Agile software development measurement data platform

Abstract

The invention mainly provides an agile software development measurement data platform, which comprises a data acquisition module used for acquiring data of a third-party system and synchronously testing related information; the index analysis module comprises a plurality of preset key indexes, an index calculation rule and an index threshold value, and is used for counting the acquired data, judging whether each index exceeds a normal range or not, forming a preliminary early warning signal and storing the preliminary early warning signal; the dynamic early warning module is used for obtaining the corresponding key indexes and comprehensive evaluation indexes according to different requirements of each project in combination with historical data and a current data state, and generating early warning information according to a preset dynamic threshold value; and the AI analysis module is used for analyzing the key index data of each item of the project by calling an AI intelligent agent, carrying out comprehensive performance evaluation, carrying out evaluation, analysis and summarization and giving analysis suggestions. According to the technical scheme, multi-dimensional dynamic early warning can be achieved, performance evaluation can be conducted on personnel in combination with the early warning indexes, and software development quality and efficiency are improved.

Description

Technical Field

[0001] This invention belongs to the field of data analysis, specifically relating to an agile software development measurement data platform that includes indicator early warning and performance evaluation. Technical Background

[0002] The existing agile software development metrics platforms mainly fall into the following categories: (1) They only display current metrics and lack the ability to provide early warning feedback and AI comparative analysis of abnormal metrics to give reminders or suggestions; (2) They lack feedback channels and fail to trace the feedback content back to the device, making continuous improvement impossible; (3) They lack performance appraisal capabilities, the ability to continuously optimize team efficiency and product quality, or traditional methods for assessing software testers suffer from problems such as difficulty in quantifying assessment dimensions and metrics, incomplete assessment scope, and inability to achieve automated assessment; and they focus on post-event evaluation, making it difficult to adapt to the fast pace and dynamic requirements of agile iteration. In agile development practices, the role of R&D personnel is becoming increasingly important. They not only need to respond quickly to changes in requirements but also ensure software quality.

[0003] Technical content

[0004] The present invention aims to solve at least one of the above-mentioned technical problems.

[0005] To address the above problems, this invention provides an agile software development metrics data platform, comprising:

[0006] The data acquisition module is used to collect data from third-party systems, capture relevant data of personnel and teams in real time during the agile development process, and synchronize test-related information.

[0007] The indicator analysis module includes multiple preset key indicators, indicator calculation rules, and indicator thresholds. It is used to statistically analyze the acquired data, determine whether each indicator exceeds the normal range, generate preliminary early warning signals, and store them. The key indicators are classified by type as project progress indicators, quality indicators, human efficiency indicators, testing process quality indicators, development process quality indicators, and project risk indicators.

[0008] The dynamic early warning module is used to combine historical data and current data status, obtain the corresponding key indicators and comprehensive evaluation indicators according to the different needs of each project, and generate early warning information according to the preset dynamic threshold; the comprehensive evaluation indicator is a combination of at least two of the key indicators.

[0009] The AI ​​analysis module is used to analyze the key indicator data of the project by calling the AI ​​agent, conduct a comprehensive performance evaluation, summarize the analysis and provide analytical suggestions; the comprehensive performance evaluation includes team performance evaluation, individual performance evaluation of developers and individual performance evaluation of testers.

[0010] Optionally, it also includes: a quality inspection module, used by quality engineers to evaluate, provide feedback on, and track improvements to standards during the agile cycle, wherein the standard evaluation affects the output of each assessment model in the comprehensive performance evaluation.

[0011] Optionally, the project schedule indicators include: Requirement Story Points (SP), Requirement Satisfaction Rate, and Schedule Deviation Rate; the personnel efficiency indicators include: Output Rate, Planned Task Saturation Rate, Actual Task Saturation Rate, Time Deviation Rate, and On-Time Task Completion Rate; the project quality indicators include: Legacy Defect Rate (DI) and Defect Escape Rate; the development process quality indicators include: Requirement Defect Density, Development Self-Test Pass Rate, Defect Activation Rate, and Average Defect Repair Time; the testing process quality indicators include: Average Defect Closure Time, Defect Contribution Score, Defect Effectiveness Rate, Defect Closure Rate, Test Case Coverage, Test Case Review Rate, and Number of Test Cases Executed; and the project risk indicators include: Project Avoidance Rate.

