposes modifications based on defects detected by the defect detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate the process from test design to defect correction proposal. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The automated testing system according to an embodiment of the present invention uses AI to automate everything from web service test analysis and design to the code to be modified. This system reads product specifications, designs tests, creates test items, automatically tests the target service, and suggests corrections if a defect is found. This allows the automated testing system to streamline the web service testing process and improve quality.

[0029] An automated testing system according to an embodiment includes a product specification reading unit, a test design unit, a test item creation unit, a test implementation unit, a defect detection unit, and a correction proposal unit. The product specification reading unit reads the product specifications. For example, the product specification reading unit reads technical specifications and functional requirements and analyzes their contents. The test design unit analyzes the product specifications read by the product specification reading unit and performs test design. For example, the test design unit creates test cases based on functional requirements and non-functional requirements. The test item creation unit creates test items based on tests designed by the test design unit. For example, the test item creation unit creates test items for the user authentication function, such as "Can you log in with a correct username and password?" and "Can you log in with an incorrect password?" The test implementation unit performs tests based on the test items created by the test item creation unit. For example, when testing the user authentication function, the test implementation unit automatically accesses a login screen, enters information according to the test items, and checks the results. The defect detection unit analyzes the results of the tests performed by the test implementation unit and detects defects. For example, the defect detection unit analyzes test results and identifies errors and bugs. The correction suggestion unit proposes corrections based on defects detected by the defect detection unit. For example, if there is a defect in the user authentication function, the correction suggestion unit analyzes the related code and identifies specific corrections. This allows the automated testing system according to the embodiment to streamline the testing process for web services and improve quality. For example, it becomes possible to respond quickly to changes in product specifications, significantly reducing the effort required for test design and implementation. Furthermore, early detection and correction of defects is possible, which is expected to shorten the development cycle.

[0030] The product specification reading unit can automatically detect ambiguous parts of the specifications using natural language processing technology and generate questions for the developer. For example, when the generation AI reads the product specifications, the product specification reading unit can automatically detect ambiguous parts of the specifications using natural language processing technology and generate questions for the developer. For example, if the specifications state "high speed," a question asking about that specific speed is generated. In this way, by automatically detecting ambiguous parts of the specifications and generating questions for the developer, it is possible to clarify the specifications.

[0031] The product specification reading unit can refer to specifications from similar past projects and propose optimal test designs. For example, the generation AI in the product specification reading unit can refer to specifications from similar past projects and propose optimal test designs. For example, test designs for new projects can be created based on test cases that were successful in past projects. This makes it possible to refer to specifications from similar past projects and propose optimal test designs, thereby improving the efficiency of test design.

[0032] The product specification reading unit can search for related patent documents at the same time as reading the specifications and evaluate the risk of patent infringement. For example, the product specification reading unit can check whether the technology described in the specifications infringes on existing patents at the same time as the generation AI reads the specifications and evaluate the risk of patent infringement. This allows the risk of patent infringement to be reduced by searching for patent documents at the same time as reading the specifications and evaluating the risk of patent infringement.

[0033] The product specification reading unit can visualize the contents of the specification and generate an interactive dashboard to be shared with the entire development team. For example, the product specification reading unit uses a generation AI to visualize the contents of the specification and generate an interactive dashboard to be shared with the entire development team. For example, each section of the specification can be displayed in graphs and charts. This allows the content of the specification to be visualized and shared with the entire development team, promoting information sharing and understanding.

[0034] The test item creation unit can refer to past test results and prioritize the selection of the most effective test items. For example, when the generation AI creates test items, the test item creation unit refers to past test results and prioritizes the selection of the most effective test items. For example, it prioritizes the selection of test items that have detected many defects in the past. This makes it possible to improve the efficiency of testing by referring to past test results and prioritize the selection of the most effective test items.

[0035] The test item creation unit can automatically generate different test scenarios and perform risk assessment for each scenario. For example, when the generation AI creates test items, the test item creation unit automatically generates different test scenarios and performs risk assessment for each scenario. For example, as test scenarios for a user authentication function, it generates scenarios with a correct password, an incorrect password, and no password. This allows different test scenarios to be automatically generated and risk assessment to be performed for each scenario, improving test diversity and risk management.

