Information processing apparatus and information processing method

Abstract

To provide an information processing apparatus and an information processing method which improve a quality of a test performed for the purpose of confirming a quality of a system.SOLUTION: The information processing apparatus comprises: an acquisition unit which acquires test information about a result of a test performed for the purpose of confirming a quality of a prescribed system; a calculation unit which, on the basis of the test information, calculates an overall evaluation value evaluating overall a quality of the test performed for the purpose of confirming the quality of the prescribed system, on the basis of a plurality of individual evaluation values respectively corresponding to a plurality of different evaluation methods for evaluating the quality of the test; a generation unit which generates evaluation report information visualizing evaluation of the quality of the test performed for the purpose of confirming the quality of the prescribed system, on the basis of the plurality of individual evaluation values and the overall evaluation value; and a providing unit which provides the evaluation report information to a user.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an information processing device and an information processing method. [Background technology]

[0002] Conventionally, there are known techniques for managing the testing process in system development, such as a technique for recording a video of the test work screen from the start to the end of a test for a specified test item, and recording information about the files used by the software under test and the test item information. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2004-185193 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the above-mentioned conventional technology merely records the test work screen as a video from the start to the end of the test for the specified test item, and records the file information and test item information used by the software being tested, so it is not necessarily possible to improve the quality of the tests conducted to confirm the quality of the system.

[0005] An object of the present application is to provide an information processing apparatus and an information processing method that can improve the quality of tests that are performed to check the quality of a system. [Means for solving the problem]

[0006] The information processing device of the present application includes an acquisition unit that acquires test information regarding the results of a test conducted to confirm the quality of a specified system, a calculation unit that calculates an overall evaluation value that comprehensively evaluates the quality of the test based on the test information and a plurality of individual evaluation values ​​corresponding to each of a plurality of different evaluation methods for evaluating the quality of the test conducted to confirm the quality of the specified system, and a generation unit that generates evaluation report information that visualizes an evaluation of the quality of the test conducted to confirm the quality of the specified system based on the plurality of individual evaluation values ​​and the overall evaluation value. [Effects of the Invention]

[0007] According to one aspect of the embodiment, it is possible to improve the quality of tests performed to confirm the quality of a system. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram for explaining the system development process. [Figure 2] FIG. 2 is a diagram illustrating an example of the configuration of an information processing system according to the embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of the configuration of the information processing device according to the embodiment. [Figure 4] FIG. 4 is a diagram for explaining a method for calculating a comprehensive evaluation value according to the embodiment. [Figure 5] FIG. 5 is a diagram for explaining a method for evaluating test density and bug density according to the embodiment. [Figure 6] FIG. 6 is a diagram for explaining a method for evaluating test density and bug density according to the embodiment. [Figure 7] FIG. 7 is a diagram for explaining a method for evaluating a bug slip-through rate according to the embodiment. [Figure 8] FIG. 8 is a diagram illustrating a bug slip-through rate according to the embodiment. [Figure 9]FIG. 9 is a diagram illustrating an example of evaluation report information according to the embodiment. [Figure 10] FIG. 10 is a flowchart showing a processing procedure performed by the information processing device according to the embodiment. [Figure 11] FIG. 11 is a diagram for explaining the zone analysis according to the first modified example. [Figure 12] FIG. 12 is a diagram showing content in which the integrated evaluation value according to the second modified example is visualized. [Figure 13] FIG. 13 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of the information processing device. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, a detailed description will be given of an information processing device and an information processing method according to the present application (hereinafter referred to as an "embodiment") with reference to the drawings. Note that the information processing device and the information processing method according to the present application are not limited to the embodiment. Furthermore, the same components in the following embodiments are denoted by the same reference numerals, and duplicated descriptions will be omitted.

[0010] (Embodiment) 1. Introduction FIG. 1 is a diagram for explaining the system development process. FIG. 1 shows an example of a system development process generally known as the waterfall type. Waterfall type system development is divided into multiple processes, such as business requirements definition, system requirements definition, basic design, detailed design, system implementation (manufacturing), unit testing, integration testing, comprehensive testing, and user acceptance testing, and each process has a sequence. In this application, the process from business requirements definition to system implementation (manufacturing) is called the manufacturing process, and the process from unit testing to user acceptance testing is called the testing process.

[0011] System development begins with the business requirements definition process. In this process, the system developer listens to the client's business processes that they want to systemize and summarizes the business requirements in a business requirements definition document. Business requirements are summarized from the perspective of what business issues or problems are behind the need for system development, and what kind of business flow they want to achieve as their final goal. Business requirements are defined from the perspective of the client and users.

[0012] After the business requirements definition process, the process moves on to the system requirements definition process. In this process, the system developer compiles the system requirements, such as the functions and performance to be included in the system, in order to realize the contents summarized in the business requirements definition document. System requirements are classified into functional requirements, which define the functions to be implemented, and non-functional requirements, which define the contents that must be met other than functionality, such as performance, stability, and security level.

[0013] After the system requirements definition process, the next step is the basic design process. In the basic design process, the system developer specifies the functions to be implemented in the system to realize the contents summarized in the system requirements definition document and compiles them into a basic design document. Basic design is also called external design, and the system developer determines the appearance (external to the system) of the screens used by the client and users from the perspective of the client and users who will actually use the system.

[0014] After the basic design stage, the system moves on to the detailed design stage. In this stage, the system developer creates a detailed design of how the system's functions will be implemented in order to realize the content outlined in the basic design document, and compiles this into a detailed design document. The detailed design is also called the internal design, and the system developer determines the internal specifications of the system from the developer's perspective.

[0015] After the detailed design process, the next step is system implementation (manufacturing). In the system implementation (manufacturing) process, the system developer programs based on the detailed design document and creates the source code that makes up the system being developed. The system being developed is created by combining multiple source codes. For example, the source code corresponds to a module, which is a program component.

[0016] Furthermore, after the system implementation (manufacturing) process, the next step is the unit testing process. In the unit testing process, the system developer verifies whether the program operates correctly as a single function. In the unit testing process, the modules that make up the program are tested to ensure that they function without any problems. In other words, unit testing is a test conducted to check the quality of each module included in the system being developed. The contents verified in unit testing correspond to the contents of the detailed design document.

[0017] After the unit testing process, the process moves on to the integration testing process. In this process, the system developer combines functions to verify that they work properly. In this process, tests are conducted to check the combination of modules, as well as the integration with external modules. In other words, this is testing to check the quality of the combination of multiple modules included in the system being developed. The contents verified in integration testing correspond to the contents of the basic design document.

[0018] After the integration testing process, the next step is the comprehensive testing process. In the comprehensive testing process, the system developer integrates all functions and verifies whether the system works properly. In other words, comprehensive testing is a test conducted to check the quality of the entire system being developed. The contents verified in comprehensive testing correspond to the contents of the system requirements specification document.

