Information processing device 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

[0006] , , , ,

[0001] The present invention relates to an information processing apparatus and an information processing method.

Background Art

[0002] Conventionally, a technique for managing a test process in system development is known. For example, a technique is known in which a test work screen from the start to the end of a test for a specified test item is recorded as a video, and file information and test item information used by the test target software are recorded.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the above prior art, since only a test work screen from the start to the end of a test for a specified test item is recorded as a video, and file information and test item information used by the test target software are recorded, it is not always possible to improve the quality of the test performed 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 capable of improving the quality of a test performed to confirm the quality of a system.

Means for Solving the Problems

[0006] The information processing device according to the present invention includes: an acquisition unit that acquires test information relating to the results of tests conducted to confirm the quality of a predetermined system; a calculation unit that calculates an overall evaluation value for comprehensively evaluating 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; and a generation unit that generates an 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. [Effects of the Invention]

[0007] According to one embodiment, it is possible to improve the quality of tests performed to verify the quality of the system. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 is a diagram illustrating the system development process. [Figure 2] Figure 2 shows an example of the configuration of an information processing system according to the embodiment. [Figure 3] Figure 3 shows an example of the configuration of an information processing device according to the embodiment. [Figure 4] Figure 4 is a diagram illustrating the method for calculating the overall evaluation value according to the embodiment. [Figure 5] Figure 5 is a diagram illustrating the evaluation method for test density and bug density according to the embodiment. [Figure 6] Figure 6 is a diagram illustrating the method for evaluating test density and bug density according to the embodiment. [Figure 7] Figure 7 is a diagram illustrating the method for evaluating the bug bypass rate according to the embodiment. [Figure 8] Figure 8 is a diagram illustrating the bug bypass rate according to the embodiment. [Figure 9]Figure 9 shows an example of evaluation report information according to the embodiment. [Figure 10] Figure 10 is a flowchart showing the processing procedure by the information processing device according to the embodiment. [Figure 11] Figure 11 is a diagram illustrating the zone analysis related to the first modified example. [Figure 12] Figure 12 shows the content visualizing the integrated evaluation values ​​related to the second modified example. [Figure 13] Figure 13 is a hardware configuration diagram showing an example of a computer that implements the functions of an information processing device. [Modes for carrying out the invention]

[0009] The following describes in detail, with reference to the drawings, the embodiments for implementing the information processing apparatus and information processing method according to the present application (hereinafter referred to as "embodiments"). Note that these embodiments do not limit the information processing apparatus and information processing method according to the present application. Furthermore, the same parts are denoted by the same reference numerals in each of the following embodiments, and redundant descriptions are omitted.

[0010] (Embodiment) [1. Introduction] Figure 1 is a diagram illustrating the system development process. Figure 1 shows an example of a system development process generally known as the waterfall model. The waterfall model system development is divided into multiple stages, including business requirements definition, system requirements definition, basic design, detailed design, system implementation (manufacturing), unit testing, integration testing, comprehensive testing, and user acceptance testing, with each stage having a specific sequence. In this application, the stages from business requirements definition to system implementation (manufacturing) are referred to as the manufacturing stage, and the stages from unit testing to user acceptance testing are referred to as the testing stage.

[0011] System development starts from the process of defining business requirements. In the process of defining business requirements, the system developer interviews the client to understand the process of the business for which systemization is desired, and summarizes the business requirements in a business requirements document. Business requirements are summarized from the perspectives of what issues or problems in the business have led to the need for system development and what business processes are desired to be realized as the ultimate goal. Business requirements are defined from the perspectives of the client and users.

[0012] Also, the next step after the process of defining business requirements is to proceed to the process of defining system requirements. In the process of defining system requirements, the system developer summarizes the system requirements such as functions and performance to be installed in the system in a system requirements document in order to realize the content summarized in the business requirements document. System requirements are classified into functional requirements that define the functions to be implemented and non-functional requirements that define the content other than functions such as performance, stability, and security levels to be satisfied.

[0013] Also, the next step after the process of defining system requirements is to proceed to the basic design process. In the basic design process, the system developer specifies the functions to be implemented in the system in order to realize the content summarized in the system requirements document and summarizes them in a basic design document. Basic design is also called external design, and the system developer determines the specifications of the appearance (the external part of the system) such as the screens used by the client and users from the perspectives of the client and users who actually use the system. <000009=]] Also, the next step after the basic design process is to proceed to the detailed design process. In the detailed design process, the system developer conducts a detailed design on how to implement the functions to be implemented in the system in order to realize the content summarized in the basic design document and summarizes them in a detailed design document. Detailed design is also called internal design, and the system developer determines the internal specifications of the system from the perspective of the developer.

[0015] Next, after the detailed design process, the system implementation (manufacturing) process begins. In the system implementation (manufacturing) process, the system developer performs programming based on the detailed design document and creates the source code that constitutes the system to be developed. Note that the system to be developed is created by combining multiple source codes. For example, the source code corresponds to the source code for a module, which is a component of the program.

[0016] Next, after the system implementation (manufacturing) process, the unit test process begins. In the unit test process, the system developer verifies whether the program operates normally in terms of individual functions. In the unit test process, it is tested whether the modules, which are components of the program, function properly. In other words, the unit test is a test conducted to confirm the quality of each module included in the system to be developed. Note that the content verified in the unit test corresponds to the content of the detailed design document.

[0017] Next, after the unit test process, the integration test process begins. In the integration test process, the system developer verifies whether the functions combined operate normally. In the integration test process, in addition to the combination of modules, tests are also conducted to confirm the integration with external modules. In other words, it is a test conducted to confirm the quality of the combination of multiple modules included in the system to be developed. Note that the content verified in the integration test corresponds to the content of the basic design document.