[0012] Optionally, the data input for the team performance evaluation includes the requirement fulfillment rate, output rate, team planned task saturation rate, team actual task saturation rate, work hour deviation, legacy defect rate ID, requirement defect density, development self-test pass rate, and maturity self-assessment.

[0013] Optionally, the data input for the individual performance evaluation of the developers includes story points, actual personal working hours consumed, available personal working hours, actual task saturation rate, R&D efficiency, output rate, number of story points satisfied, requirement defect density, defect activation rate, average defect repair time, development self-test pass rate, and team affiliation.

[0014] Optionally, the data input for the individual performance evaluation of the testers includes on-time task completion rate, schedule deviation rate, defect contribution score, defect effectiveness rate, defect closure rate, defect discovery rate, test case coverage rate, test case review rate, number of test cases executed, quality inspection score, residual defect rate (DI), defect escape rate, and average defect closure time.

[0015] Optionally, the test-related information includes requirements and schedule, task hours, project defects, test cases, test case review, test case execution, on-site issues, maturity self-assessment, and software release.

[0016] Optionally, the calculation rules of the team performance evaluation model include: weighted score calculation for individual indicator ranking, score calculation for month-on-month change, and comprehensive evaluation score; the comprehensive evaluation score is the weighted sum of the weighted score for individual indicator ranking and the score for month-on-month change.

[0017] Optionally, the calculation rules for both the developer's individual performance evaluation model and the tester's individual performance evaluation model include: weighted score calculation for individual indicator ranking, year-on-year change score calculation, and final score; the final score is the weighted sum of the weighted score for individual indicator ranking and the year-on-year change score.

[0018] Optionally, it also includes a feedback optimization module, which is used to push the corresponding indicator warning information to relevant personnel and continuously optimize the algorithm of the AI ​​analysis module based on the feedback from different relevant personnel.

[0019] The software development measurement data platform of the present invention: (1) Combines the characteristics of agile development, constructs a closed-loop feedback mechanism, and continuously optimizes the early warning rules and thresholds to ensure the long-term effectiveness and adaptability of the early warning system; (2) Combines performance evaluation, conducts comprehensive evaluation of team and individual performance through a more complete multi-dimensional project progress, human efficiency indicators, project quality, development process quality, testing process quality, as well as project risk management, agile maturity self-evaluation, etc., and adopts multi-dimensional methods such as multi-team horizontal indicator ranking and differentiated weighted scores to effectively motivate the team, thereby promoting product quality and reducing product development and usage costs.

[0020] Instruction manual illustrations

[0021] Figure 1 This is a schematic diagram of the structure of the software development measurement data platform in an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of the workflow of the software development measurement data platform in an embodiment of the present invention. Detailed Implementation

[0023] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0024] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0025] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0026] It should be noted that the terms "one" and "more" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0027] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0028] This invention provides an agile software development metrics data platform, which mainly consists of two parts: an indicator early warning unit and a personnel performance evaluation unit. The indicator early warning unit can be embedded in project management tools or integrated development environments to monitor changes in various indicators in real time. Once an indicator becomes abnormal, an early warning is immediately triggered, helping the team quickly locate problems and take countermeasures.

[0029] Specifically, refer to Figure 1 The indicator early warning unit includes: a data acquisition module, a message communication module, an indicator analysis module, a visualization display module, a dynamic early warning module, a quality inspection module, a feedback optimization module, and an AI analysis module.

[0030] P0: Synchronizes personnel and team-related data, including user needs and progress, task hours, project defects, test cases, test case reviews, test case execution, on-site issues, software releases, and other related information.

[0031] P1: Message communication module, which synchronizes data from third-party ZenTao systems, domain controller systems, MeterSphere systems, attendance systems, user requirements, and software release platforms.