[0036] The test case creation department can incorporate best practices from different industries to ensure the diversity of test cases. For example, when the generative AI creates test cases, the test case creation department incorporates best practices from different industries to ensure the diversity of test cases. For example, it creates security test cases that incorporate best practices from the financial industry. This improves the quality of tests by incorporating best practices from different industries and ensuring the diversity of test cases.

[0037] The test item creation unit can reflect the results of user interviews and add test items from the user's perspective. For example, when the generation AI creates test items, the test item creation unit reflects the results of user interviews and adds test items from the user's perspective. For example, it adds test items for interfaces that users find easy to use. In this way, by reflecting the results of user interviews and adding test items from the user's perspective, user satisfaction is improved.

[0038] The test execution unit can simultaneously perform compatibility tests on different devices and browsers. For example, when the generative AI performs a test, the test execution unit simultaneously performs compatibility tests on different devices and browsers. For example, it can simultaneously check operation on smartphones, tablets, and desktop PCs. This improves test efficiency and coverage by simultaneously performing compatibility tests on different devices and browsers.

[0039] The test implementation unit can simulate network environments in different regions and conduct tests from a global perspective. For example, when the generation AI conducts a test, the test implementation unit simulates network environments in different regions and conducts tests from a global perspective. For example, it simulates network environments in the United States, Europe, and Asia. This allows for simulating network environments in different regions and conducting tests from a global perspective, thereby improving test coverage.

[0040] The test execution unit can simultaneously perform security tests and detect vulnerabilities. For example, when the generative AI performs a test, the test execution unit simultaneously performs security tests and detects vulnerabilities. For example, it detects SQL injection and cross-site scripting vulnerabilities. In this way, by simultaneously performing security tests and detecting vulnerabilities, the safety of the system is improved.

[0041] The defect detection unit can refer to past correction history and propose the most effective correction method. For example, when the generation AI detects a defect, the defect detection unit refers to past correction history and proposes the most effective correction method. For example, if a similar defect occurred in the past, the proposal will be based on that correction method. In this way, by referring to past correction history and proposing the most effective correction method, the efficiency and accuracy of corrections are improved.

[0042] The defect detection unit can analyze the code change history and evaluate the impact of the changes on the defect. For example, when the generative AI detects a defect, the defect detection unit analyzes the code change history and evaluates the impact of the changes on the defect. For example, it identifies whether a recent code change is the cause of the defect. This improves the accuracy of corrections by analyzing the code change history and evaluating the impact of the changes on the defect.

[0043] The defect detection unit can propose correction methods in different programming languages ​​and frameworks. For example, when the generative AI detects a defect, the defect detection unit proposes correction methods in different programming languages ​​and frameworks. For example, it proposes a correction method in Python for a JavaScript defect. This improves the flexibility of corrections by proposing correction methods in different programming languages ​​and frameworks.

[0044] The defect detection unit can visualize the extent of impact of the correction and share it with the entire development team. For example, when the generative AI detects a defect, the defect detection unit visualizes the extent of impact of the correction and shares it with the entire development team. For example, it displays the impact of the correction on other functions in a graph. This makes it easier to understand the impact of the correction by visualizing the extent of impact of the correction and sharing it with the entire development team.

[0045] The correction suggestion unit can refer to past correction history and suggest the most effective correction method. For example, when the generation AI detects a defect, the correction suggestion unit refers to past correction history and suggests the most effective correction method. For example, if a similar defect occurred in the past, the suggestion will be based on that correction method. In this way, by referring to past correction history and suggesting the most effective correction method, the efficiency and accuracy of corrections are improved.

[0046] The correction suggestion unit can analyze the code change history and evaluate the impact of the changes on the defect. For example, when the generative AI detects a defect, the correction suggestion unit analyzes the code change history and evaluates the impact of the changes on the defect. For example, it identifies whether a recent code change is the cause of the defect. This improves the accuracy of corrections by analyzing the code change history and evaluating the impact of the changes on the defect.

[0047] The correction suggestion unit can propose correction methods in different programming languages ​​and frameworks. For example, when the generative AI detects a bug, the correction suggestion unit proposes correction methods in different programming languages ​​and frameworks. For example, it proposes a correction method in Python for a JavaScript bug. This improves the flexibility of corrections by proposing correction methods in different programming languages ​​and frameworks.