[0018] Next, after the integration test process, the comprehensive test process begins. In the comprehensive test process, the system developer verifies whether the system operates normally when all functions are combined. In other words, the comprehensive test is a test conducted to confirm the quality of the entire system to be developed. Note that the content verified in the comprehensive test corresponds to the content of the system requirements definition document.

[0019] Next, following the integrated testing phase, the process moves on to user acceptance testing. In user acceptance testing, the system developers verify that the system functions correctly in the actual operating environment used by the client or users. In other words, user acceptance testing is a test to check the quality of the system under development in the actual operating environment used by the client or users. The content verified in user acceptance testing corresponds to the content of the business requirements specification document.

[0020] This application focuses on the unit testing process, integration testing process, and system testing process shown in Figure 1. Furthermore, in the following, the unit testing process, integration testing process, and system testing process will be collectively referred to as the "testing process." The tests performed in the testing process are, for example, conducted to confirm the quality of the system under development. Conventionally, it has not been possible to comprehensively visualize multiple evaluation results corresponding to each of multiple different evaluation methods used to evaluate the quality of tests conducted to confirm the quality of the system under development. Also, conventionally, it has not been possible to visualize the overall evaluation results for the quality of tests conducted to confirm the quality of the system under development.

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

[0022] This allows the information processing device to comprehensively visualize multiple evaluation results corresponding to each of several different evaluation methods used to assess the quality of tests conducted to verify the quality of a system under development. Furthermore, the information processing device can visualize the overall evaluation results of the quality of tests conducted to verify the quality of the system under development. Additionally, by presenting report information, the information processing device can encourage users, such as those involved in system development, to improve the quality of the tests. Therefore, the information processing device can enable improvements in the quality of tests conducted to verify the quality of a system.

[0023] [2. Configuration of the Information Processing System] Figure 2 shows an example configuration of an information processing system 1 according to an embodiment. As shown in Figure 2, the information processing system 1 according to an 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) by wired or wireless means.

[0024] The user terminal 10 is an information processing device used by users, such as those involved in system development. For example, the user terminal 10 may be an information processing device such as a desktop PC (Personal Computer) or a notebook PC. Alternatively, the user terminal 10 may be a smart device such as a smartphone or tablet. The user terminal 10 displays information received from the information processing device 100, etc., using a web browser or 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 implemented by a server device, a cloud system, or the like. For example, the information processing device 100 may perform information processing according to the embodiment in accordance with the information processing method implemented by the information processing program according to the embodiment.

[0026] [3. Configuration of the Information Processing Device] Figure 3 shows an example of the configuration of an 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 130.

[0027] (Communications Department 110) The communication unit 110 is connected to the network N by wire or wireless connection and transmits and receives information between the user terminal 10 and the collection server 20. For example, the communication unit 110 can be implemented using a NIC (Network Interface Card) or an antenna.

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

[0029] (Control unit 130) The control unit 130 is a controller, and is realized, for example, by executing various programs stored in the memory device inside the information processing device 100 using RAM as the working area, using a CPU (Central Processing Unit) or MPU (Micro Processing Unit), etc. Alternatively, the control unit 130 is a controller and can be realized, for example, by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or 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 operations described below. Note that the internal configuration of the control unit 130 is not limited to the configuration shown in Figure 3, and other configurations are also acceptable as long as they perform the information processing described later. Furthermore, each functional unit represents the function of the control unit 130 and does not necessarily have to be physically separated.

[0031] (Acquisition part 131) The acquisition unit 131 acquires test information relating 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 acquisition unit 131 acquires test information relating to the results of unit tests, integration tests, and comprehensive tests conducted to confirm the quality of the predetermined system. For example, the acquisition unit 131 may acquire test information from the user terminal 10.

[0032] Furthermore, the acquisition unit 131 acquires the number of tests performed to confirm the quality of a given system as test information. For example, it acquires the number of tests performed in each of the unit tests, integration tests, and comprehensive tests performed to confirm the quality of a given system as test information.

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

[0034] Furthermore, the acquisition unit 131 acquires, as test information, the number of slip-through bugs, which are bugs that should be detected in the preceding test process but are detected in the subsequent test process, and the number of preceding bugs, which are bugs that should be detected in the preceding test process but are detected in the preceding test process.

[0035] For example, multiple test processes performed to verify the quality of a given system may consist of three test processes: unit testing, integration testing, and system testing. Furthermore, the preceding test process in multiple test processes may be, for example, unit testing. For example, if the preceding test process is unit testing, then the subsequent test processes performed after the preceding test process are integration testing and system testing. Also, the preceding test process in multiple test processes may be, for example, integration testing. For example, if the preceding test process is integration testing, then the subsequent test processes performed after the preceding test process are system testing.

[0036] Furthermore, each bug detected in a test phase is pre-associated with information indicating the level of testing at which that bug should be detected. For example, a bug that should be detected in the unit testing phase is associated with information indicating the unit testing phase (e.g., a label such as "unit level bug"). Similarly, a bug that should be detected in the integration testing phase is associated with information indicating the integration testing phase (e.g., a label such as "integration level bug"). And a bug that should be detected in the system testing phase is associated with information indicating the system testing phase (e.g., a label such as "system level bug").

