Software development management system

Abstract

The invention relates to the technical field of software development, and discloses a software development management system. Comprising a project planning stage, an AI intelligent demand analysis and dynamic planning engine introduction stage, a team establishment and task allocation stage, a talent skill map and intelligent task matching platform establishment stage, a software development stage, a low-code development framework and AI auxiliary code review adopting stage and a software test stage. According to the software development management system, an AI intelligent demand analysis and dynamic planning engine is introduced, the AI intelligent demand analysis accelerates demand understanding and planning making, a low-code development framework enables developers to rapidly set up software functions, AI auxiliary code review and automatic defect prediction can find and solve problems and potential risks in codes in advance, and the development efficiency is improved. The comprehensive and highly-targeted intelligent test case generation ensures the coverage rate and effectiveness of software testing and reduces software vulnerabilities, so that the effects of improving project management efficiency, optimizing task allocation and ensuring code quality can be achieved.

Description

Technical Field

[0001] This invention relates to the field of software development technology, specifically to a software development management system. Background Technology

[0002] As digital transformation deepens across industries, the scale and complexity of software systems are growing explosively. Taking the core trading system in the financial industry as an example, it not only needs to handle massive amounts of real-time trading data, but also needs to meet high concurrency, low latency, and strict security and compliance requirements, involving complex algorithms, encryption technologies, and interactions with various external systems.

[0003] Traditional software development management relies mainly on human experience and simple documentation. In terms of project progress management, it is often tracked through regular meetings and manually filled progress reports. Information updates are lagging and cannot reflect the actual progress of the project in a timely and accurate manner, which often leads to project delays. In terms of team collaboration, communication between members mainly relies on email, instant messaging tools, etc. Information is scattered and lacks effective integration, which can easily lead to problems such as poor communication, duplicate tasks, or omissions. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] To address the shortcomings of existing technologies, this invention provides a software development and management system that has advantages such as improving project management efficiency, optimizing task allocation, and ensuring code quality, thus solving the problems mentioned in the background section.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, the present invention provides the following technical solution: a software development management system, comprising a project planning phase, introducing an AI-powered intelligent requirements analysis and dynamic planning engine, a team building and task allocation phase, building a talent skills map and an intelligent task matching platform, a software development phase, adopting a low-code development framework and AI-assisted code review, a software testing phase, implementing big data-based intelligent test case generation and automated defect prediction, a software deployment and launch phase, automating deployment using containerization technology and canary release strategies, a project maintenance and continuous improvement phase, and building a user behavior data analysis platform and agile improvement processes. The project planning phase includes requirements collection and analysis and project plan formulation; the team building and task allocation phase includes team member recruitment and selection and task allocation; the software development phase includes system design, code development, and code review; the software testing phase includes test plan formulation, test execution, defect repair, and regression testing; the software deployment and launch phase includes deployment environment preparation, software deployment, and launch; and the project maintenance and continuous improvement phase includes software maintenance, project evaluation and summary, and continuous improvement.

[0008] Preferably, the step of introducing AI intelligent demand analysis and dynamic planning engine utilizes natural language processing and machine learning technologies. The AI ​​intelligent demand analysis tool can quickly read and understand demand documents collected from various channels, extract key information, automatically generate demand analysis reports, and predict potential demands by comparing with historical project data. When formulating project plans, the dynamic planning engine adjusts the project plan in real time according to the project's progress, resource changes, and market environment changes to ensure that the plan is always reasonable and feasible.

[0009] It can quickly process massive amounts of demand information, greatly improving analysis efficiency. Moreover, algorithm-based analysis is more accurate than manual analysis, which can uncover potential demands. At the same time, dynamic planning can respond to changes in a timely manner, ensuring that project plans always keep up with reality, avoiding the omission and misunderstanding of requirements, providing a clear and accurate demand direction for project development, reducing cost increases and schedule delays caused by demand changes and unreasonable plans, and improving the project success rate.

[0010] Preferably, the step of building a talent skills map and intelligent task matching platform involves collecting and organizing data such as team members' past project experience, skill level, and training experience to construct a talent skills map, which intuitively displays the strengths of each member. The intelligent task matching platform automatically matches the most suitable team members for each task based on factors such as the technical requirements, difficulty level, and time constraints of the project task, combined with the talent skills map.