[0048] The correction suggestion unit can visualize the scope of impact of the correction and share it with the entire development team. For example, when the generative AI detects a bug, the correction suggestion unit visualizes the scope of impact of the correction and shares it with the entire development team. For example, it displays the impact of the correction on other functions in a graph. This makes it easier to understand the impact of the correction by visualizing the scope of impact of the correction and sharing it with the entire development team.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The product specification reader can also be equipped with a function to read the contents of the specification aloud. For example, developers can work while listening to the specification being read aloud. This reduces visual burden and allows developers to efficiently grasp the contents of the specification. The voice reading function can also highlight important parts of the specification when reading aloud. Furthermore, the reading speed and type of voice can be customized.

[0051] The test design department can compare different test design methods and select the most appropriate one. For example, they can compare the effectiveness of black box testing and white box testing and select the method that is best suited to the project. This improves the accuracy and efficiency of test design. The test design department can also be equipped with a function to explain to the development team the reasons for selecting a test design method. Furthermore, it is possible to automatically generate test cases based on the selected method.

[0052] The test item creation unit can analyze user operation logs and create test items based on actual usage. For example, it can create test items focusing on functions that users use frequently. This enables testing that is in line with actual usage conditions, improving the user experience. It can also set the priority of test items based on the operation logs. It can also be equipped with a function to provide feedback on the analysis results of the operation logs to the development team.

[0053] The test execution department can analyze the causes of errors that occur during testing in real time and immediately propose corrections. For example, they can analyze the logs of errors that occur during testing to identify the cause of the error. This allows them to quickly identify the cause of the error and propose corrections, improving testing efficiency. The system can also be equipped with a function to automatically collect detailed logs to identify the cause of the error. It is also possible to notify the development team of the cause of the error and proposed corrections in real time.

[0054] The defect detection unit can compare defect occurrence rates in different test environments and propose the most effective test environment. For example, it can compare defect occurrence rates in the development environment, staging environment, and production environment. This allows the most effective test environment to be identified and the accuracy of testing to be improved. The system can also be equipped with a function to display the defect occurrence rate for each test environment in a graph. It is also possible to provide a function to automatically optimize the test environment settings.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The product specification reader reads the product specifications, such as technical specifications or functional requirements, and analyzes their contents. Step 2: The test design department analyzes the product specifications read by the product specification reading department and performs test design. For example, it creates test cases based on functional and non-functional requirements. Step 3: The test item creation department creates test items based on the tests designed by the test design department. For example, test items for the user authentication function include "Can you log in with the correct username and password?" and "Can you log in with an incorrect password?" Step 4: The test execution unit conducts the test based on the test items created by the test item creation unit. For example, when testing the user authentication function, the test unit automatically accesses the login screen, enters information according to the test items, and checks the results. Step 5: The defect detection unit analyzes the results of the tests performed by the test execution unit and detects defects, for example, by analyzing the test results and identifying errors or bugs. Step 6: The fix suggestion unit proposes fixes based on the defects detected by the defect detection unit. For example, if there is a defect in the user authentication function, it analyzes the related code and identifies the specific fixes.

[0057] (Example 2) The automated testing system according to an embodiment of the present invention uses AI to automate everything from web service test analysis and design to the code to be modified. This system reads product specifications, designs tests, creates test items, automatically tests the target service, and suggests corrections if a defect is found. This allows the automated testing system to streamline the web service testing process and improve quality.

[0058] An automated testing system according to an embodiment includes a product specification reading unit, a test design unit, a test item creation unit, a test implementation unit, a defect detection unit, and a correction proposal unit. The product specification reading unit reads the product specifications. For example, the product specification reading unit reads technical specifications and functional requirements and analyzes their contents. The test design unit analyzes the product specifications read by the product specification reading unit and performs test design. For example, the test design unit creates test cases based on functional requirements and non-functional requirements. The test item creation unit creates test items based on tests designed by the test design unit. For example, the test item creation unit creates test items for the user authentication function, such as "Can you log in with a correct username and password?" and "Can you log in with an incorrect password?" The test implementation unit performs tests based on the test items created by the test item creation unit. For example, when testing the user authentication function, the test implementation unit automatically accesses a login screen, enters information according to the test items, and checks the results. The defect detection unit analyzes the results of the tests performed by the test implementation unit and detects defects. For example, the defect detection unit analyzes test results and identifies errors and bugs. The correction suggestion unit proposes corrections based on defects detected by the defect detection unit. For example, if there is a defect in the user authentication function, the correction suggestion unit analyzes the related code and identifies specific corrections. This allows the automated testing system according to the embodiment to streamline the testing process for web services and improve quality. For example, it becomes possible to respond quickly to changes in product specifications, significantly reducing the effort required for test design and implementation. Furthermore, early detection and correction of defects is possible, which is expected to shorten the development cycle.