[0019] After the comprehensive testing process, the next step is the user acceptance testing process. In the user acceptance testing process, the system developer verifies whether the system works properly in the actual operating environment of the client or user. In other words, user acceptance testing is a test that checks the quality of the system being developed in the actual operating environment of the client or user. The contents verified in user acceptance testing correspond to the contents of the business requirements specification document.

[0020] This application focuses on the unit testing process, integration testing process, and comprehensive testing process shown in FIG. 1. Hereinafter, the unit testing process, integration testing process, and comprehensive testing process may be collectively referred to as the "testing process." Tests performed in the testing process are performed, for example, to confirm the quality of a system under development. Conventionally, it has not been possible to comprehensively visualize multiple evaluation results corresponding to multiple different evaluation methods for evaluating the quality of tests performed to confirm the quality of a system under development. Conventionally, it has not been possible to visualize comprehensive evaluation results for the quality of tests performed to confirm the quality of a system under development.

[0021] In contrast, an information processing device according to an embodiment acquires test information relating to the results of a test conducted to confirm the quality of a specified system. The information processing device also calculates, based on the test information, a plurality of individual evaluation values ​​corresponding to each of a plurality of different evaluation methods for evaluating the quality of the test conducted to confirm the quality of the specified system. The information processing device also calculates, based on the plurality of individual evaluation values, an overall evaluation value that comprehensively evaluates the quality of the test. The information processing device also generates evaluation report information that visualizes an evaluation of the quality of the test conducted to confirm the quality of the specified system, based on the plurality of individual evaluation values ​​and the overall evaluation value.

[0022] This allows the information processing device to comprehensively visualize, for example, multiple evaluation results corresponding to each of multiple different evaluation methods for evaluating the quality of tests conducted to confirm the quality of a system under development. The information processing device can also visualize comprehensive evaluation results for the quality of tests conducted to confirm the quality of a system under development. Furthermore, the information processing device can, for example, present report information to encourage users, such as those involved in system development, to improve the quality of tests. Therefore, the information processing device can improve the quality of tests conducted to confirm the quality of a system.

[0023] [2. Information Processing System Configuration] Fig. 2 is a diagram showing an example of the configuration of an information processing system 1 according to an embodiment. As shown in Fig. 2, the information processing system 1 according to the embodiment includes a user terminal 10 and an information processing device 100. The user terminal 10 and the information processing device 100 are connected to each other via a predetermined communication network (network N) so as to be able to communicate with each other via wired or wireless communication.

[0024] The user terminal 10 is an information processing device used by a user who is a person involved in system development. For example, the user terminal 10 is an information processing device such as a desktop PC (Personal Computer) or a notebook PC. The user terminal 10 may also be a smart device such as a smartphone or a tablet. The user terminal 10 displays information received from the information processing device 100 or the like using a web browser or an application.

[0025] The information processing device 100 is an information processing device that performs information processing according to the embodiment. The information processing device 100 is realized by a server device, a cloud system, etc. For example, the information processing device 100 may perform the information processing according to the embodiment in accordance with an information processing method realized by an information processing program according to the embodiment.

[0026] 3. Configuration of Information Processing Device 3 is a diagram showing an example of the configuration of the information processing device 100 according to the embodiment. The information processing device 100 according to the embodiment includes a communication unit 110, a storage unit 120, and a control unit .

[0027] (Communication unit 110) The communication unit 110 is connected to the network N by wire or wirelessly, and transmits and receives information to and from the user terminal 10 and the collection server 20. For example, the communication unit 110 is realized by a NIC (Network Interface Card), an antenna, or the like.

[0028] (Storage unit 120) The storage unit 120 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk. Specifically, the storage unit 120 stores an information processing program according to the embodiment. The storage unit 120 also stores various pieces of information acquired by the acquisition unit 131. The storage unit 120 also stores various pieces of information calculated by the calculation unit 132. The storage unit 120 also stores various pieces of information generated by the generation unit 133.

[0029] (control unit 130) The control unit 130 is a controller, and is realized, for example, by a CPU (Central Processing Unit) or an MPU (Micro Processing Unit) executing various programs stored in a storage device inside the information processing device 100 using RAM as a work area. The control unit 130 is also a controller, and is realized, for example, by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0030] The control unit 130 has an acquisition unit 131, a calculation unit 132, a generation unit 133, and a provision unit 134 as functional units, and may realize or execute the information processing actions described below. Note that the internal configuration of the control unit 130 is not limited to the configuration shown in FIG. 3, and may be any other configuration that performs the information processing described below. Furthermore, each functional unit indicates a function of the control unit 130, and does not necessarily have to be physically distinct.

[0031] (Acquisition part 131) The acquiring unit 131 acquires test information related to the results of tests conducted to confirm the quality of a predetermined system. The predetermined system may be, for example, a system under development. For example, the acquiring unit 131 acquires test information related to the results of each of a unit test, an integration test, and a comprehensive test conducted to confirm the quality of the predetermined system. For example, the acquiring unit 131 may acquire the test information from the user terminal 10.

[0032] Furthermore, the acquiring unit 131 acquires, as the test information, the number of tests performed to check the quality of a predetermined system. For example, as the test information, the acquiring unit 131 acquires the number of tests performed in each process of a unit test, an integration test, and a comprehensive test performed to check the quality of a predetermined system.

[0033] Furthermore, the acquiring unit 131 acquires, as the test information, the number of bugs detected in tests conducted to check the quality of a predetermined system. For example, as the test information, the acquiring unit 131 acquires the number of bugs detected in each of the unit test, the integration test, and the comprehensive test conducted to check the quality of a predetermined system.

[0034] In addition, the acquisition unit 131 acquires, as test information, the number of slip-through bugs, which are bugs that should be detected in an earlier test process in multiple test processes performed to check the quality of a specified system and that are detected in a later test process that is performed after the earlier test process, and the number of earlier bugs, which are bugs that should be detected in the earlier test process but are detected in the earlier test process.

[0035] For example, the multiple test processes performed to check the quality of a specified system may be three test processes: a unit test, an integration test, and a comprehensive test. Furthermore, the first test process in the multiple test processes may be, for example, a unit test. For example, if the first test process is a unit test, the second test process performed after the first test process may be an integration test and a comprehensive test. Furthermore, the first test process in the multiple test processes may be, for example, an integration test. For example, if the first test process is an integration test, the second test process performed after the first test process is a comprehensive test.

[0036] Furthermore, bugs detected in each testing phase are associated in advance with information indicating the phase at which the bug should be detected. For example, bugs at a level that should be detected in a unit testing phase are associated with information indicating the unit testing phase (e.g., label information such as "unit-level bug"). Bugs at a level that should be detected in an integration testing phase are associated with information indicating the integration testing phase (e.g., label information such as "integration-level bug"). Bugs at a level that should be detected in a comprehensive testing phase are associated with information indicating the comprehensive testing phase (e.g., label information such as "comprehensive-level bug").