[0032] P2: Data Acquisition Module: Captures relevant data of personnel and teams in real time during the agile development process, and synchronizes relevant information such as requirements and progress, task hours, project defects, test cases, test case reviews, test case execution, on-site issues, and agile maturity self-assessment data.

[0033] P3: Indicator Analysis Module: Based on preset baselines, thresholds, and rules, it performs statistical analysis, rule-based measurement, and in-depth analysis on the collected data to determine whether each indicator exceeds the normal range, generate preliminary early warning signals, and store them. It mainly includes project progress indicators, quality indicators, human efficiency indicators, testing and development process indicators, project risk indicators, and comprehensive evaluation indicators.

[0034] P4: Visual Display: Provides interactive dashboards that visually display tester performance overviews, indicator trends, and early warning information in chart form, facilitating self-assessment and improvement by management and testers.

[0035] P5: Dynamic Early Warning Module: Combining historical data trends and current data status, the AI ​​analysis module intelligently analyzes and predicts early warning signals to generate forward-looking early warning information.

[0036] P6: Quality Inspection Module: QA engineers evaluate the standards during the agile cycle, promptly report any issues to the agile team, and track the completion of improvements.

[0037] P7: Feedback Optimization Module: Pushes relevant indicator warning information to relevant personnel, analyzes the actual situation after the warning and provides risk handling solutions, continuously monitors indicators, and continuously optimizes warning thresholds and rules after the warning is eliminated to improve the accuracy and practicality of the warning.

[0038] P8: AI Analysis Module: Transmits the project's various indicator data, as well as the baseline and early warning data of each indicator, to the intelligent agent module for analysis and summarization, providing a reference for the agile team.

[0039] Reference Figure 2 The operational principle of the indicator early warning unit is as follows:

[0040] S1. Data Acquisition: The data acquisition module collects data from third-party systems such as ZenTao and the operations and maintenance system. This provides the team with a real-time, comprehensive, and high-quality data foundation for agile development, offering strong support for subsequent indicator analysis, dynamic early warning, visualization and reporting, and feedback optimization. The data acquisition module obtains real-time data through a scheduled Python script.

[0041] S2. Indicator Calculation and Analysis: The acquired data is statistically analyzed to determine whether each indicator exceeds the normal range, generating and storing preliminary early warning signals. Specifically, the definitions and rules of key indicators are explained in detail in Table 1.

[0042]

[0043]

[0044]

[0045] Table 1

[0046] Among them, the self-assessment of maturity...

[0047] S3, Dynamic Early Warning: Based on the results of indicator calculation and analysis, combined with real-time data and business rules, corresponding key indicators are obtained according to the different needs of each project. Early warning information is dynamically generated and released to assist the team in timely discovery and handling of potential problems to ensure the smooth progress of the project.

[0048] Specifically, this implementation provides examples of some indicator warning thresholds for illustrative purposes.

[0049] 1. Schedule Deviation Rate: When the schedule deviation rate exceeds 10%, a prompt "Schedule not meeting the target" will be given; when the schedule deviation rate exceeds 10% for two consecutive weeks, a warning "Signature seriously behind schedule and not meeting the target" will be given.

[0050] 2. Individual Productivity: When the individual productivity rate is below 98%, a "low productivity rate" warning will be issued. If the individual productivity rate is below 98% for two consecutive weeks, a warning will be issued indicating that the individual has not invested enough time within the iteration.

[0051] 3. Development self-test pass rate: When the development self-test pass rate is below 90%, a prompt "Development test quality is substandard" will be given; when the development self-test pass rate is below 90% for two consecutive weeks, a warning "Development test quality is seriously substandard" will be given.

[0052] 4. Demand Defect Density: If the demand defect density exceeds 0.5% for two consecutive weeks, a "Demand Defect Density Too High" warning will be triggered.

[0053] 5. Defect Activation Rate: If the defect activation rate exceeds 5% for two consecutive weeks, a "Defect Activation Too High" warning will be triggered.