[0011] Clearly present team members' skills, achieve precise matching of people and positions, improve employee work efficiency and satisfaction, reduce resource waste caused by improper task allocation, give full play to the strengths of team members, enhance the overall combat effectiveness of the team, promote the efficient progress of projects, optimize human resource allocation, and improve the level of enterprise talent management.

[0012] Preferably, in the step of using a low-code development framework and AI-assisted code review, the low-code development framework allows developers to quickly build software functional modules through a visual interface and a small amount of code, reducing development difficulty and workload, and speeding up development. The AI-assisted code review tool uses deep learning algorithms to perform multi-dimensional checks on the code, including code style, potential vulnerabilities, performance optimization, etc., and automatically provides review opinions and improvement suggestions.

[0013] Low-code development lowers the development threshold and workload, shortens the development cycle, and enables rapid response to market demands. AI-assisted code review comprehensively checks the code, improves code quality, reduces defects, lowers software development costs, increases development efficiency, enhances software stability and security, reduces later maintenance costs, and improves software competitiveness.

[0014] Preferably, the implementation of the big data-based intelligent test case generation and automated defect prediction steps utilizes a big data-based intelligent test case generation tool. By analyzing the software's functional characteristics, historical test data, and testing experience from similar projects, it automatically generates comprehensive and targeted test cases. The automated defect prediction uses machine learning algorithms to analyze the data generated during the testing process in real time, predicting the location and type of potential software defects and alerting testers and developers in advance.

[0015] Intelligent generation of comprehensive and targeted test cases improves test coverage, automated defect prediction helps identify potential problems in advance, reduces repair costs, more comprehensively detects software issues, improves software quality, reduces post-launch failures and user complaints, and ensures a better user experience.

[0016] Preferably, the step of using containerization technology and automated canary release strategy involves packaging the software and its dependencies into an independent container to achieve environment-independent deployment, thereby improving the flexibility and efficiency of deployment. The automated canary release strategy uses automated tools to gradually push the new version of the software to some users according to predetermined rules and proportions, monitors user feedback and software running status in real time, and decides whether to continue to expand the release scope based on the feedback.

[0017] Containerization enables environment-independent deployment, improving deployment flexibility and efficiency. It automates canary releases to reduce risks, allows for timely adjustments based on user feedback, simplifies the deployment process, reduces deployment time and workload, minimizes the impact of software launch on users, and ensures stable software deployment.

[0018] Preferably, the steps of building a user behavior data analysis platform and an agile improvement process involve the user behavior data analysis platform collecting user operation data in real time during the use of the software, such as click frequency, usage duration, and function usage sequence. Through data analysis, user needs and software problems are identified. Based on the data analysis results, the agile improvement process quickly adjusts software functions and optimizes user experience to achieve continuous software iteration.

[0019] By gaining a deep understanding of user needs and usage habits, identifying software issues, and implementing agile process improvements for rapid response, we can enhance user experience, increase user satisfaction and loyalty, continuously optimize the software, adapt to market changes, and improve its market competitiveness.

[0020] Compared with the prior art, the present invention provides a software development and management system, which has the following beneficial effects:

[0021] 1. This software development management system introduces an AI-powered intelligent requirements analysis and dynamic planning engine. AI-powered intelligent requirements analysis accelerates requirements understanding and planning, while the low-code development framework allows developers to quickly build software functions. AI-assisted code review and automated defect prediction can identify and resolve problems and potential risks in the code in advance, and comprehensive and targeted intelligent test case generation ensures the coverage and effectiveness of software testing, reducing software vulnerabilities. As a result, it can improve project management efficiency, optimize task allocation, and ensure code quality. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the overall framework of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Please see Figure 1 A software development management system includes the following phases: project planning, introducing AI-powered intelligent requirements analysis and dynamic programming engine, team building and task allocation, building a talent skills map and intelligent task matching platform, software development, adopting a low-code development framework and AI-assisted code review, software testing, implementing big data-based intelligent test case generation and automated defect prediction, software deployment and launch, automating deployment using containerization technology and canary release strategies, project maintenance and continuous improvement, and building a user behavior data analysis platform and agile improvement processes.