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

[0038] Furthermore, a pre-stage bug refers to a bug that should be detected during the unit testing phase (for example, a bug associated with the label "unit-level bug"), but is detected during the unit testing phase. Below, a bug that should be detected during the unit testing phase (for example, a bug associated with the label "unit-level bug"), but is detected during the unit testing phase, may be referred to as a "unit pre-stage bug." Furthermore, a pre-stage bug refers to a bug that should be detected during the integration testing phase (for example, a bug associated with the label "integration-level bug"), but is detected during the integration testing phase. Below, a bug that should be detected during the integration testing phase (for example, a bug associated with the label "integration-level bug"), but is detected during the integration testing phase, may be referred to as an "integration pre-stage bug."

[0039] (Calculation section 132) 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 tests performed to confirm the quality of a predetermined system. For example, the calculation unit 132 calculates the test density as an individual evaluation value, which is the value obtained by dividing the number of tests by the effort. Here, effort refers to the estimated effort for the system. More specifically, effort may be the estimated effort provided by the development vendor for each system. For example, effort may be an estimate of the time required to develop the system. Alternatively, effort may be the number of steps (e.g., lines) of the source code that constitutes the system. The calculation unit 132 also calculates the bug density as an individual evaluation value, which is the value obtained by dividing the number of bugs by the effort. Furthermore, the calculation unit 132 calculates the bug slippage rate as an individual evaluation value, which is the value obtained by dividing the number of slippage bugs by the sum of the number of preceding bugs and the number of slippage bugs. Furthermore, the calculation unit 132 calculates an overall evaluation value based on the test density, bug density, and bug bypass rate.

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

[0041] For example, the calculation unit 132 calculates the unit test density, which is the test density used to evaluate the quality of unit tests. The calculation unit 132 calculates the unit test density, which is the value obtained by dividing the number of unit tests by the man-hours required. The calculation unit 132 also calculates the unit bug density, which is the bug density used to evaluate the quality of unit tests. The calculation unit 132 calculates the unit bug pass rate, which is the bug pass rate used to evaluate the quality of unit tests. The calculation unit 132 calculates the unit bug pass rate, which is the value obtained by dividing the number of unit bugs that passed through by the sum of the number of unit pre-bugs and the number of unit bugs that passed through. The calculation unit 132 also calculates an overall evaluation value based on the unit test density, unit bug density, and unit bug pass rate.

[0042] Furthermore, the calculation unit 132 calculates the integration test density, which is the test density used to evaluate the quality of the integration tests. The calculation unit 132 calculates the integration test density, which is the value obtained by dividing the number of integration tests by the man-hours. Furthermore, the calculation unit 132 calculates the integration bug density, which is the bug density used to evaluate the quality of the integration tests. The calculation unit 132 calculates the integration bug density, which is the value obtained by dividing the number of bugs detected in the integration tests by the man-hours. Furthermore, the calculation unit 132 calculates the integration bug slippage rate, which is the bug slippage rate used to evaluate the quality of the integration tests. The calculation unit 132 calculates the integration bug slippage rate, which is the value obtained by dividing the number of integration slippage bugs by the sum of the number of pre-integration bugs and the number of integration slippage bugs. Furthermore, the calculation unit 132 calculates an overall evaluation value based on the integration test density, integration bug density, and integration bug slippage rate.

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

[0044] More specifically, the calculation unit 132 calculates unit test density evaluation points, integration test density evaluation points, and total test density evaluation points corresponding to unit test density, integration test density, and total test density, respectively. The calculation unit 132 also calculates unit bug density evaluation points, integration bug density evaluation points, and total bug density evaluation points corresponding to unit bug density, integration bug density, and total bug density, respectively. The calculation unit 132 also calculates unit bug slippage rate evaluation points corresponding to the unit bug slippage rate value and integration bug slippage rate evaluation points corresponding to the integration bug slippage rate value. The calculation unit 132 also calculates a total evaluation value based on the unit test density evaluation points, integration test density evaluation points, total test density evaluation points, unit bug density evaluation points, integration bug density evaluation points, total bug density evaluation points, unit bug slippage rate evaluation points, and integration bug slippage rate evaluation points. Furthermore, the calculation unit 132 calculates a summation value which is the sum of the unit test density evaluation score, integration test density evaluation score, overall test density evaluation score, unit bug density evaluation score, integration bug density evaluation score, overall bug density evaluation score, unit bug slippage rate evaluation score, and integration bug slippage rate evaluation score. The calculation unit 132 also converts the summation value to a value between 0 and 100 to obtain the overall evaluation value. Furthermore, if the overall evaluation value is 90 or higher, the calculation unit 132 decides to assign the overall evaluation to an A rating, which is the highest possible rating. Furthermore, if the overall evaluation value is between 50 and 90, the calculation unit 132 decides to assign the overall evaluation to a B rating, which is the next lowest rating after A. Furthermore, if the overall evaluation value is between 30 and 50, the calculation unit 132 decides to assign the overall evaluation to a C rating, which is the next lowest rating after B. Furthermore, if the overall evaluation value is less than 30, the calculation unit 132 decides to assign the overall evaluation to a D rating, which is the lowest possible rating.