[0054] 6. Average Defect Repair Time: Static threshold (average defect repair time exceeds the baseline value) and dynamic threshold (20% increase compared to the same time point in the previous iteration) provide risk warnings that the average defect repair time will exceed the limit.

[0055] 7. Residual Defect DI Value: Static threshold (residual defects exceeding the baseline value in the iteration) and dynamic threshold (20% increase compared to the same time point in the previous iteration) provide risk warnings that the residual defect DI value will exceed the limit.

[0056] 8. Defect Escape Rate: When the defect escape rate exceeds 5%, a "Quality Failure" warning will be given; when the on-site problem escape rate exceeds 5% for two consecutive months, a "Seriously Non-compliant Quality" warning will be given.

[0057] 9. Test Case Coverage: When the test case coverage is less than 95%, a warning will be given that "Test case coverage does not meet the standard"; when the test case coverage is less than 95% for two consecutive weeks, a warning will be given that "Quality does not meet the standard".

[0058] 10. Average Defect Closure Time: Static threshold (average defect closure time exceeds the baseline value) and dynamic threshold (20% increase compared to the same time point in the previous iteration) provide risk warnings that the average defect closure time will exceed the limit.

[0059] 11. Risk Aversion Rate: When the risk aversion rate exceeds 5%, a warning message will be given indicating "insufficient risk management capabilities"; when the risk aversion rate exceeds 5% for two consecutive months, an early warning message will be given indicating "whether project risks are being effectively managed".

[0060] In this embodiment, in addition to single indicator thresholds, a comprehensive indicator threshold is also included, wherein the comprehensive indicator is a combination of at least two single key indicators, which is illustrated in the following example:

[0061] "A warning is triggered when the schedule deviation rate rises above 10% for three consecutive days and the demand fulfillment rate is below 60%."

[0062] If the defect activation rate is higher than 5% and the development self-test pass rate is lower than 90%, a warning of "Development test quality seriously substandard" will be triggered.

[0063] When the defect density exceeds 0.5% and the average defect repair time exceeds the baseline value by 20%, an alert for "poor development quality and slow defect repair" is triggered.

[0064] When the defect escape rate is higher than 5% and the residual defect DI is higher than 10%, a "serious quality non-compliance" warning is triggered.

[0065] The dynamic early warning module continuously receives and processes key performance indicator data provided by the indicator analysis module. When an indicator deviates from the normal range or reaches a preset threshold, an early warning event is immediately triggered. When a non-compliance occurs, the system automatically displays the early warning information of the indicator through a visual FineReport report.

[0066] The system automatically pushes information to relevant project team members, product managers, and administrators through various means such as email and instant messaging tools to ensure timely delivery of information.

[0067] The dynamic early warning module is closely integrated with the project management process. When an early warning occurs, the system automatically initiates the corresponding problem-solving process, such as holding an ad-hoc meeting to discuss countermeasures or adjusting the iteration plan to eliminate risks.

[0068] Meanwhile, the metrics are dynamically stored. These monitored metrics are stored in a metric library, and key metrics that need to be monitored can be dynamically retrieved according to the characteristics of the project. For example: 1. If Team 1 is currently only in the R&D stage and has not been put into field use, then the key metrics are only legacy defects (DI), test case execution rate, and personnel productivity rate; 2. If Team 2 is currently in field use, then the key metrics are legacy defects (DI), test case execution rate, personnel productivity rate, and defect escape rate.

[0069] S4, AI intelligent agent analysis, receives various indicator information from the dynamic early warning module, as well as information on indicators that do not meet the standards, and performs analysis and comprehensive performance evaluation and action suggestions.

[0070] It mainly consists of AI model and API interface configuration, agent key, temperature value, output format, prompt word information configuration, and by calling the AI ​​intelligent evaluator, it evaluates and analyzes the project's various indicator data and scores, dynamic early warning information, and historical trend data of key indicators (demand fulfillment rate, legacy DI value, defect activation rate, and development self-test pass rate) through the AI ​​big model, and provides analysis suggestions.