[0059] The product specification reading unit can automatically detect ambiguous parts of the specifications using natural language processing technology and generate questions for the developer. For example, when the generation AI reads the product specifications, the product specification reading unit can automatically detect ambiguous parts of the specifications using natural language processing technology and generate questions for the developer. For example, if the specifications state "high speed," a question asking about that specific speed is generated. In this way, by automatically detecting ambiguous parts of the specifications and generating questions for the developer, it is possible to clarify the specifications.

[0060] The product specification reading unit can refer to specifications from similar past projects and propose optimal test designs. For example, the generation AI in the product specification reading unit can refer to specifications from similar past projects and propose optimal test designs. For example, test designs for new projects can be created based on test cases that were successful in past projects. This makes it possible to refer to specifications from similar past projects and propose optimal test designs, thereby improving the efficiency of test design.

[0061] The product specification reading unit can use the emotion estimation function to infer the intentions and emotions of the specification writer and make suggestions to resolve ambiguity in the specifications. For example, the product specification reading unit can use the emotion estimation function to infer the intentions and emotions of the specification writer and make suggestions to resolve ambiguity in the specifications. For example, the product specification reading unit can identify parts of the specifications that are causing stress to the specification writer and request a detailed explanation of those parts. This allows the specification writer's intentions and emotions to be inferred and suggestions to resolve ambiguity in the specifications to clarify the specifications.

[0062] The product specification reading unit can search for related patent documents at the same time as reading the specifications and evaluate the risk of patent infringement. For example, the product specification reading unit can check whether the technology described in the specifications infringes on existing patents at the same time as the generation AI reads the specifications and evaluate the risk of patent infringement. This allows the risk of patent infringement to be reduced by searching for patent documents at the same time as reading the specifications and evaluating the risk of patent infringement.

[0063] The product specification reading unit can visualize the contents of the specification and generate an interactive dashboard to be shared with the entire development team. For example, the product specification reading unit uses a generation AI to visualize the contents of the specification and generate an interactive dashboard to be shared with the entire development team. For example, each section of the specification can be displayed in graphs and charts. This allows the content of the specification to be visualized and shared with the entire development team, promoting information sharing and understanding.

[0064] The product specification reading unit can use the emotion estimation function to monitor the emotions of developers in real time when reviewing specifications and encourage positive feedback. The product specification reading unit, for example, uses the emotion estimation function to monitor the emotions of developers in real time when reviewing specifications and encourage positive feedback. For example, if a developer is feeling stressed, an encouraging message is displayed. In this way, the developer's emotions can be monitored in real time when reviewing specifications and positive feedback can be encouraged, thereby improving the developer's motivation.

[0065] The test item creation unit can refer to past test results and prioritize the selection of the most effective test items. For example, when the generation AI creates test items, the test item creation unit refers to past test results and prioritizes the selection of the most effective test items. For example, it prioritizes the selection of test items that have detected many defects in the past. This makes it possible to improve the efficiency of testing by referring to past test results and prioritize the selection of the most effective test items.

[0066] The test item creation unit can automatically generate different test scenarios and perform risk assessment for each scenario. For example, when the generation AI creates test items, the test item creation unit automatically generates different test scenarios and performs risk assessment for each scenario. For example, as test scenarios for a user authentication function, it generates scenarios with a correct password, an incorrect password, and no password. This allows different test scenarios to be automatically generated and risk assessment to be performed for each scenario, improving test diversity and risk management.

[0067] The test item creation unit can use the emotion estimation function to monitor the developer's stress level when creating test items and make suggestions to reduce stress. The test item creation unit, for example, uses the emotion estimation function to monitor the developer's stress level when creating test items and make suggestions to reduce stress. For example, if the developer is feeling stressed, a message urging the developer to take a break is displayed. In this way, the developer's stress level can be monitored when creating test items and suggestions to reduce stress can be made, thereby reducing the burden on the developer.

[0068] The test case creation department can incorporate best practices from different industries to ensure the diversity of test cases. For example, when the generative AI creates test cases, the test case creation department incorporates best practices from different industries to ensure the diversity of test cases. For example, it creates security test cases that incorporate best practices from the financial industry. This improves the quality of tests by incorporating best practices from different industries and ensuring the diversity of test cases.