[0045] Figure 5 is a diagram illustrating the evaluation method for test density and bug density according to the embodiment. As shown in the upper part of Figure 5, the calculation unit 132 calculates a test density evaluation score corresponding to the test density value calculated in Figure 4, based on a comparison of the reference value of test density and the test density value calculated in Figure 4. For example, if the test density value calculated in Figure 4 is 75% or more of the reference value of test density and 125% or less of the reference value of test density, the calculation unit 132 determines the evaluation of the test density calculated in Figure 4 to be an A rating, which is the highest evaluation. Also, if the calculation unit 132 determines the evaluation of the test density calculated in Figure 4 to be an A rating, it calculates a test density evaluation score of 100 points corresponding to the test density value calculated in Figure 4. Also, if the test density value calculated in Figure 4 is more than 125% of the reference value of test density, the calculation unit 132 determines the evaluation of the test density calculated in Figure 4 to be a B rating, which is an intermediate evaluation. Furthermore, if the calculation unit 132 determines that the test density calculated in Figure 4 is a B rating, it calculates a test density evaluation score of 70 points corresponding to the test density value calculated in Figure 4. Also, if the test density value calculated in Figure 4 is less than 75% of the standard value of test density, the calculation unit 132 determines that the evaluation of the test density calculated in Figure 4 is a C rating, which is the lowest possible rating. Also, if the calculation unit 132 determines that the test density calculated in Figure 4 is a C rating, it calculates a test density evaluation score of 10 points corresponding to the test density value calculated in Figure 4.

[0046] Furthermore, as shown in the lower part of Figure 5, the calculation unit 132 calculates a bug density evaluation score corresponding to the bug density value calculated in Figure 4, based on a comparison between the reference value of bug density and the bug density value calculated in Figure 4. For example, if the bug density value calculated in Figure 4 is 75% or more of the reference value of bug density and 125% or less of the reference value of bug density, the calculation unit 132 determines that the evaluation of the bug density calculated in Figure 4 is A, which is the highest evaluation. Also, if the calculation unit 132 determines that the evaluation of the bug density calculated in Figure 4 is A, it calculates a bug density evaluation score of 100 points corresponding to the bug density value calculated in Figure 4. Also, if the bug density value calculated in Figure 4 is less than 75% of the reference value of bug density, the calculation unit 132 determines that the evaluation of the bug density calculated in Figure 4 is B, which is an intermediate evaluation. Furthermore, if the calculation unit 132 determines that the bug density calculated in Figure 4 is a B rating, it calculates a bug density evaluation score of 70 points corresponding to the bug density value calculated in Figure 4. Also, if the bug density value calculated in Figure 4 exceeds 125% of the standard bug density value, the calculation unit 132 determines that the bug density calculated in Figure 4 is a C rating, which is the lowest possible rating. Also, if the calculation unit 132 determines that the bug density calculated in Figure 4 is a C rating, it calculates a bug density evaluation score of 10 points corresponding to the bug density value calculated in Figure 4.

[0047] In Figure 5, the lower limit of an A rating is explained as being 75% or more of the standard value of test density (or bug density). However, the lower limit of an A rating is not limited to being 75% or more 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 70% or more of the standard value of test density (or bug density) or 80% or more of the standard value. Similarly, in Figure 5, the upper limit of an A rating is explained as being 125% or less of the standard value of test density (or bug density). However, the upper limit of an A rating is not limited to being 125% or less of the standard value of test density (or bug density). For example, the upper limit of an A rating may be any percentage more than 100% of the standard value, such as 120% or more of the standard value of test density (or bug density) or 130% or more of the standard value.

[0048] Figure 6 is a diagram illustrating the evaluation method for test density and bug density according to the embodiment. Figure 6 illustrates the case where the reference value of test density (or bug density) described in Figure 5 is the average value of test density (or bug density) over the target period (for example, the year 2023). Furthermore, Figure 6 illustrates the case where the test density (or bug density) calculated in Figure 4 is the test density (or bug density) over the evaluation period included in the target period (for example, the month of April included in the year 2023).

[0049] In the left-hand diagram of Figure 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 system function, 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 left-hand diagram of Figure 6, cases A through C arose for system A during the month of April 2023. The calculation unit 132 calculates the test density for each of cases A through C. The calculation unit 132 also calculates the bug density for each of cases A through C. Furthermore, cases D and E arose for system A during the 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 arose for system A during the 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 arose for system B during the 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 month of May 2023, Case I occurred for System B. The calculation unit 132 calculates the test density for Case I. The calculation unit 132 also calculates the bug density for Case I.

[0051] In the central diagram of Figure 6, the calculation unit 132 calculates the average test density and bug density for each system during the target period. The calculation unit 132 also calculates the lower and upper limits for an A rating for test density and bug density for each system based on the average test density and bug density for each system.

[0052] For example, the calculation unit 132 calculates the average values ​​of test density and bug density for system A during the target period. For example, the calculation unit 132 calculates the average value of test density for system A during the period from April to June 2023 (an example of a target period for system A) based on the test density for cases A to F explained in the left-hand diagram of Figure 6. The calculation unit 132 also calculates the average value of bug density for system A during the period from April to June 2023 based on the bug density for cases A to F explained in the left-hand diagram of Figure 6. Furthermore, the calculation unit 132 calculates the lower and upper limits of the A rating for test density for system A during the target period based on the respective average values ​​of test density and bug density for system A during the target period.

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

[0054] In the right-hand diagram of Figure 6, the calculation unit 132 calculates the test density evaluation score and bug density evaluation score corresponding to the test density and bug density during the evaluation period, based on a comparison of the average values ​​of test density and bug density during the target period with the average values ​​of test density and bug density during the evaluation period included in the target period. In the right-hand diagram of Figure 6, the calculation unit 132 calculates the average value of test density (or bug density) for the one month of 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 the test density evaluation score (or bug density evaluation score) corresponding to the average value of test density (or bug density) for System A during the one month of April 2023, based on a comparison of the average value of test density (or bug density) for System A during the period of April to June 2023 with the average value of test density (or bug density) for System A during the one month of April 2023. More specifically, the calculation unit 132 calculates a test density evaluation score (or bug density evaluation score) corresponding to the average test density (or bug density) of System A for the month of April 2023, based on a comparison of the lower and upper limits of System A's test density (or bug density) for the period from April to June 2023 with the average test density (or bug density) of System A for the month of April 2023. The calculation unit 132 also calculates an overall evaluation value for System A for the month of April 2023 based on System A's test density evaluation score and bug density evaluation score for the month of April 2023.