[0071] S5, Feedback Optimization, is primarily responsible for processing and analyzing feedback on early warning results. Based on various project metrics, especially early warning data, when there are disagreements regarding metric values, developers, testers, project managers, and supervisors confirm and provide feedback on the early warning results. The dynamic early warning device for test metrics continuously upgrades and optimizes its algorithm by collecting feedback from all levels of personnel, enabling it to return more accurate early warning information. Simultaneously, developers, testers, project managers, and supervisors review the causes of abnormal metrics, uncovering deeper reasons for continuous optimization in subsequent agile development work.

[0072] S6. Visualization: Project indicator data and early warning information are presented in a graphical interface. Specifically, this includes highlighting early warning indicators in the project management platform or using hierarchical color coding to indicate early warning levels in the visualization dashboard. This enables intuitive visualization of project status information, making it easier for team members to quickly identify and understand the overall project operation status.

[0073] As a preferred embodiment, the visualization module is implemented based on the FineReport tool. Its specific implementation includes: dynamically displaying the performance indicator data of each tester; automatically triggering an early warning mechanism when a specific indicator value exceeds a preset threshold, highlighting the abnormal indicator in red, and generating a visual early warning prompt. This technical solution effectively improves the real-time monitoring capabilities and anomaly identification efficiency of project management.

[0074] Specifically, in this embodiment, the comprehensive performance evaluation includes three aspects: team performance evaluation, individual performance evaluation of developers, and individual performance evaluation of testers.

[0075] The data input for team performance evaluation includes project ID, requirement fulfillment rate, output rate, team planned task saturation rate, team actual task saturation rate, work hour deviation, legacy defect rate ID, requirement defect density, development self-test pass rate, defect escape rate, and maturity self-assessment. The evaluation method is as follows:

[0076] (1) Calculation of weighted scores for ranking of individual indicators.

[0077] Suppose there are N teams, and for each metric x, team i has a value of xi. Define the ranking function:

[0078]

[0079] Let $v_{i,j}$ be the observed value of the $i$-th indicator for the $j$-th team.

[0080] Calculate ranking ratio

[0081]

[0082] Four scoring levels (10, 7, 4, 1)

[0083]

[0084] Weighted score formula:

[0085] Score1 = S1 (output rate) × 0.35 + S1 (demand fulfillment rate) × 0.15

[0086] +S1 (Team Planned Task Saturation Rate) × 0.05 + S1 (Team Actual Task Saturation Rate) × 0.05 + S1 (Work Hour Deviation Rate) × 0.05 + S1 (Development Self-Test Pass Rate) × 0.05

[0087] +S1(Residual Defect D1)×0.05+S1(Demand Defect Density)×0.10

[0088] +S1 (maturity) × 0.05 + S1 (schedule deviation rate) × 0.05

[0089] +S1 (defect escape rate) × 0.10

[0090] (2) Calculation of month-on-month change score.

[0091] Define the rate of change:

[0092]

[0093] For positive metrics (output rate, demand fulfillment rate, ... development self-test pass rate, a total of 6 items):

[0094]

[0095] For negative indicators (defect-related):

[0096]

[0097] Risk aversion rate calculation method:

[0098]

[0099] Weighted score formula:

[0100] Score2 = S2 (output rate) × 0.15 + S2 (demand fulfillment rate) × 0.10

[0101] +S2(Team planned task saturation rate) × 0.05 +S2(Team actual task saturation rate) × 0.05

[0102] +S2 (Work Hour Deviation Rate) × 0.05 + S2 (Development Self-Test Pass Rate) × 0.10

[0103] +S2(Residual Defect D1)×0.05+S2(Demand Defect Density)×0.05

[0104] +S2 (maturity) × 0.10 +S2 (schedule deviation rate) × 0.10

[0105] +S2 (Defect Escape Rate) × 0.10 +S2 (Risk Aversion Rate) × 0.05

[0106] (3) Overall evaluation score final = 0.7 × Score1 + 0.3 × Score2.