[0069] The test item creation unit can reflect the results of user interviews and add test items from the user's perspective. For example, when the generation AI creates test items, the test item creation unit reflects the results of user interviews and adds test items from the user's perspective. For example, it adds test items for interfaces that users find easy to use. In this way, by reflecting the results of user interviews and adding test items from the user's perspective, user satisfaction is improved.

[0070] The test item creation unit can use the emotion estimation function to analyze the emotions of team members when reviewing test items and promote positive feedback. For example, the test item creation unit uses the emotion estimation function to analyze the emotions of team members when reviewing test items and promote positive feedback. For example, if a team member is feeling stressed, an encouraging message is displayed. In this way, the team morale is improved by analyzing the emotions of team members when reviewing test items and promoting positive feedback.

[0071] The test execution unit can simultaneously perform compatibility tests on different devices and browsers. For example, when the generative AI performs a test, the test execution unit simultaneously performs compatibility tests on different devices and browsers. For example, it can simultaneously check operation on smartphones, tablets, and desktop PCs. This improves test efficiency and coverage by simultaneously performing compatibility tests on different devices and browsers.

[0072] The test administration unit can use the emotion estimation function to monitor the user's emotions during the test in real time and reflect them in the test results. For example, the test administration unit uses the emotion estimation function to monitor the user's emotions during the test in real time and reflect them in the test results. For example, if the user is feeling stressed, the cause can be identified and reflected in the test results. In this way, by monitoring the user's emotions during the test in real time and reflecting them in the test results, the user experience can be improved.

[0073] The test implementation unit can simulate network environments in different regions and conduct tests from a global perspective. For example, when the generation AI conducts a test, the test implementation unit simulates network environments in different regions and conducts tests from a global perspective. For example, it simulates network environments in the United States, Europe, and Asia. This allows for simulating network environments in different regions and conducting tests from a global perspective, thereby improving test coverage.

[0074] The test execution unit can simultaneously perform security tests and detect vulnerabilities. For example, when the generative AI performs a test, the test execution unit simultaneously performs security tests and detects vulnerabilities. For example, it detects SQL injection and cross-site scripting vulnerabilities. In this way, by simultaneously performing security tests and detecting vulnerabilities, the safety of the system is improved.

[0075] The test implementation unit can use the emotion estimation function to monitor the emotions of the developer while the test is being conducted and make suggestions to reduce stress. The test implementation unit, for example, uses the emotion estimation function to monitor the emotions of the developer while the test is being conducted and make suggestions to reduce stress. For example, if the developer is feeling stressed, a message urging the developer to take a break is displayed. In this way, by monitoring the emotions of the developer while the test is being conducted and making suggestions to reduce stress, the burden on the developer can be reduced.

[0076] The defect detection unit can refer to past correction history and propose the most effective correction method. For example, when the generation AI detects a defect, the defect detection unit refers to past correction history and proposes the most effective correction method. For example, if a similar defect occurred in the past, the proposal will be based on that correction method. In this way, by referring to past correction history and proposing the most effective correction method, the efficiency and accuracy of corrections are improved.

[0077] The defect detection unit can analyze the code change history and evaluate the impact of the changes on the defect. For example, when the generative AI detects a defect, the defect detection unit analyzes the code change history and evaluates the impact of the changes on the defect. For example, it identifies whether a recent code change is the cause of the defect. This improves the accuracy of corrections by analyzing the code change history and evaluating the impact of the changes on the defect.

[0078] The defect detection unit can use the emotion estimation function to analyze the developer's emotion when proposing a correction and promote positive feedback. The defect detection unit, for example, uses the emotion estimation function to analyze the developer's emotion when proposing a correction and promote positive feedback. For example, if the developer is feeling stressed, an encouraging message is displayed. In this way, analyzing the developer's emotion when proposing a correction and promoting positive feedback improves the developer's motivation.

[0079] The defect detection unit can propose correction methods in different programming languages ​​and frameworks. For example, when the generative AI detects a defect, the defect detection unit proposes correction methods in different programming languages ​​and frameworks. For example, it proposes a correction method in Python for a JavaScript defect. This improves the flexibility of corrections by proposing correction methods in different programming languages ​​and frameworks.