[0055] Similarly, the calculation unit 132 calculates the average test density (or bug density) for the month of 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 test density (or bug density) for System A in the month of May 2023, based on a comparison of the average test density (or bug density) for System A during the period from April to June 2023 and the average test density (or bug density) for System A during the month of May 2023. More specifically, the calculation unit 132 calculates a test density evaluation score (or bug density evaluation score) corresponding to the average test density (or bug density) of System A for the month of May 2023, based on a comparison of the lower and upper limits of System A's test density (or bug density) for the period from April to June 2023 with the average test density (or bug density) of System A for the month of May 2023. The calculation unit 132 also calculates an overall evaluation value for System A for the month of May 2023 based on System A's test density evaluation score and bug density evaluation score for the month of May 2023.

[0056] Similarly, the calculation unit 132 calculates the average test density (or bug density) for the month of June 2023 (an example of an evaluation period for System A) based on the test density (or bug density) in Case F. The calculation unit 132 then calculates a test density evaluation score (or bug density evaluation score) corresponding to the average test density (or bug density) for System A during the month of June 2023, based on a comparison of the average test density (or bug density) for System A during the period of April to June 2023 and the average test density (or bug density) for System A during the month of June 2023. More specifically, the calculation unit 132 calculates a test density evaluation score (or bug density evaluation score) corresponding to the average test density (or bug density) of System A for the month of June 2023, based on a comparison of the lower and upper limits of System A's test density (or bug density) for the period from April to June 2023 with the average test density (or bug density) of System A for the month of June 2023. The calculation unit 132 also calculates an overall evaluation value for System A for the month of June 2023 based on System A's test density evaluation score and bug density evaluation score for the month of June 2023.

[0057] Similarly, the calculation unit 132 calculates the average test density (or bug density) for the one month of 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 test density (or bug density) for System B during the one month of April 2023, based on a comparison of the average test density (or bug density) for System B during the period of April to May 2023 with the average test density (or bug density) for System B during the one month of April 2023. More specifically, the calculation unit 132 calculates a test density evaluation score (or bug density evaluation score) corresponding to the average test density (or bug density) of System B for the month of April 2023, based on a comparison of the lower and upper limits of System B's A-rated test density (or bug density) for the period from April to May 2023 with the average test density (or bug density) of System B for the month of April 2023. The calculation unit 132 also calculates an overall evaluation value for System B for the month of April 2023, based on System B's test density evaluation score and bug density evaluation score for the month of April 2023.

[0058] Similarly, the calculation unit 132 calculates the average test density (or bug density) for the month of May 2023 (an example of an evaluation period for System B) based on the test density (or bug density) in Case I. Furthermore, the calculation unit 132 calculates a test density evaluation score (or bug density evaluation score) corresponding to the average test density (or bug density) for System B in the month of May 2023, based on a comparison of the average test density (or bug density) for System B during the period of April to May 2023 and the average test density (or bug density) for System B during the month of May 2023. More specifically, the calculation unit 132 calculates a test density evaluation score (or bug density evaluation score) corresponding to the average test density (or bug density) of System B for the month of May 2023, based on a comparison of the lower and upper limits of System B's A-rated test density (or bug density) for the period from April to May 2023 with the average test density (or bug density) of System B for the month of May 2023. The calculation unit 132 also calculates an overall evaluation value for System B for the month of May 2023, based on System B's test density evaluation score and bug density evaluation score for the month of May 2023.

[0059] As explained in Figure 6, the calculation unit 132 calculates multiple individual evaluation scores corresponding to each of the multiple individual evaluation scores in the evaluation period based on a comparison of the average value of each of the multiple individual evaluation scores in the target period with the average value of each of the multiple individual evaluation scores in the evaluation period included in the target period, and calculates the overall evaluation score for the evaluation period based on the multiple individual evaluation scores for the evaluation period. Furthermore, the calculation unit 132 calculates multiple individual evaluation scores for each of the multiple different evaluation periods based on a comparison of the average value of each of the multiple individual evaluation scores in the target period with the average value of each of the multiple individual evaluation scores in each of the multiple different evaluation periods, and calculates the overall evaluation score for each of the multiple different evaluation periods based on the multiple individual evaluation scores for each of the multiple different evaluation periods.

[0060] Figure 7 is a diagram illustrating the method for evaluating the bug bypass rate according to the embodiment. In Figure 7, if the bug bypass rate value calculated in Figure 4 is between 0% and 10%, the calculation unit 132 determines that the evaluation of the bug bypass rate calculated in Figure 4 is an A rating, which is the highest possible evaluation. Furthermore, if the calculation unit 132 determines that the evaluation of the bug bypass rate calculated in Figure 4 is an A rating, it calculates a bug bypass rate evaluation score of 100 points corresponding to the bug bypass rate value calculated in Figure 4.

[0061] Furthermore, if the bug bypass rate calculated in Figure 4 is between 10% and 20%, the calculation unit 132 determines that the evaluation of the bug bypass rate calculated in Figure 4 is a B rating, which is the next lowest rating after A. Also, if the calculation unit 132 determines that the evaluation of the bug bypass rate calculated in Figure 4 is a B rating, it calculates a bug bypass rate evaluation score of 70 points corresponding to the bug bypass rate value calculated in Figure 4.