[0025] The project planning phase includes requirements gathering and analysis, and project plan development. This is the starting point for software development management. System administrators or project leaders need to communicate with clients, business departments, and other relevant stakeholders to gather their expectations and requirements regarding the software's functionality, performance, interface, etc. Detailed requirements information is obtained through various methods, such as interviews, questionnaires, and workshops. This information is then organized, analyzed, and summarized to form a clear requirements document. This document serves as the foundation for subsequent software development, clarifying the software's goals and functional scope. Based on the results of the requirements analysis, a comprehensive project plan is developed. This includes determining the various phases of the project (such as requirements analysis, design, development, testing, and deployment) and their timelines, estimating the resources (human, material, and financial) required for each phase, and assigning roles to project team members. The project manager is responsible for defining project milestones and deliverables to effectively monitor and evaluate project progress. The project plan should also consider potential risks and develop corresponding countermeasures. A comprehensive and accurate understanding of customer and business needs is crucial to avoid rework and changes due to unclear requirements during development. Potential requirement conflicts and problems should be identified in advance to reduce project risks. Clear goals and directions should be provided for subsequent design, development, and testing to ensure the software product meets actual user needs and improves user satisfaction. Project schedules, resources, and costs should be rationally allocated to improve project controllability and predictability. Clear project milestones and deliverables facilitate monitoring and evaluation, provide clear work guidance to the project team, ensure team members understand their tasks and timelines, coordinate resources, and ensure the project proceeds smoothly as planned.

[0026] The team building and task allocation phase includes team member recruitment and selection, and task allocation. Based on the project's needs and plans, the required professionals, such as software engineers, testers, designers, and project managers, are identified. Through recruitment channels, individuals with the necessary skills and experience are screened and selected to join the project team. During the selection process, not only the candidate's professional abilities but also their teamwork and communication skills are considered. The project manager, based on the project plan and the team members' skills, experience, and workload, breaks down the project tasks into specific sub-tasks and assigns them to suitable team members. Each task should have clearly defined objectives, requirements, delivery time, and quality standards, while ensuring... The reasonableness and balance of task allocation should be ensured to avoid situations where some members have too heavy a workload while others have too light a workload. A team with professional skills and experience should be formed to provide human resource support for the successful implementation of the project. Members with good teamwork spirit should be selected to promote efficient team operation. It should be ensured that team members possess the necessary abilities and qualities to complete project tasks, thereby improving the overall combat effectiveness and work efficiency of the team. Tasks should be reasonably allocated according to the skills and strengths of team members to improve work efficiency and quality. The responsibilities and tasks of each member should be clearly defined to avoid unclear responsibilities and shirking of responsibility. This will allow team members to focus on their areas of expertise, fully utilize their personal strengths, and promote collaboration and communication among team members.

[0027] The software development phase includes system design, code development, and code review. Software designers design the system based on the requirements document, including architecture design, database design, and user interface design. Architecture design determines the overall structure and module division of the software, defining the interfaces and interaction methods between modules. Database design plans the data storage structure and relationships. User interface design designs the layout and style of the interface for user interaction with the software. The system design document provides detailed guidance for subsequent development work. Developers write code based on the system design document, using appropriate programming languages ​​and development tools, and implementing the various functional modules of the software according to coding standards and design requirements. During development, attention should be paid to code quality and maintainability, employing appropriate design patterns and programming techniques to ensure code readability and scalability. Unit testing is also essential, with each module tested independently to ensure its correct functionality. After code development is completed, the team conducts a code review, with reviewers assessing the code's structure, logic, performance, security, and other aspects. Code review involves conducting thorough checks to identify and correct problems and potential risks in the code. It improves code quality, reduces later maintenance workload, helps design a reasonable software architecture and database structure, enhances maintainability, scalability, and performance, proactively considers software compatibility and security, lowers later maintenance costs, provides developers with detailed design blueprints to guide code development, ensures the overall quality and stability of the software system, implements various software functions according to design requirements, guarantees software correctness and reliability, adopts standardized coding styles and development processes to improve code readability and maintainability, transforms design schemes into actual software products, provides users with the required functions and services, identifies potential problems and defects in the code for timely repair, improves code quality and security, promotes technical exchange and learning among team members, enhances the overall technical level of the team, ensures code conforms to coding standards and design requirements, reduces the probability of software failures during operation, and improves software stability and reliability.