[0080] The defect detection unit can visualize the extent of impact of the correction and share it with the entire development team. For example, when the generative AI detects a defect, the defect detection unit visualizes the extent of impact of the correction and shares it with the entire development team. For example, it displays the impact of the correction on other functions in a graph. This makes it easier to understand the impact of the correction by visualizing the extent of impact of the correction and sharing it with the entire development team.

[0081] The defect detection unit can use the emotion estimation function to monitor the developer's emotions in real time when proposing corrections and encourage positive feedback. The defect detection unit, for example, uses the emotion estimation function to monitor the developer's emotions in real time when proposing corrections and encourage positive feedback. For example, if the developer is feeling stressed, an encouraging message is displayed. In this way, the developer's emotions can be monitored in real time when proposing corrections and positive feedback can be encouraged, thereby improving the developer's motivation.

[0082] The correction suggestion unit can refer to past correction history and suggest the most effective correction method. For example, when the generation AI detects a defect, the correction suggestion unit refers to past correction history and suggests the most effective correction method. For example, if a similar defect occurred in the past, the suggestion will be based on that correction method. In this way, by referring to past correction history and suggesting the most effective correction method, the efficiency and accuracy of corrections are improved.

[0083] The correction suggestion unit can analyze the code change history and evaluate the impact of the changes on the defect. For example, when the generative AI detects a defect, the correction suggestion unit analyzes the code change history and evaluates the impact of the changes on the defect. For example, it identifies whether a recent code change is the cause of the defect. This improves the accuracy of corrections by analyzing the code change history and evaluating the impact of the changes on the defect.

[0084] The revision suggestion unit can use the emotion estimation function to analyze the developer's emotion when proposing a revision and promote positive feedback. The revision suggestion unit, for example, uses the emotion estimation function to analyze the developer's emotion when proposing a revision and promote positive feedback. For example, if the developer is feeling stressed, an encouraging message is displayed. In this way, analyzing the developer's emotion when proposing a revision and promoting positive feedback improves the developer's motivation.

[0085] The correction suggestion unit can propose correction methods in different programming languages ​​and frameworks. For example, when the generative AI detects a bug, the correction suggestion unit proposes correction methods in different programming languages ​​and frameworks. For example, it proposes a correction method in Python for a JavaScript bug. This improves the flexibility of corrections by proposing correction methods in different programming languages ​​and frameworks.

[0086] The correction suggestion unit can visualize the scope of impact of the correction and share it with the entire development team. For example, when the generative AI detects a bug, the correction suggestion unit visualizes the scope of impact of the correction and shares it with the entire development team. For example, it displays the impact of the correction on other functions in a graph. This makes it easier to understand the impact of the correction by visualizing the scope of impact of the correction and sharing it with the entire development team.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The product specification reader can also be equipped with a function to read the contents of the specification aloud. For example, developers can work while listening to the specification being read aloud. This reduces visual burden and allows developers to efficiently grasp the contents of the specification. The voice reading function can also highlight important parts of the specification when reading aloud. Furthermore, the reading speed and type of voice can be customized.

[0089] The test design department can compare different test design methods and select the most appropriate one. For example, they can compare the effectiveness of black box testing and white box testing and select the method that is best suited to the project. This improves the accuracy and efficiency of test design. The test design department can also be equipped with a function to explain to the development team the reasons for selecting a test design method. Furthermore, it is possible to automatically generate test cases based on the selected method.

[0090] The test item creation unit can analyze user operation logs and create test items based on actual usage. For example, it can create test items focusing on functions that users use frequently. This enables testing that is in line with actual usage conditions, improving the user experience. It can also set the priority of test items based on the operation logs. It can also be equipped with a function to provide feedback on the analysis results of the operation logs to the development team.

[0091] The test execution department can analyze the causes of errors that occur during testing in real time and immediately propose corrections. For example, they can analyze the logs of errors that occur during testing to identify the cause of the error. This allows them to quickly identify the cause of the error and propose corrections, improving testing efficiency. The system can also be equipped with a function to automatically collect detailed logs to identify the cause of the error. It is also possible to notify the development team of the cause of the error and proposed corrections in real time.

[0092] The defect detection unit can compare defect occurrence rates in different test environments and propose the most effective test environment. For example, it can compare defect occurrence rates in the development environment, staging environment, and production environment. This allows the most effective test environment to be identified and the accuracy of testing to be improved. The system can also be equipped with a function to display the defect occurrence rate for each test environment in a graph. It is also possible to provide a function to automatically optimize the test environment settings.