[0062] Furthermore, if the bug bypass rate calculated in Figure 4 is between 20% and 50%, the calculation unit 132 determines that the evaluation of the bug bypass rate calculated in Figure 4 is a C rating, which is the next lowest rating after a B rating. Also, if the calculation unit 132 determines that the evaluation of the bug bypass rate calculated in Figure 4 is a C rating, it calculates a bug bypass rate evaluation score of 30 points corresponding to the bug bypass rate value calculated in Figure 4.

[0063] Furthermore, if the bug bypass rate calculated in Figure 4 is between 50% and 100%, the calculation unit 132 determines that the evaluation of the bug bypass rate calculated in Figure 4 is the lowest possible evaluation, a D rating. Also, if the calculation unit 132 determines that the evaluation of the bug bypass rate calculated in Figure 4 is a D rating, it calculates a bug bypass rate evaluation score of 10 points corresponding to the bug bypass rate value calculated in Figure 4.

[0064] Note that while Figure 7 illustrates the case where the ranges corresponding to each of the A to D ratings are 0-10%, 10-20%, 20-50%, and 50-100%, the ranges corresponding to each of the A to D ratings are not limited to 0-10%, 10-20%, 20-50%, and 50-100%. Specifically, the ranges corresponding to each of the A to D ratings may be any range specified in advance by the system developers or other relevant parties.

[0065] Figure 8 is a diagram illustrating the bug bypass rate according to the embodiment. In Figure 8, the number of standalone pre-stage bugs is 35. The number of standalone bypass bugs is 15. In Figure 8, the calculation unit 132 calculates the standalone bug bypass rate as 0.3 (equivalent to 30%) by dividing the number of standalone bypass bugs, which is 15, by the sum of the number of standalone pre-stage bugs (35) and the number of standalone bypass bugs (15), which is 50.

[0066] (Generation unit 133) The generation unit 133 generates evaluation report information that visualizes the evaluation of the quality of tests performed to confirm the quality of a predetermined system, based on a plurality of individual evaluation values ​​and an overall evaluation value. Figure 9 is a diagram showing an example of evaluation report information according to the embodiment. The generation unit 133 generates evaluation report information such as that shown in Figure 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 Figure 9, the generation unit 133 generates evaluation report information that visualizes each of the values ​​of unit test density, integration test density, overall test density, unit bug density, integration bug density, overall bug density, unit bug pass-through rate, and integration bug pass-through rate calculated by the calculation unit 132.

[0067] Furthermore, the generation unit 133 generates report information that visualizes the evaluations corresponding to each of the multiple individual evaluation points calculated by the calculation unit 132. In Figure 9, the generation unit 133 generates report information that visualizes the evaluations (e.g., A to D ratings) corresponding to each of the unit test density evaluation points, integration test density evaluation points, overall test density evaluation points, unit bug density evaluation points, integration bug density evaluation points, overall bug density evaluation points, unit bug slippage rate evaluation points, and integration bug slippage rate evaluation points calculated by the calculation unit 132. In addition, the generation unit 133 generates report information that visualizes the overall evaluation (e.g., A to D ratings) determined by the calculation unit 132.

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

[0069] Furthermore, the generation unit 133 may generate report information that visualizes the evaluations corresponding to each of the multiple individual evaluation points in each of the multiple different evaluation periods. For example, the generation unit 133 generates report information that visualizes the evaluations (e.g., A to D ratings) corresponding to each of the unit test density evaluation points, integration test density evaluation points, overall test density evaluation points, unit bug density evaluation points, integration bug density evaluation points, overall bug density evaluation points, unit bug pass-through rate evaluation points, and integration bug pass-through rate evaluation points in each of the multiple different evaluation periods. In addition, the generation unit 133 may generate report information that visualizes the overall evaluation (e.g., A to D ratings) in each of the multiple different evaluation periods. In Figure 9, the generation unit 133 generates report information that visualizes the evaluations corresponding to each of the multiple individual evaluation points in the current evaluation period, June 2023. In addition, the generation unit 133 generates report information that visualizes the evaluations corresponding to each of the multiple individual evaluation points in the previous evaluation period, March 2023.

[0070] (Provider 134) The provisioning unit 134 provides the user with evaluation report information that visualizes the evaluation of the quality of tests conducted to confirm the quality of a predetermined system. The user is a development person involved in system development, etc. For example, the provisioning unit 134 transmits the evaluation report information generated by the generation unit 133 to the user terminal 10.

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

[0072] [5. Variations] The processing according to the above-described embodiment may be carried out in various other forms besides those described above.

[0073] Figure 11 is a diagram illustrating the zone analysis according to the first modified example. The generation unit 133 generates zone analysis information that associates, on a one-to-one basis, a combination of evaluations corresponding to each of the multiple individual evaluation points with each region of the matrix. This allows the information processing device 100 to enable the user to visually grasp the combination of evaluations that correspond to each of the multiple individual evaluation points. In Figure 11, the generation unit 133 generates zone analysis information that associates, on a one-to-one basis, a combination of a test density evaluation (which corresponds to the test density evaluation point) and a bug density evaluation (which corresponds to the bug density evaluation point) with each region of the nine-quadrant matrix. The providing unit 134 then provides the zone analysis information generated by the generation unit 133 to the user.