[0028] The software testing phase includes test planning, test execution, defect fixing, and regression testing. Testers develop a test plan based on the requirements document and project plan. The test plan includes the scope of testing, testing methods, testing schedule, and resource requirements. It determines the types of tests to be performed, such as functional testing, performance testing, security testing, and compatibility testing, and develops corresponding test cases. The software is tested according to the test plan and test cases. During testing, defects and problems found are recorded, including defect descriptions, reproduction steps, and severity. Testers must communicate with developers promptly, providing feedback on test results and assisting developers in locating and resolving problems. Developers modify and fix the code based on the defect information provided by the testers. After the fixes are completed, testers perform regression testing on the fixed parts to ensure that the defects have been resolved and no new problems have been introduced. This process may... The testing process is repeated until the software meets the predetermined quality standards. The scope, methods, and schedule of testing are clearly defined. Testing resources are allocated rationally to improve testing efficiency and effectiveness, ensuring the comprehensiveness and systematic nature of the testing work. Important test scenarios are avoided, and detailed testing guidance is provided to testers to ensure that the testing work is systematic and effective. Defects and problems in the software are discovered and promptly reported to developers for fixing, improving software quality. Comprehensive testing of the software's functionality, performance, and compatibility is conducted to ensure that the software meets user needs and expectations. Potential problems are identified and resolved before software launch, reducing post-release risks and increasing user trust. Defects are promptly fixed to ensure normal software operation. Regression testing verifies the effectiveness of defect fixes, avoiding the introduction of new problems and ensuring that the software remains stable and reliable after defect fixes, thus improving software quality and user experience.

[0029] The software deployment and launch phase includes deployment environment preparation, software deployment, and release. Before the software is officially launched, the deployment environment needs to be prepared, including server configuration, network setup, database installation and configuration, etc., ensuring that the deployment environment matches the software's operational requirements and has sufficient performance and stability. The tested software is then deployed to the production environment, which may involve copying code to the server, configuring the application, initializing the database, etc. During deployment, necessary testing and verification must be performed to ensure the software runs correctly in the production environment. After the software deployment is complete and passes verification, it is released online, relevant users are notified that the software is officially available, and necessary training is provided. In addition to providing support, a monitoring mechanism must be established to monitor the software's operational status and performance in real time, promptly identify and address potential problems, build a stable and reliable deployment environment to ensure the software runs normally, consider the scalability and compatibility of the environment in advance to provide guarantees for subsequent software upgrades and optimizations, provide necessary infrastructure support for software deployment and launch, ensure the stability and reliability of the software in the production environment, successfully deploy the tested software to the production environment, achieve the official launch of the software, adopt a scientific deployment strategy to reduce deployment risks and minimize the impact on users, enable the software to provide services to users in a timely manner, realize the commercial value of the software, officially launch the software product to users, and attract users to use it. Timely collection of user feedback and opinions provides a basis for subsequent software improvements, increases the software's awareness and market share, and promotes the promotion and application of the software.

[0030] The project maintenance and continuous improvement phase includes software maintenance, project evaluation and summary, and continuous improvement. After the software goes live, continuous maintenance is required. This includes fixing new problems that arise during operation, optimizing software performance, and expanding software functionality based on user feedback and business needs. Maintenance work must respond promptly to user needs to ensure the normal operation and continuous availability of the software. After a period of time following project completion, a comprehensive evaluation and summary is conducted to analyze the project's performance, including indicators such as project progress, quality, and cost. Lessons learned are summarized to provide reference for future projects. User feedback is also collected to understand user satisfaction and suggestions for improvement, providing a basis for continuous software improvement. Based on the project evaluation and user feedback results, a continuous improvement plan is developed to continuously optimize and upgrade the software. This involves improving software performance, functionality, and user experience, optimizing and improving software development management processes, enhancing project management efficiency and quality, promptly resolving issues arising during software operation, ensuring normal software use, optimizing software performance and expanding functionality to improve software competitiveness, extend software lifespan, increase user satisfaction and loyalty, summarizing project lessons learned to provide reference for future projects, evaluating project performance and results, providing reasonable rewards and incentives for team members, continuously improving project management level and software development capabilities, promoting team growth and development, continuously optimizing and upgrading software based on user feedback and market demands to maintain software competitiveness, continuously improving software development management processes, enhancing project quality and efficiency, enabling software to adapt to the ever-changing market environment and user needs, and achieving sustainable development.