[0037] Furthermore, a slip-through bug refers to, for example, a bug that should be detected in the unit testing process (e.g., a bug associated with the label "unit-level bug") but is detected in the integration testing process or the comprehensive testing process, which are performed after the unit testing process. In the following, a bug that should be detected in the unit testing process (e.g., a bug associated with the label "unit-level bug") but is detected in the integration testing process or the comprehensive testing process, which are performed after the unit testing process, may be referred to as a "unit slip-through bug." In addition, a slip-through bug refers to, for example, a bug that should be detected in the integration testing process (e.g., a bug associated with the label "integration-level bug") but is detected in the comprehensive testing process, which is performed after the integration testing process. In the following, a bug that should be detected in the integration testing process (e.g., a bug associated with the label "integration-level bug") but is detected in the comprehensive testing process, which is performed after the integration testing process, may be referred to as a "integration slip-through bug."

[0038] Furthermore, a preceding bug refers to, for example, a bug that should be detected in the unit testing process (e.g., a bug associated with the label information "unit-level bug") and that is detected in the unit testing process. In the following, a bug that should be detected in the unit testing process (e.g., a bug associated with the label information "unit-level bug") and that is detected in the unit testing process may be referred to as a "pre-unit bug." In addition, a preceding bug refers to, for example, a bug that should be detected in the integration testing process (e.g., a bug associated with the label information "integration-level bug") and that is detected in the integration testing process. In the following, a bug that should be detected in the integration testing process (e.g., a bug associated with the label information "integration-level bug") and that is detected in the integration testing process may be referred to as a "pre-integration bug."

[0039] (Calculation unit 132) The calculation unit 132 calculates, based on the test information, a comprehensive evaluation value that comprehensively evaluates the quality of the tests based on a plurality of individual evaluation values ​​corresponding to each of a plurality of different evaluation methods for evaluating the quality of tests conducted to confirm the quality of a predetermined system. For example, the calculation unit 132 calculates, as the individual evaluation value, a test density, which is a value obtained by dividing the number of tests by the man-hours. Here, the man-hours refer to the estimated man-hours for the system. More specifically, the man-hours may be the estimated man-hours provided by a development vendor for each system. For example, the man-hours may be an estimate of the time required to develop the system. Furthermore, for example, the man-hours may be the number of steps (e.g., the number of lines) of source code that constitutes the system. Furthermore, the calculation unit 132 calculates, as the individual evaluation value, a bug density, which is a value obtained by dividing the number of bugs by the man-hours. Furthermore, the calculation unit 132 calculates, as the individual evaluation value, a bug slip-through rate, which is a value obtained by dividing the number of slip-through bugs by the sum of the number of pre-stage bugs and the number of slip-through bugs. Furthermore, the calculation unit 132 calculates a comprehensive evaluation value based on the test density, the bug density, and the bug slip-through rate.

[0040] 4 is a diagram illustrating a method for calculating an overall evaluation value according to an embodiment. In FIG. 4, the calculation unit 132 calculates a plurality of individual evaluation values ​​for evaluating the quality of each of the unit test, the integration test, and the overall test, and calculates the overall evaluation value based on the plurality of individual evaluation values ​​for each of the unit test, the integration test, and the overall test.

[0041] For example, the calculation unit 132 calculates a unit test density, which is a test density for evaluating the quality of a unit test. The calculation unit 132 calculates the unit test density, which is a value obtained by dividing the number of unit tests by the man-hours. The calculation unit 132 also calculates a unit bug density, which is a bug density for evaluating the quality of a unit test. The calculation unit 132 calculates the unit bug density, which is a value obtained by dividing the number of bugs detected in a unit test by the man-hours. The calculation unit 132 also calculates a unit bug slip-through rate, which is a bug slip-through rate for evaluating the quality of a unit test. The calculation unit 132 calculates the unit bug slip-through rate, which is a value obtained by dividing the number of unit slip-through bugs by the sum of the number of unit pre-stage bugs and the number of unit slip-through bugs. The calculation unit 132 also calculates a comprehensive evaluation value based on the unit test density, the unit bug density, and the unit bug slip-through rate.

[0042] The calculation unit 132 also calculates an integration test density, which is a test density for evaluating the quality of an integration test. The calculation unit 132 calculates the integration test density, which is a value obtained by dividing the number of integration tests by the man-hours. The calculation unit 132 also calculates an integration bug density, which is a bug density for evaluating the quality of an integration test. The calculation unit 132 calculates the integration bug density, which is a value obtained by dividing the number of bugs detected in an integration test by the man-hours. The calculation unit 132 also calculates an integration bug slip-through rate, which is a bug slip-through rate for evaluating the quality of an integration test. The calculation unit 132 calculates the integration bug slip-through rate, which is a value obtained by dividing the number of integration slip-through bugs by the sum of the number of pre-integration bugs and the number of integration slip-through bugs. The calculation unit 132 also calculates an overall evaluation value based on the integration test density, the integration bug density, and the integration bug slip-through rate.

[0043] The calculation unit 132 also calculates an overall test density, which is a test density for evaluating the quality of the overall test. The calculation unit 132 calculates an overall test density, which is a value obtained by dividing the number of overall tests by the man-hours. The calculation unit 132 also calculates an overall bug density, which is a bug density for evaluating the quality of the overall test. The calculation unit 132 calculates an overall bug density, which is a value obtained by dividing the number of bugs detected in the overall test by the man-hours. The calculation unit 132 also calculates an overall evaluation value based on the overall test density and the overall bug density.

[0044] More specifically, the calculation unit 132 calculates a unit test density evaluation score, an integration test density evaluation score, and an overall test density evaluation score corresponding to the unit test density, the integration test density, and the overall test density, respectively. The calculation unit 132 also calculates a unit bug density evaluation score, an integration bug density evaluation score, and an overall bug density evaluation score corresponding to the unit bug density, the integration bug density, and the overall bug density, respectively. The calculation unit 132 also calculates a unit bug slip-through rate evaluation score corresponding to the value of the unit bug slip-through rate and a integration bug slip-through rate evaluation score corresponding to the value of the integration bug slip-through rate. The calculation unit 132 also calculates an overall evaluation value based on the unit test density evaluation score, the integration test density evaluation score, the overall test density evaluation score, the unit bug density evaluation score, the integration bug density evaluation score, the overall bug density evaluation score, the unit bug slip-through rate evaluation score, and the integration bug slip-through rate evaluation score. Furthermore, the calculation unit 132 calculates a total value which is a sum of the unit test density evaluation score, the integration test density evaluation score, the overall test density evaluation score, the individual bug density evaluation score, the integration bug density evaluation score, the overall bug density evaluation score, the individual bug slip-through rate evaluation score, and the integration bug slip-through rate evaluation score. The calculation unit 132 converts the total value into a value between 0 and 100 to obtain an overall evaluation value. Furthermore, if the overall evaluation value is 90 or more, the calculation unit 132 determines that the overall evaluation is to be grade A, which indicates the highest evaluation. Furthermore, if the overall evaluation value is 50 or more but less than 90, the calculation unit 132 determines that the overall evaluation is to be grade B, which indicates the next lowest evaluation after grade A. Furthermore, if the overall evaluation value is 30 or more but less than 50, the calculation unit 132 determines that the overall evaluation is to be grade C, which indicates the next lowest evaluation after grade B. Furthermore, if the overall evaluation value is less than 30, the calculation unit 132 determines that the overall evaluation is to be grade D, which indicates the lowest evaluation.