[0107] The data input for individual developer performance evaluation includes employee ID, team member names, story points, actual work hours consumed, available work hours, actual task saturation rate, development efficiency, output rate, satisfied story points, requirement defect density, defect activation rate, average defect fix time, development self-test pass rate, and team affiliation. The specific evaluation method is as follows:

[0108] (1) Calculation of weighted scores for individual indicator ranking. Define the ranking of employee $j$ on indicator $i$:

[0109] Where $N$ is the total number of samples, and $\operatorname{rank}(x)$ is...

[0110] Ranking in ascending order. Assign scores based on four tiers according to $p(i,j)$:

[0111]

[0112] Calculate the weighted score $S_1(j)$ for the 11 indicators according to their weights:

[0113] S1(j)=0.05s(1,j)+0.10s(2,j)+0.05s(3,j)+0.15s(4,j)+0.10s(5,j)+0.1 0s(6,j)+0.05s(7,j)+0.10s(8,j)+0.10s(9,j)+0.15s(10,j)+0.05s(11,j)

[0114] (2) Calculation method for year-on-year change score. Definition of change rate:

[0115] Where $\bar x_i$ is the individual's average value of this indicator over the past year.

[0116] Positive indicators:

[0117]

[0118] Negative indicators:

[0119]

[0120] Then, summing all the year-on-year scores yields $S_2(j)$:

[0121]

[0122] (3) Individual final score:

[0123] F(i) = 0.6S1(j) + 0.4S2(j)

[0124] The data input for individual performance evaluation of testers includes employee ID, team member names, on-time task completion rate, schedule deviation rate, defect contribution score, defect effectiveness rate, defect closure rate, defect discovery rate, test case coverage rate, test case review rate, number of test cases executed, quality inspection score, legacy defect rate (DI), defect escape rate, average defect closure time, and team affiliation. The specific scoring rules are as follows:

[0125] (1) Weighted score calculation for single indicator ranking. Let there be N samples for the j-th indicator in the team, and the original value of individual i for this indicator be $X_{i,j}$. Its ranking (from smallest to largest) among all samples is:

[0126] R i，j =#(k|X k，j ≤Xi，j}

[0127] Four scoring levels (10, 7, 4, 1):

[0128]

[0129] Ten-level scoring (1–10):

[0130]

[0131] The scores of each indicator are summed according to their weights, and this sum is recorded as the total score for the i-th person.

[0132]

[0133] (2) Calculation method for year-on-year change score. The individual average of the j-th indicator over the past year is $\bar X_{i,j}$, and the value in the current period is still $X_{i,j}$, then the relative change rate is.

[0134]

[0135] Positive metrics: (On-time task completion rate, defect contribution score, defect effectiveness rate, defect closure rate, defect discovery rate, test case coverage, test case review rate, number of test cases executed, quality inspection score)

[0136]

[0137] Negative metrics: (Schedule Deviation Rate, Defect Defect Rate (DI), Defect Escape Rate, Average Defect Closure Time)

[0138]

[0139] Calculate the average (or summation) of all comparative indicators:

[0140]

[0141] (3) Individual final score:

[0142] Specifically, the software development metrics data platform in this embodiment also includes a quality inspection module. This module ensures the accuracy and fairness of various metrics and avoids distortion by checking the data, task logs, work hour reports, defects, and other work standards in ZenTao during the agile process. If problems are found, they are corrected and rectified in a timely manner. At the same time, the team and individuals are scored, with 0.5 points deducted for each item, and a maximum deduction of 2 points for the same type of problem, with a maximum deduction of 10 points.