[0093] The product specification reader can use the emotion estimation function to infer the intent and emotions of the specification writer and make suggestions to resolve ambiguity in the specifications. For example, it can identify parts of the specification that cause stress to the writer and ask for a detailed explanation of those parts. This allows the intent and emotions of the writer to be inferred and suggestions to resolve ambiguity in the specifications to clarify the specifications. The emotion estimation function can also be used to more accurately grasp the intent of the writer. It is also possible to provide appropriate feedback based on the writer's emotions.

[0094] The test item creation unit can use the emotion estimation function to monitor the developer's stress level when creating test items and make suggestions to reduce stress. For example, if a developer is feeling stressed, a message urging them to take a break can be displayed. This reduces the burden on developers by monitoring their stress level when creating test items and making suggestions to reduce stress. The system can also be equipped with a function to provide relaxing music to developers who feel stressed. It can also suggest activities to reduce stress.

[0095] The test execution unit can use the emotion estimation function to monitor the user's emotions during the test in real time and reflect them in the test results. For example, if the user is feeling stressed, the cause can be identified and reflected in the test results. This allows for real-time monitoring of the user's emotions during the test and reflecting them in the test results, thereby improving the user experience. The system can also be equipped with a function to adjust the progress of the test based on the user's emotions. Furthermore, it is possible to analyze user emotion data and use it to improve the test process.

[0096] The defect detection unit can use its emotion estimation function to analyze the developer's emotions when proposing fixes and encourage positive feedback. For example, if a developer is feeling stressed, an encouraging message can be displayed. This improves the developer's motivation by analyzing the developer's emotions when proposing fixes and encouraging positive feedback. The system can also be equipped with a function to make appropriate fix suggestions based on the developer's emotions. Furthermore, it is possible to analyze the developer's emotion data and propose measures to improve the morale of the entire team.

[0097] The revision suggestion unit uses an emotion estimation function to monitor the developer's emotions in real time when proposing revisions, and can encourage positive feedback. For example, if a developer is feeling stressed, an encouraging message can be displayed. This improves the developer's motivation by monitoring the developer's emotions in real time when proposing revisions and encouraging positive feedback. The system can also be equipped with a function to make appropriate revision suggestions based on the developer's emotions. Furthermore, it is possible to analyze the developer's emotional data and propose measures to improve the morale of the entire team.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The product specification reader reads the product specifications, such as technical specifications or functional requirements, and analyzes their contents. Step 2: The test design department analyzes the product specifications read by the product specification reading department and performs test design. For example, it creates test cases based on functional and non-functional requirements. Step 3: The test item creation department creates test items based on the tests designed by the test design department. For example, test items for the user authentication function include "Can you log in with the correct username and password?" and "Can you log in with an incorrect password?" Step 4: The test execution unit conducts the test based on the test items created by the test item creation unit. For example, when testing the user authentication function, the test unit automatically accesses the login screen, enters information according to the test items, and checks the results. Step 5: The defect detection unit analyzes the results of the tests performed by the test execution unit and detects defects, for example, by analyzing the test results and identifying errors or bugs. Step 6: The fix suggestion unit proposes fixes based on the defects detected by the defect detection unit. For example, if there is a defect in the user authentication function, it analyzes the related code and identifies the specific fixes.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0141] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0159] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a product specification reading unit that reads a product specification; a test design unit that analyzes the product specifications read by the product specification reading unit and performs test design; a test item creation unit that creates test items based on the tests designed by the test design unit; a test execution unit that executes tests based on the test items created by the test item creation unit; a defect detection unit that analyzes the results of the test performed by the test execution unit and detects defects; a correction suggestion unit that suggests corrections based on the defects detected by the defect detection unit; A system characterized by:

2. The product specification reading unit Simultaneously with reading the specifications, search for related patent documents and evaluate the risk of patent infringement.

2. The system of claim 1.

3. The test item creation unit Refer to past test results and prioritize the most effective test items.

2. The system of claim 1.

4. The test implementation unit Monitor the performance of your test environment in real time and take immediate action if an anomaly is detected 2. The system of claim 1.

5. The defect detection unit Analyze developer sentiment when proposing changes and encourage positive feedback 2. The system of claim 1.

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