[0045] FIG. 5 is a diagram illustrating a method for evaluating test density and bug density according to an embodiment. As shown in the upper part of FIG. 5, the calculation unit 132 calculates a test density evaluation score corresponding to the test density value calculated in FIG. 4 based on a comparison between the test density reference value and the test density value calculated in FIG. 4. For example, if the test density value calculated in FIG. 4 is equal to or greater than 75% of the test density reference value and equal to or less than 125% of the test density reference value, the calculation unit 132 determines the evaluation of the test density calculated in FIG. 4 as Grade A, which represents the highest evaluation. Furthermore, if the calculation unit 132 determines the evaluation of the test density calculated in FIG. 4 as Grade A, the calculation unit 132 calculates the test density evaluation score corresponding to the test density value calculated in FIG. 4 as 100 points. Furthermore, if the test density value calculated in FIG. 4 is greater than 125% of the test density reference value, the calculation unit 132 determines the evaluation of the test density calculated in FIG. 4 as Grade B, which represents an intermediate evaluation. Furthermore, when the calculation unit 132 determines that the evaluation of the test density calculated in Fig. 4 is evaluation B, it calculates the test density evaluation score corresponding to the value of the test density calculated in Fig. 4 as 70 points. When the value of the test density calculated in Fig. 4 is less than 75% of the reference value of the test density, the calculation unit 132 determines that the evaluation of the test density calculated in Fig. 4 is evaluation C, which indicates the lowest evaluation. When the calculation unit 132 determines that the evaluation of the test density calculated in Fig. 4 is evaluation C, it calculates the test density evaluation score corresponding to the value of the test density calculated in Fig. 4 as 10 points.

[0046] 5, the calculation unit 132 calculates a bug density evaluation score corresponding to the bug density value calculated in FIG. 4 based on a comparison between the bug density reference value and the bug density value calculated in FIG. 4. For example, if the bug density value calculated in FIG. 4 is equal to or greater than 75% of the bug density reference value and equal to or less than 125% of the bug density reference value, the calculation unit 132 determines the evaluation of the bug density calculated in FIG. 4 as an A rating, which represents the highest evaluation. If the calculation unit 132 determines the evaluation of the bug density calculated in FIG. 4 as an A rating, the calculation unit 132 calculates the bug density evaluation score corresponding to the bug density value calculated in FIG. 4 as 100 points. If the bug density value calculated in FIG. 4 is less than 75% of the bug density reference value, the calculation unit 132 determines the evaluation of the bug density calculated in FIG. 4 as a B rating, which represents an intermediate evaluation. Furthermore, when the calculation unit 132 determines that the bug density evaluation calculated in Fig. 4 is evaluation B, it calculates the bug density evaluation score corresponding to the bug density value calculated in Fig. 4 as 70 points. When the bug density value calculated in Fig. 4 is a value that exceeds 125% of the bug density reference value, the calculation unit 132 determines that the evaluation of the bug density calculated in Fig. 4 is evaluation C, which indicates the lowest evaluation. When the calculation unit 132 determines that the bug density evaluation calculated in Fig. 4 is evaluation C, it calculates the bug density evaluation score corresponding to the bug density value calculated in Fig. 4 as 10 points.

[0047] Note that, although FIG. 5 illustrates a case in which the lower limit of an A rating is a value equal to or greater than 75% of the standard value of test density (or bug density), the lower limit of an A rating is not limited to a value equal to or greater than 75% of the standard value of test density (or bug density). For example, the lower limit of an A rating may be any percentage less than 100% of the standard value, such as a value equal to or greater than 70% of the standard value of test density (or bug density) or a value equal to or greater than 80% of the standard value. Similarly, FIG. 5 illustrates a case in which the upper limit of an A rating is a value equal to or less than 125% of the standard value of test density (or bug density). However, the upper limit of an A rating is not limited to a value equal to or less than 125% of the standard value of test density (or bug density). For example, the upper limit of an A rating may be any percentage equal to or greater than 100% of the standard value, such as a value equal to or greater than 120% of the standard value of test density (or bug density) or a value equal to or greater than 130% of the standard value.

[0048] Fig. 6 is a diagram for explaining a method for evaluating test density and bug density according to an embodiment. Fig. 6 explains a case where the reference value of test density (or bug density) explained in Fig. 5 is the average value of test density (or bug density) in a target period (for example, one year in 2023). Fig. 6 also explains a case where the test density (or bug density) calculated in Fig. 4 is the test density (or bug density) in an evaluation period included in the target period (for example, one month in April included in one year in 2023).

[0049] 6, the calculation unit 132 calculates the test density and bug density for each project during the evaluation period. Here, a project may be an addition or modification of a function in a system, a specification change, etc. Furthermore, if there are multiple different systems, the calculation unit 132 calculates the test density and bug density for each system.

[0050] In the diagram on the left side of FIG. 6, cases A to C occurred for system A during the one month of April 2023. The calculation unit 132 calculates the test density for each of cases A to C. The calculation unit 132 also calculates the bug density for each of cases A to C. Furthermore, cases D and E occurred for system A during the one month of May 2024. The calculation unit 132 calculates the test density for each of cases D and E. The calculation unit 132 also calculates the bug density for each of cases D and E. Furthermore, case F occurred for system A during the one month of June 2024. The calculation unit 132 calculates the test density for case F. The calculation unit 132 also calculates the bug density for case F. Furthermore, cases G and H occurred for system B during the one month of April 2023. The calculation unit 132 calculates the test density for each of cases G and H. The calculation unit 132 also calculates the bug density for each of cases G and H. Furthermore, during the one month of May 2023, an incident I occurs for system B. The calculation unit 132 calculates the test density for incident I. The calculation unit 132 also calculates the bug density for incident I.

[0051] 6, the calculation unit 132 calculates the average values ​​of test density and bug density for each system during the target period. Furthermore, the calculation unit 132 calculates the lower and upper limits of the A-grade rating for the test density and bug density for each system based on the average values ​​of test density and bug density for each system.

[0052] For example, the calculation unit 132 calculates the average values ​​of the test density and the bug density for the target period of system A. For example, the calculation unit 132 calculates the average value of the test density for system A for the period from April to June 2023 (an example of the target period for system A) based on the test densities for cases A to F described in the diagram on the left side of FIG. 6. Furthermore, the calculation unit 132 calculates the average value of the bug density for system A for the period from April to June 2023 based on the bug densities for cases A to F described in the diagram on the left side of FIG. 6. Furthermore, the calculation unit 132 calculates the lower limit and upper limit of the A rating for the test density for system A for the target period based on the average values ​​of the test density and the bug densities for system A for the target period.