[0074] In the nine-quadrant matrix shown in Figure 11, the center column represents a test density rating of A. The right-hand column represents a test density rating of B. The left-hand column represents a test density rating of C. The center row represents a bug density rating of A. The lower row represents a bug density rating of B. The upper row represents a bug density rating of C. For example, when both the test density and bug density ratings are A, the combined evaluation is highest, corresponding to the center of the nine regions. When both the test density and bug density ratings are C, the combined evaluation is lowest, corresponding to the top-left region. When both the test density and bug density ratings are B, the combined evaluation is average, corresponding to the bottom-right region.

[0075] Figure 12 shows content that visualizes the integrated evaluation values ​​related to the second modification. The generation unit 133 generates content that visualizes multiple integrated evaluation values ​​obtained by integrating each of the multiple individual evaluation values ​​in multiple different systems. In Figure 12, the generation unit 133 generates content that displays the sum of the test density, bug density, and number of bugs in multiple different systems as a bar graph. The generation unit 133 also generates content that displays the sum of the test density, bug density, and number of bugs in each of the unit tests, integration tests, and system tests in multiple different systems as a bar graph. The generation unit 133 may also generate content that displays the sum of the test density, bug density, and number of bugs in multiple different projects as a bar graph. The provision unit 134 provides the content generated by the generation unit 133 to the 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 relating to the results of tests conducted to confirm the quality of a predetermined system. Based on the test information, the calculation unit 132 calculates an overall 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. 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 tests conducted to confirm the quality of a predetermined system.

[0077] As a result, the information processing device 100 can comprehensively visualize multiple evaluation results corresponding to each of several different evaluation methods for evaluating the quality of tests performed to confirm the quality of a given system. Furthermore, the information processing device 100 can visualize the overall evaluation results of the quality of tests performed to confirm the quality of a given system. Additionally, the information processing device 100 can encourage users, such as those involved in system development, to improve the quality of tests by, for example, presenting report information. Therefore, the information processing device 100 can enable improvements in the quality of tests performed to confirm the quality of a system. Moreover, because the information processing device 100 can enable improvements in the quality of tests performed to confirm the quality of a system, it can contribute to achieving Sustainable Development Goal (SDG) 9, "Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation."

[0078] Furthermore, the calculation unit 132 calculates multiple individual evaluation scores corresponding to each of the multiple individual evaluation scores in the evaluation period based on a comparison between the average value of each of the multiple individual evaluation scores in the target period and the average value of each of the multiple individual evaluation scores in the evaluation period included in the target period, and calculates the overall evaluation score for the evaluation period based on the multiple individual evaluation scores in the evaluation period.

[0079] As a result, the information processing device 100 can calculate an overall evaluation value appropriate to the system based on individual evaluation scores appropriate to the system, thereby enabling the evaluation of the quality of tests appropriate to the system.

[0080] Furthermore, the calculation unit 132 calculates multiple individual evaluation scores for each of the multiple different evaluation periods based on a comparison of the average value of each of the multiple individual evaluation values ​​during the target period with the average value of each of the multiple individual evaluation values ​​during each of the multiple different evaluation periods, and then calculates an overall evaluation value for each of the multiple different evaluation periods based on the multiple individual evaluation scores for each of the multiple different evaluation periods.

[0081] As a result, the information processing device 100 can calculate an overall evaluation value for each of several different periods, making it possible to understand the temporal trend of improvement or deterioration in test quality.

[0082] Furthermore, the acquisition unit 131 acquires test information regarding the results of unit tests, integration tests, and system tests performed to confirm the quality of a predetermined system. The calculation unit 132 calculates multiple individual evaluation values ​​to evaluate the quality of each of the unit tests, integration tests, and system tests, and calculates an overall evaluation value based on the multiple individual evaluation values ​​for each of the unit tests, integration tests, and system tests.

[0083] This enables the information processing device 100 to evaluate the overall quality of the tests based on evaluations of each of the unit tests, integration tests, and system tests.

[0084] Furthermore, the acquisition unit 131 acquires the number of tests performed to confirm the quality of a predetermined system as test information. The calculation unit 132 calculates the test density as an individual evaluation value, which is the 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 tests based on the test density.

[0086] Furthermore, the acquisition unit 131 acquires the number of bugs detected in tests conducted to confirm the quality of a predetermined system as test information. The calculation unit 132 calculates the bug density as an individual evaluation value, which is the number of bugs divided by the 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 acquisition unit 131 acquires, as test information, the number of "slip-through bugs" which are bugs that should be detected in the preceding test process but are detected in the subsequent test process which is performed after the preceding test process, and the number of "preceding bugs" which are bugs that should be detected in the preceding test process but are detected in the preceding test process. The calculation unit 132 calculates the bug slip-through rate as an individual evaluation value, which is the number of slip-through bugs divided by the sum of the number of preceding bugs and the number of slip-through bugs.

[0089] This allows the information processing device 100 to evaluate the quality of the tests based on the bug pass rate.

[0090] Furthermore, the information processing device 100 further includes a generation unit 133. The generation unit 133 generates content that visualizes multiple integrated evaluation values, which are obtained by integrating each of multiple individual evaluation values ​​from multiple different systems.

[0091] This enables the information processing device 100 to comprehensively understand the quality of tests across multiple different systems.

[0092] [7. Hardware Configuration] Furthermore, the information processing device 100 according to the above-described embodiment is realized by a computer 1000 having a configuration such as that shown in Figure 13. Figure 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, RAM 1200, ROM 1300, HDD 1400, communication interface (I / F) 1500, input / output interface (I / F) 1600, and media interface (I / F) 1700.

[0093] The CPU 1100 operates based on programs stored in the ROM 1300 or HDD 1400, controlling various components. The ROM 1300 stores boot programs executed by the CPU 1100 when the computer 1000 starts up, as well as programs that depend on the computer 1000's hardware.