[0143] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. An agile software development metrics data platform, characterized in that... include: The data acquisition module is used to collect data from third-party systems, capture relevant data of personnel and teams in real time during the agile development process, and synchronize test-related information. The indicator analysis module includes multiple preset key indicators, indicator calculation rules, and indicator thresholds. It is used to statistically analyze the acquired data, determine whether each indicator exceeds the normal range, generate preliminary early warning signals, and store them. The key indicators are classified by type as project progress indicators, quality indicators, human efficiency indicators, testing process quality indicators, development process quality indicators, and project risk indicators. The dynamic early warning module is used to combine historical data and current data status, obtain the corresponding key indicators and comprehensive evaluation indicators according to the different needs of each project, and generate early warning information according to the preset dynamic threshold; the comprehensive evaluation indicator is a combination of at least two of the key indicators. The AI ​​analysis module is used to analyze the key indicator data of the project by calling the AI ​​agent, conduct a comprehensive performance evaluation, summarize the analysis and provide analytical suggestions; the comprehensive performance evaluation includes team performance evaluation, individual performance evaluation of developers and individual performance evaluation of testers.

2. The agile software development metric data platform as described in claim 1, characterized in that... Also includes: The quality inspection module is used by quality engineers to evaluate, provide feedback on, and track improvements to standards during the agile cycle. The evaluation of standards affects the output of each assessment model in the comprehensive performance evaluation.

3. The agile software development metrics data platform as described in claim 1, characterized in that... The project schedule indicators include: Requirement Story Points (SP), Requirement Satisfaction Rate, and Schedule Deviation Rate; the personnel efficiency indicators include: Output Rate, Planned Task Saturation Rate, Actual Task Saturation Rate, Time Deviation Rate, and On-Time Task Completion Rate; the project quality indicators include: Legacy Defect Rate (DI) and Defect Escape Rate; the development process quality indicators include: Requirement Defect Density, Development Self-Test Pass Rate, Defect Activation Rate, and Average Defect Repair Time; the testing process quality indicators include: Average Defect Closure Time, Defect Contribution Score, Defect Effectiveness Rate, Defect Closure Rate, Test Case Coverage, Test Case Review Rate, and Number of Test Cases Executed; the project risk indicators include: Project Avoidance Rate.

4. The agile software development metric data platform as described in claim 3, characterized in that... The data input for the team performance evaluation includes the requirement fulfillment rate, output rate, team planned task saturation rate, team actual task saturation rate, work hour deviation, legacy defect rate ID, requirement defect density, development self-test pass rate, and maturity self-assessment.

5. The agile software development metrics data platform as described in claim 3, characterized in that... The data input for the individual performance evaluation of the developers includes story points, actual personal working hours consumed, available personal working hours, actual task saturation rate, R&D efficiency, output rate, number of story points satisfied, requirement defect density, defect activation rate, average defect repair time, development self-test pass rate, and team affiliation.

6. The agile software development metrics data platform as described in claim 3, characterized in that... The data input for the individual performance evaluation of the testers includes on-time task completion rate, schedule deviation rate, defect contribution score, defect effectiveness rate, defect closure rate, defect discovery rate, test case coverage rate, test case review rate, number of test cases executed, quality inspection score, residual defect rate (DI), defect escape rate, and average defect closure time.

7. The agile software development measurement data platform as described in claim 1, characterized in that... The test-related information includes requirements and schedule, task hours, project defects, test cases, test case review, test case execution, on-site issues, maturity self-assessment, and software release.

8. The agile software development metric data platform as described in claim 4, characterized in that... The calculation rules of the team performance evaluation model include: weighted score calculation for individual indicator ranking, score calculation for month-on-month change, and comprehensive evaluation score; the comprehensive evaluation score is the weighted sum of the weighted score for individual indicator ranking and the score for month-on-month change.

9. The agile software development metrics data platform as described in claim 5, characterized in that... The calculation rules for both the developer's individual performance evaluation model and the tester's individual performance evaluation model include: weighted score calculation for individual indicator ranking, year-on-year change score calculation, and final score; the final score is the weighted sum of the individual indicator ranking weighted score and the year-on-year change score.

10. The agile software development metric data platform as described in claim 1, characterized in that... It also includes a feedback optimization module, which is used to push relevant indicator warning information to relevant personnel and continuously optimize the algorithm of the AI ​​analysis module based on the feedback from different relevant personnel.

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