[0053] Similarly, the calculation unit 132 calculates the average values ​​of the test density and the bug density for the target period of system B. For example, the calculation unit 132 calculates the average value of the test density for system B for the period from April to May 2023 (an example of the target period for system B) based on the test densities for cases G to H described in the diagram on the left side of FIG. 6. The calculation unit 132 also calculates the average value of the bug density for system B for the period from April to May 2023 based on the bug densities for cases G to H described in the diagram on the left side of FIG. 6. The calculation unit 132 also calculates the lower limit and upper limit of the A rating for the test density for system B for the target period based on the average values ​​of the test density and the bug densities for system B for the target period.

[0054] In the diagram on the right side of FIG. 6, the calculation unit 132 calculates a test density evaluation score and a bug density evaluation score corresponding to the test density and bug density for the evaluation period based on a comparison between the average values ​​of the test density and bug density for the target period and the average values ​​of the test density and bug density for the evaluation period included in the target period. In the diagram on the right side of FIG. 6, the calculation unit 132 calculates the average value of the test density (or bug density) for one month in April 2023 (an example of an evaluation period for System A) based on the test density (or bug density) for each of Cases A to C. Furthermore, the calculation unit 132 calculates a test density evaluation score (or bug density evaluation score) corresponding to the average value of the test density (or bug density) for System A for one month in April 2023 based on a comparison between the average value of the test density (or bug density) for System A for the period from April to June 2023 and the average value of the test density (or bug density) for System A for one month in April 2023. More specifically, the calculation unit 132 calculates a test density evaluation score (or bug density evaluation score) corresponding to the average value of test density (or bug density) for system A for the one month of April 2023, based on a comparison between the lower limit and upper limit values ​​of the A rating of test density (or bug density) for system A for the period from April to June 2023 and the average value of test density (or bug density) for system A for the one month of April 2023. Furthermore, the calculation unit 132 calculates an overall evaluation score for system A for the one month of April 2023, based on the test density evaluation score and bug density evaluation score of system A for the one month of April 2023.

[0055] Similarly, the calculation unit 132 calculates the average value of the test density (or bug density) for one month in May 2023 (an example of an evaluation period for system A) based on the test density (or bug density) for each of cases D and E. The calculation unit 132 also calculates a test density evaluation score (or bug density evaluation score) corresponding to the average value of the test density (or bug density) for system A for one month in May 2023, based on a comparison between the average value of the test density (or bug density) for system A for the period from April to June 2023 and the average value of the test density (or bug density) for system A for one month in May 2023. More specifically, the calculation unit 132 calculates a test density evaluation score (or bug density evaluation score) corresponding to the average value of test density (or bug density) for system A for the one month of May 2023, based on a comparison between the lower limit and upper limit values ​​of the A rating of test density (or bug density) for system A for the period from April to June 2023 and the average value of test density (or bug density) for system A for the one month of May 2023. Furthermore, the calculation unit 132 calculates an overall evaluation score for system A for the one month of May 2023, based on the test density evaluation score and bug density evaluation score of system A for the one month of May 2023.

[0056] Similarly, the calculation unit 132 calculates the average value of the test density (or bug density) for one month in June 2023 (an example of an evaluation period for system A) based on the test density (or bug density) for case F. The calculation unit 132 calculates a test density evaluation score (or bug density evaluation score) corresponding to the average value of the test density (or bug density) for system A for one month in June 2023 based on a comparison between the average value of the test density (or bug density) for system A for the period from April to June 2023 and the average value of the test density (or bug density) for system A for one month in June 2023. More specifically, the calculation unit 132 calculates a test density evaluation score (or bug density evaluation score) corresponding to the average value of test density (or bug density) for system A for the one month of June 2023, based on a comparison between the lower limit and upper limit values ​​of the A rating of test density (or bug density) for system A for the period from April to June 2023 and the average value of test density (or bug density) for system A for the one month of June 2023. Furthermore, the calculation unit 132 calculates an overall evaluation score for system A for the one month of June 2023, based on the test density evaluation score and bug density evaluation score of system A for the one month of June 2023.

[0057] Similarly, the calculation unit 132 calculates the average value of the test density (or bug density) for one month in April 2023 (an example of an evaluation period for system B) based on the test density (or bug density) for each of cases G and H. The calculation unit 132 also calculates a test density evaluation score (or bug density evaluation score) corresponding to the average value of the test density (or bug density) for system B for one month in April 2023, based on a comparison between the average value of the test density (or bug density) for system B for the period from April to May 2023 and the average value of the test density (or bug density) for system B for one month in April 2023. More specifically, the calculation unit 132 calculates a test density evaluation score (or bug density evaluation score) corresponding to the average value of test density (or bug density) for system B for the one month of April 2023, based on a comparison between the lower limit and upper limit values ​​of the A rating of test density (or bug density) for system B for the period from April to May 2023 and the average value of test density (or bug density) for system B for the one month of April 2023. Furthermore, the calculation unit 132 calculates an overall evaluation score for system B for the one month of April 2023, based on the test density evaluation score and bug density evaluation score of system B for the one month of April 2023.

[0058] Similarly, the calculation unit 132 calculates the average value of the test density (or bug density) for one month in May 2023 (an example of an evaluation period for System B) based on the test density (or bug density) for Case I. The calculation unit 132 also calculates a test density evaluation score (or bug density evaluation score) corresponding to the average value of the test density (or bug density) for System B for one month in May 2023, based on a comparison between the average value of the test density (or bug density) for System B for the period from April to May 2023 and the average value of the test density (or bug density) for System B for one month in May 2023. More specifically, the calculation unit 132 calculates a test density evaluation score (or bug density evaluation score) corresponding to the average value of test density (or bug density) for system B for the one month of May 2023, based on a comparison between the lower limit and upper limit values ​​of the A rating of test density (or bug density) for system B for the period from April to May 2023 and the average value of test density (or bug density) for system B for the one month of May 2023. Furthermore, the calculation unit 132 calculates an overall evaluation score for system B for the one month of May 2023, based on the test density evaluation score and bug density evaluation score of system B for the one month of May 2023.

[0059] 6, the calculation unit 132 calculates a plurality of individual evaluation points corresponding to each of the plurality of individual evaluation values ​​in the evaluation period based on a comparison between the average value of each of the plurality of individual evaluation values ​​in the target period and the average value of each of the plurality of individual evaluation values ​​in the evaluation periods included in the target period, and calculates an overall evaluation value for the evaluation period based on the plurality of individual evaluation points in the evaluation period. In addition, the calculation unit 132 calculates a plurality of individual evaluation points for each of the plurality of different evaluation periods based on a comparison between the average value of each of the plurality of individual evaluation values ​​in the target period and the average value of each of the plurality of individual evaluation values ​​in each of the plurality of different evaluation periods, and calculates an overall evaluation value for each of the plurality of different evaluation periods based on the plurality of individual evaluation points in each of the plurality of different evaluation periods.