[0094] The HDD1400 stores programs executed by the CPU1100, as well as data used by such programs. The communication interface1500 receives data from other devices via a predetermined communication network and sends it to the CPU1100, and transmits data generated by the CPU1100 to other devices via the predetermined communication network.

[0095] The CPU 1100 controls output devices such as displays and printers, and input devices such as keyboards and mice, via the input / output interface 1600. The CPU 1100 acquires data from input devices via the input / output interface 1600. The CPU 1100 also outputs the generated data to output devices via the input / output interface 1600.

[0096] The media interface 1700 reads a program or data stored in the recording medium 1800 and provides it to the CPU 1100 via the RAM 1200. The CPU 1100 loads the program from the recording medium 1800 onto the RAM 1200 via the media interface 1700 and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase Change Rewritable Disk), 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 an information processing device 100 according to the embodiment, the CPU 1100 of the computer 1000 realizes the functions of the control unit 130 by executing a program loaded on the RAM 1200. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, these programs may be obtained from other devices via a predetermined communication network.

[0098] Although some embodiments of the present invention have been described in detail above with reference to the drawings, these are illustrative examples, and the present invention can be implemented in various other forms with modifications and improvements based on the knowledge of those skilled in the art, starting with the embodiments described in the disclosure section of the invention.

[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 by known methods. In addition, the processing procedures, specific names, and information including various data and parameters shown in the above document and drawings can be changed at will unless otherwise specified. For example, the various information shown in each figure is not limited to the information shown.

[0100] Furthermore, the components of each illustrated device are functionally conceptual and do not necessarily need to be physically configured as shown. In other words, the specific forms of distribution and integration of each device are not limited to those shown, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads and usage conditions.

[0101] Furthermore, the embodiments and modifications described above can be combined as appropriate, provided that the processing content is not inconsistent. [Explanation of Symbols]

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

Claims

1. An acquisition unit that acquires test information regarding the results of tests conducted to confirm the quality of a specified system, A calculation unit 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, A generation unit generates evaluation report information that visualizes the evaluation of the quality of the test based on the plurality of individual evaluation values ​​and the overall evaluation value, Equipped with, The calculation unit described above, Based on a comparison between the average value of each of the multiple individual evaluation values ​​during the target period and the average value of each of the multiple individual evaluation values ​​during the evaluation period included in the target period, multiple individual evaluation scores corresponding to each of the multiple individual evaluation values ​​during the evaluation period are calculated, and based on the multiple individual evaluation scores during the evaluation period, the overall evaluation value for the evaluation period is calculated. Information processing device.

2. The calculation unit described above, Based on a comparison of the average value of each of the multiple individual evaluation values ​​during the aforementioned target period with the average value of each of the multiple individual evaluation values ​​during each of the multiple different evaluation periods, the multiple individual evaluation scores for each of the multiple different evaluation periods are calculated, and based on the multiple individual evaluation scores for each of the multiple different evaluation periods, the overall evaluation value for each of the multiple different evaluation periods is calculated. The information processing apparatus according to claim 1.

3. The acquisition unit is, To confirm the quality of the predetermined system, the test information regarding the results of each of the unit tests, integration tests, and system tests performed is acquired. The calculation unit described above, The system calculates the multiple individual evaluation values ​​for evaluating the quality of each of the unit tests, integration tests, and system tests, and calculates the overall evaluation value based on the multiple individual evaluation values ​​for each of the unit tests, integration tests, and system tests. The information processing apparatus according to claim 1.

4. The acquisition unit is, As the test information, obtain the number of tests performed. The calculation unit described above, As the aforementioned individual evaluation value, the test density is calculated by dividing the number of tests by the effort required. The information processing apparatus according to claim 1.

5. The acquisition unit is, As the test information, the number of bugs detected in the test is obtained. The calculation unit described above, As the aforementioned individual evaluation value, the bug density is calculated by dividing the number of bugs by the effort required. The information processing apparatus according to claim 1.

6. The acquisition unit is, As the test information, the number of slip-through bugs, which are bugs that should be detected in the preceding test step of a plurality of test steps performed to confirm the quality of the predetermined system, but are detected in a later test step performed after the preceding test step, and the number of preceding bugs, which are bugs that should be detected in the preceding test step but are detected in the preceding test step, are obtained. The calculation unit described above, As the aforementioned individual evaluation value, the bug bypass rate is calculated by dividing the number of bypass bugs by the sum of the number of preceding bugs and the number of bypass bugs. The information processing apparatus according to claim 1.

7. The system further comprises a generation unit that generates content that visualizes multiple integrated evaluation values ​​obtained by integrating each of the multiple individual evaluation values ​​from multiple different systems. The information processing apparatus according to claim 1.

8. An information processing method implemented by a program executed by an information processing device, A process for acquiring test information regarding the results of tests conducted to verify the quality of a specified system, A calculation step to calculate 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, A generation process that generates evaluation report information that visualizes the evaluation of the quality of the test based on the plurality of individual evaluation values ​​and the overall evaluation value, Includes, The calculation process described above is: Based on a comparison between the average value of each of the multiple individual evaluation values ​​during the target period and the average value of each of the multiple individual evaluation values ​​during the evaluation period included in the target period, multiple individual evaluation scores corresponding to each of the multiple individual evaluation values ​​during the evaluation period are calculated, and based on the multiple individual evaluation scores during the evaluation period, the overall evaluation value for the evaluation period is calculated. Information processing methods.

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