[0060] Fig. 7 is a diagram for explaining a method for evaluating a bug slip-through rate according to an embodiment. In Fig. 7, when the value of the bug slip-through rate calculated in Fig. 4 is a value between 0% and 10% or less, the calculation unit 132 determines the evaluation of the bug slip-through rate calculated in Fig. 4 as an A rating, which indicates the highest evaluation. Furthermore, when the calculation unit 132 determines the evaluation of the bug slip-through rate calculated in Fig. 4 as an A rating, the calculation unit 132 calculates the bug slip-through rate evaluation score corresponding to the value of the bug slip-through rate calculated in Fig. 4 as 100 points.

[0061] 4 is a value greater than 10% to 20% or less, the calculation unit 132 determines the evaluation of the bug slip-through rate calculated in Fig. 4 as Grade B, which is the next lowest evaluation after Grade A. Furthermore, when the calculation unit 132 determines the evaluation of the bug slip-through rate calculated in Fig. 4 as Grade B, the calculation unit 132 calculates the bug slip-through rate evaluation score corresponding to the value of the bug slip-through rate calculated in Fig. 4 as 70 points.

[0062] 4 is a value greater than 20% to 50% or less, the calculation unit 132 determines the evaluation of the bug slip-through rate calculated in Fig. 4 as C, which is the next lowest evaluation after B. Furthermore, when the calculation unit 132 determines the evaluation of the bug slip-through rate calculated in Fig. 4 as C, the calculation unit 132 calculates the bug slip-through rate evaluation score corresponding to the value of the bug slip-through rate calculated in Fig. 4 as 30 points.

[0063] 4 is a value greater than 50% to 100% or less, the calculation unit 132 determines the evaluation of the bug slip-through rate calculated in Fig. 4 as D, which indicates the lowest evaluation. Furthermore, when the calculation unit 132 determines the evaluation of the bug slip-through rate calculated in Fig. 4 as D, the calculation unit 132 calculates the bug slip-through rate evaluation score corresponding to the value of the bug slip-through rate calculated in Fig. 4 as 10 points.

[0064] 7, the ranges corresponding to each of the ratings A to D are described as 0 to 10%, 10 to 20%, 20 to 50%, and 50 to 100%, but the ranges corresponding to each of the ratings A to D are not limited to 0 to 10%, 10 to 20%, 20 to 50%, and 50 to 100%. Specifically, the ranges corresponding to each of the ratings A to D may be any range designated in advance by the system developer or the like.

[0065] 8 is a diagram illustrating the bug slip-through rate according to the embodiment. In FIG. 8, the number of single-unit preceding bugs is 35. Also, the number of single-unit slip-through bugs is 15. In FIG. 8, the calculation unit 132 calculates the single-unit bug slip-through rate, which is the value obtained by dividing 15, which is the number of single-unit preceding bugs, by 50, which is the sum of 35, which is the number of single-unit slip-through bugs, and 15, which is the number of single-unit slip-through bugs, as 0.3 (equivalent to 30%).

[0066] (Generation unit 133) The generation unit 133 generates evaluation report information that visualizes an evaluation of the quality of a test conducted to confirm the quality of a predetermined system, based on a plurality of individual evaluation values ​​and a comprehensive evaluation value. FIG. 9 is a diagram illustrating an example of evaluation report information according to an embodiment. The generation unit 133 generates, for example, evaluation report information as shown in FIG. 9. For example, the generation unit 133 generates report information that visualizes each of the plurality of individual evaluation values ​​calculated by the calculation unit 132. In FIG. 9, the generation unit 133 generates evaluation report information that visualizes each of the values ​​calculated by the calculation unit 132: unit test density, integration test density, overall test density, individual bug density, integration bug density, overall bug density, individual bug slip-through rate, and integration bug slip-through rate.

[0067] 9, the generation unit 133 generates report information that visualizes an evaluation corresponding to each of the plurality of individual evaluation points calculated by the calculation unit 132. In FIG. 9, the generation unit 133 generates report information that visualizes an evaluation (e.g., evaluation A to evaluation D) corresponding to each of the unit test density evaluation point, the integration test density evaluation point, the overall test density evaluation point, the individual bug density evaluation point, the integration bug density evaluation point, the overall bug density evaluation point, the individual bug slip-through rate evaluation point, and the integration bug slip-through rate evaluation point calculated by the calculation unit 132. The generation unit 133 also generates report information that visualizes an overall evaluation (e.g., evaluation A to evaluation D) determined by the calculation unit 132.

[0068] The generation unit 133 also generates report information that visualizes each of the multiple individual evaluation values ​​for each of multiple different evaluation periods. For example, the generation unit 133 generates evaluation report information that visualizes each of the values ​​of unit test density, integration test density, overall test density, individual bug density, integration bug density, overall bug density, individual bug slip-through rate, and integration bug slip-through rate for each of multiple different evaluation periods. In FIG. 9 , the generation unit 133 generates report information that visualizes each of the multiple individual evaluation values ​​for June 2023, which is the current evaluation period. The generation unit 133 also generates report information that visualizes each of the multiple individual evaluation values ​​for March 2023, which is the previous evaluation period.

[0069] The generation unit 133 may also generate report information that visualizes evaluations corresponding to each of a plurality of individual evaluation points for each of a plurality of different evaluation periods. For example, the generation unit 133 generates report information that visualizes evaluations (e.g., evaluations A to D) corresponding to each of the unit test density evaluation point, integration test density evaluation point, overall test density evaluation point, individual bug density evaluation point, integration bug density evaluation point, overall bug density evaluation point, individual bug slippage rate evaluation point, and integration bug slippage rate evaluation point for each of a plurality of different evaluation periods. The generation unit 133 may also generate report information that visualizes the overall evaluation (e.g., evaluations A to D) for each of a plurality of different evaluation periods. In FIG. 9 , the generation unit 133 generates report information that visualizes evaluations corresponding to each of a plurality of individual evaluation points for the current evaluation period, which is June 2023. The generation unit 133 also generates report information that visualizes evaluations corresponding to each of a plurality of individual evaluation points for the previous evaluation period, which is March 2023.

[0070] (Provider 134) The providing unit 134 provides a user with evaluation report information that visualizes an evaluation of the quality of a test conducted to confirm the quality of a predetermined system. The user may be a person involved in system development. For example, the providing unit 134 transmits the evaluation report information generated by the generating unit 133 to the user terminal 10.

[0071] [4. Processing Procedure] FIG. 10 is a flowchart showing a processing procedure performed by the information processing device 100 according to the embodiment. In FIG. 10, the acquisition unit 131 acquires test information related to the results of a test conducted to confirm the quality of a predetermined system (step S101). Based on the test information, the calculation unit 132 calculates a plurality of individual evaluation values ​​corresponding to each of a plurality of different evaluation methods for evaluating the quality of the test conducted to confirm the quality of the predetermined system (step S102). Based on the plurality of individual evaluation values, the calculation unit 132 calculates an overall evaluation value for comprehensively evaluating the quality of the test (step S103). Based on the plurality of individual evaluation values ​​and the overall evaluation value, the generation unit 133 generates evaluation report information that visualizes the evaluation of the quality of the test conducted to confirm the quality of the predetermined system (step S104). Based on the plurality of individual evaluation values ​​and the overall evaluation value, the generation unit 133 provides the evaluation report information that visualizes the evaluation of the quality of the test conducted to confirm the quality of the predetermined system to a user (step S105).

[0072] [5. Modifications] The processing according to the above-described embodiment may be implemented in various different forms other than the above embodiment.

[0073] FIG. 11 is a diagram illustrating zone analysis according to a first modified example. The generation unit 133 generates zone analysis information in which a combined evaluation, which combines multiple evaluations corresponding to multiple individual evaluation points, is associated one-to-one with each region of the matrix. This allows the information processing device 100 to enable a user to visually grasp the combined evaluation, which combines evaluations corresponding to multiple individual evaluation points. In FIG. 11, the generation unit 133 generates zone analysis information in which a combined evaluation, which combines a test density evaluation, which is an evaluation corresponding to a test density evaluation point, with a bug density evaluation, which is an evaluation corresponding to a bug density evaluation point, is associated one-to-one with each region of the nine-quadrant matrix. The provision unit 134 also provides the zone analysis information generated by the generation unit 133 to the user.

[0074] The center column of the nine-quadrant matrix shown in Figure 11 is the column with a test density rating of A. The right column of the nine-quadrant matrix is ​​the column with a test density rating of B. The left column of the nine-quadrant matrix is ​​the column with a test density rating of C. The center row of the nine-quadrant matrix is ​​the row with a bug density rating of A. The bottom row of the nine-quadrant matrix is ​​the row with a bug density rating of B. The top row of the nine-quadrant matrix is ​​the row with a bug density rating of C. For example, when the test density rating is A and the bug density rating is A, the combination rating is the highest, which corresponds to the center area of ​​the nine regions. When the test density rating is C and the bug density rating is C, the combination rating is the lowest, which corresponds to the upper left area of ​​the nine regions. When the test density rating is B and the bug density rating is B, the combination rating is average, which corresponds to the lower right area of ​​the nine regions.

[0075] FIG. 12 is a diagram showing content visualizing an integrated evaluation value according to a second modified example. The generation unit 133 generates content visualizing multiple integrated evaluation values ​​obtained by integrating multiple individual evaluation values ​​for multiple different systems. In FIG. 12, the generation unit 133 generates content displaying, in a bar graph, a value obtained by adding up the test density, bug density, and number of bugs for multiple different systems. The generation unit 133 also generates content displaying, in a bar graph, a value obtained by adding up the test density, bug density, and number of bugs for each of the unit tests, integration tests, and integration tests for multiple different systems. The generation unit 133 may also generate content displaying, in a bar graph, a value obtained by adding up the test density, bug density, and number of bugs for multiple different cases. The provision unit 134 provides the content generated by the generation unit 133 to a user.

[0076] [6. Effects] As described above, the information processing device 100 according to the embodiment includes an acquisition unit 131, a calculation unit 132, and a generation unit 133. The acquisition unit 131 acquires test information related to the results of tests conducted to confirm the quality of a predetermined system. The calculation unit 132 calculates an overall evaluation value that comprehensively evaluates the quality of the tests based on the test information and a plurality of individual evaluation values ​​corresponding to each of a plurality of different evaluation methods for evaluating the quality of the tests conducted to confirm the quality of the predetermined system. The generation unit 133 generates evaluation report information that visualizes the evaluation of the quality of the tests conducted to confirm the quality of the predetermined system based on the plurality of individual evaluation values ​​and the overall evaluation value.

[0077] As a result, the information processing device 100 can comprehensively visualize multiple evaluation results corresponding to each of multiple different evaluation methods for evaluating the quality of tests conducted to confirm the quality of a specified system. Furthermore, the information processing device 100 can visualize comprehensive evaluation results for the quality of tests conducted to confirm the quality of a specified system. Furthermore, the information processing device 100 can prompt users, such as those involved in system development, to improve the quality of the tests by, for example, presenting report information. Therefore, the information processing device 100 can improve the quality of tests conducted to confirm the quality of a system. Furthermore, since the information processing device 100 can improve the quality of tests conducted to confirm the quality of a system, it can contribute to the achievement of Goal 9 of the Sustainable Development Goals (SDGs), "Build resilient infrastructure, promote inclusive and sustainable industrialization, and build resilient technological infrastructure."

[0078] In addition, the calculation unit 132 calculates multiple individual evaluation points corresponding to each of the multiple individual evaluation values ​​in the evaluation period based on a comparison between the average value of each of the multiple individual evaluation values ​​in the target period and the average value of each of the multiple individual evaluation values ​​in the evaluation period included in the target period, and calculates an overall evaluation value for the evaluation period based on the multiple individual evaluation points in the evaluation period.

[0079] This allows the information processing device 100 to calculate a comprehensive evaluation value according to the system based on the individual evaluation points according to the system, thereby making it possible to evaluate the quality of the test according to the system.

[0080] In addition, the calculation unit 132 calculates multiple individual evaluation points for each of the multiple different evaluation periods based on a comparison between the average value of each of the multiple individual evaluation values ​​for the target period and the average value of each of the multiple individual evaluation values ​​for each of the multiple different evaluation periods, and calculates an overall evaluation value for each of the multiple different evaluation periods based on the multiple individual evaluation points for each of the multiple different evaluation periods.

[0081] This allows the information processing device 100 to calculate the overall evaluation value for each of a plurality of different periods, thereby making it possible to grasp the progress of improvement or deterioration in the quality of the test over time.

[0082] The acquiring unit 131 acquires test information relating to the results of each of the unit tests, integration tests, and comprehensive tests conducted to confirm the quality of a predetermined system. The calculating unit 132 calculates a plurality of individual evaluation values ​​for evaluating the quality of each of the unit tests, integration tests, and comprehensive tests, and calculates a comprehensive evaluation value based on the plurality of individual evaluation values ​​for each of the unit tests, integration tests, and comprehensive tests.

[0083] This allows the information processing device 100 to evaluate the quality of the entire test based on the evaluation of each of the unit test, the integration test, and the overall test.

[0084] The acquiring unit 131 also acquires, as the test information, the number of tests performed to check the quality of a given system. The calculating unit 132 calculates, as the individual evaluation value, the test density, which is a value obtained by dividing the number of tests by the man-hours.

[0085] This enables the information processing device 100 to evaluate the quality of the test based on the test density.

[0086] The acquiring unit 131 also acquires, as test information, the number of bugs detected in a test conducted to confirm the quality of a predetermined system. The calculating unit 132 calculates, as an individual evaluation value, a bug density, which is a value obtained by dividing the number of bugs by the number of man-hours.

[0087] This enables the information processing device 100 to evaluate the quality of the test based on the bug density.

[0088] Furthermore, the acquiring unit 131 acquires, as the test information, the number of slip-through bugs, which are bugs that should be detected in a previous test step in a plurality of test steps that are performed to check the quality of a predetermined system and that are detected in a later test step that is performed after the previous test step, and the number of previous-stage bugs, which are bugs that should be detected in the previous-stage test step and that are detected in the previous test step. The calculating unit 132 calculates, as the individual evaluation value, a bug slip-through rate, which is a value obtained by dividing the number of slip-through bugs by the sum of the number of previous-stage bugs and the number of slip-through bugs.

[0089] This enables the information processing device 100 to evaluate the quality of the test based on the bug slip-through rate.

[0090] The information processing device 100 further includes a generating unit 133. The generating unit 133 generates content that visualizes a plurality of integrated evaluation values ​​obtained by integrating a plurality of individual evaluation values ​​in a plurality of different systems.

[0091] This allows the information processing device 100 to grasp the quality of tests across a plurality of different systems.

[0092] [7. Hardware Configuration] The information processing device 100 according to the embodiment described above is realized by a computer 1000 having a configuration as shown in Fig. 13, for example. Fig. 13 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device 100. The computer 1000 includes a CPU 1100, a RAM 1200, a ROM 1300, an HDD 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F) 1700.

[0093] The CPU 1100 operates and controls each unit based on programs stored in the ROM 1300 or the HDD 1400. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 starts up, programs that depend on the hardware of the computer 1000, and the like.

[0094] The HDD 1400 stores programs executed by the CPU 1100, data used by these programs, etc. The communication interface 1500 receives data from other devices via a predetermined communication network and sends the data to the CPU 1100, and transmits data generated by the CPU 1100 to other devices via the predetermined communication network.

[0095] The CPU 1100 controls output devices such as a display and a printer, and input devices such as a keyboard and a mouse, via the input / output interface 1600. The CPU 1100 acquires data from the input devices via the input / output interface 1600. The CPU 1100 also outputs generated data to the output devices via the input / output interface 1600.

[0096] Media interface 1700 reads a program or data stored in recording medium 1800 and provides it to CPU 1100 via RAM 1200. CPU 1100 loads the program or data from recording medium 1800 onto RAM 1200 via media interface 1700 and executes the loaded program. Recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase Change Rewritable Disc), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0097] For example, when the computer 1000 functions as the information processing device 100 according to the embodiment, the CPU 1100 of the computer 1000 executes programs loaded onto the RAM 1200 to realize the functions of the control unit 130. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, the CPU 1100 may obtain these programs from another device via a predetermined communication network.

[0098] Although some of the embodiments of the present application have been described in detail above with reference to the drawings, these are merely examples, and the present invention can be implemented in other forms that include the aspects described in the Disclosure of the Invention section, as well as in various modifications and improvements based on the knowledge of those skilled in the art.

[0099] [8. Other] Furthermore, among the processes described in the above embodiments and modifications, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods. In addition, the information including the processing procedures, specific names, various data, and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown in the drawings.

[0100] Furthermore, the components of each device shown in the figure are conceptual functional components and do not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

[0101] Furthermore, the above-described embodiments and modifications can be combined as appropriate within the scope of not causing any contradiction in the processing content. [Explanation of symbols]

[0102] 100 Information processing device 110 Communications Department 120 Storage section 130 Control Unit 131 Acquisition Department 132 Calculation Unit 133 Generation part 134 Provision Department

Claims

1. an acquisition unit that acquires test information regarding the results of tests conducted to verify the quality of a predetermined system; a calculation unit that calculates a comprehensive evaluation value that comprehensively evaluates the quality of the test based on a plurality of individual evaluation values ​​corresponding to each of a plurality of different evaluation methods that evaluate the quality of the test conducted to confirm the quality of the predetermined system, based on the test information; a generation unit that generates evaluation report information that visualizes an evaluation of the quality of a test that was conducted to confirm the quality of the predetermined system based on the plurality of individual evaluation values ​​and the comprehensive evaluation value; An information processing device comprising:

2. The calculation unit calculating a plurality of individual evaluation points corresponding to each of the plurality of individual evaluation values ​​in the evaluation period based on a comparison between the average value of each of the plurality of individual evaluation values ​​in the target period and the average value of each of the plurality of individual evaluation values ​​in an evaluation period included in the target period, and calculating the overall evaluation value for the evaluation period based on the plurality of individual evaluation points in the evaluation period; The information processing device according to claim 1 .

3. The calculation unit calculating the plurality of individual evaluation points for each of the plurality of different evaluation periods based on a comparison between an average value of each of the plurality of individual evaluation values ​​for the target period and an average value of each of the plurality of individual evaluation values ​​for each of the plurality of different evaluation periods, and calculating the comprehensive evaluation value for each of the plurality of different evaluation periods based on the plurality of individual evaluation points for each of the plurality of different evaluation periods; The information processing device according to claim 2 .

4. The acquisition unit acquiring the test information relating to the results of each of a unit test, an integration test, and a comprehensive test conducted to confirm the quality of the predetermined system; The calculation unit calculating the plurality of individual evaluation values ​​for evaluating the quality of each of the unit test, the integration test, and the comprehensive test, and calculating the comprehensive evaluation value based on the plurality of individual evaluation values ​​for each of the unit test, the integration test, and the comprehensive test; The information processing device according to claim 1 .

5. The acquisition unit As the test information, the number of tests conducted to confirm the quality of the predetermined system is acquired; The calculation unit Calculating a test density, which is a value obtained by dividing the number of tests by the man-hours, as the individual evaluation value. The information processing device according to claim 1 .

6. The acquisition unit As the test information, the number of bugs detected in a test conducted to confirm the quality of the predetermined system is acquired; The calculation unit Calculating a bug density, which is the value obtained by dividing the number of bugs by the number of man-hours, as the individual evaluation value. The information processing device according to claim 1 .

7. The acquisition unit As the test information, the number of slip-through bugs, which are bugs that should be detected in a previous stage test process in a plurality of test processes that are performed to check the quality of the predetermined system, but that are detected in a later stage test process that is performed after the previous stage test process, and the number of previous stage bugs, which are bugs that should be detected in the previous stage test process, are obtained; The calculation unit a bug slip-through rate is calculated as the individual evaluation value, which is a value obtained by dividing the number of slip-through bugs by the sum of the number of pre-stage bugs and the number of slip-through bugs; The information processing device according to claim 1 .

8. The system further includes a generating unit that generates content that visualizes a plurality of integrated evaluation values ​​obtained by integrating the plurality of individual evaluation values ​​in a plurality of different systems. The information processing device according to claim 1 .

9. An information processing method realized by a program executed by an information processing device, an acquisition step of acquiring test information relating to the results of tests conducted to verify the quality of a given system; a calculation step of calculating a comprehensive evaluation value for comprehensively evaluating the quality of the test based on a plurality of individual evaluation values ​​corresponding to each of a plurality of different evaluation methods for evaluating the quality of the test conducted to confirm the quality of the predetermined system based on the test information; a generating step of generating evaluation report information that visualizes an evaluation of the quality of a test conducted to confirm the quality of the predetermined system based on the plurality of individual evaluation values ​​and the comprehensive evaluation value; An information processing method including:

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