[0031] The introduction of an AI-powered intelligent requirements analysis and dynamic planning engine utilizes natural language processing and machine learning technologies. The AI-powered intelligent requirements analysis tool can quickly read and understand requirements documents collected from various channels, extract key information, automatically generate requirements analysis reports, and predict potential requirements by comparing them with historical project data. During project planning, the dynamic planning engine adjusts the project plan in real time based on the project's progress, resource changes, and market environment changes to ensure that the plan is always reasonable and feasible.

[0032] The above technical solutions enable the rapid processing of massive amounts of demand information, significantly improving analysis efficiency. Algorithm-based analysis is more accurate than manual analysis, uncovering potential demands. Meanwhile, dynamic planning can respond to changes in a timely manner, ensuring that project plans always align with reality, avoiding omissions and misunderstandings of requirements, providing a clear and accurate direction for project development, reducing cost increases and schedule delays caused by changes in requirements and unreasonable plans, and improving the project success rate.

[0033] The steps to build a talent skills map and intelligent task matching platform are as follows: By collecting and organizing data such as team members' past project experience, skill level, and training experience, a talent skills map is constructed to intuitively display the strengths of each member. The intelligent task matching platform automatically matches the most suitable team members for each task based on factors such as the technical requirements, difficulty level, and time constraints of the project task, combined with the talent skills map.

[0034] The above technical solutions clearly present the skills of team members, achieve precise matching of people and positions, improve employee work efficiency and satisfaction, reduce resource waste caused by improper task allocation, give full play to the strengths of team members, enhance the overall combat effectiveness of the team, promote the efficient progress of projects, optimize human resource allocation, and improve the level of enterprise talent management.

[0035] The low-code development framework allows developers to quickly build software functional modules with a visual interface and a small amount of code, reducing development difficulty and workload and speeding up development. The AI-assisted code review tool uses deep learning algorithms to perform multi-dimensional checks on the code, including code style, potential vulnerabilities, performance optimization, etc., and automatically provides review opinions and improvement suggestions.

[0036] Through the above technical solutions, low-code development lowers the development threshold and workload, shortens the development cycle, and enables rapid response to market demands. AI-assisted code review comprehensively checks the code, improves code quality, reduces defects, lowers software development costs, increases development efficiency, enhances software stability and security, reduces later maintenance costs, and improves software competitiveness.

[0037] Implementing intelligent test case generation and automated defect prediction based on big data: The intelligent test case generation tool based on big data automatically generates comprehensive and targeted test cases by analyzing the software's functional characteristics, historical test data, and testing experience from similar projects. Automated defect prediction uses machine learning algorithms to analyze the data generated during the testing process in real time, predict the location and type of defects that may occur in the software, and alert testers and developers in advance.

[0038] Through the above technical solutions, comprehensive and targeted test cases are generated intelligently, improving test coverage, automatically predicting defects to discover potential problems in advance, reducing repair costs, more comprehensively detecting software issues, improving software quality, reducing software failures and user complaints after launch, and ensuring user experience.

[0039] By leveraging containerization technology and automating the canary release strategy, containerization technology packages the software and its dependencies into an independent container, enabling environment-independent deployment and improving deployment flexibility and efficiency. The automated canary release strategy uses automated tools to gradually push new versions of the software to a subset of users according to predetermined rules and proportions, while monitoring user feedback and software running status in real time, and deciding whether to continue expanding the release scope based on the feedback.

[0040] Through the above technical solutions, containerization enables environment-independent deployment, improves deployment flexibility and efficiency, automates canary releases to reduce risks, allows for timely adjustments based on user feedback, simplifies the deployment process, reduces deployment time and workload, minimizes the impact of software launch on users, and ensures stable software launch.

[0041] The steps for building a user behavior data analysis platform and agile improvement processes are as follows: The user behavior data analysis platform collects user operation data in real time during software use, such as click frequency, usage duration, and function usage sequence. Through data analysis, user needs and software problems are identified. The agile improvement process quickly adjusts software functions and optimizes user experience based on the data analysis results, enabling continuous software iteration.

[0042] By using the above technical solutions, we can gain a deeper understanding of user needs and usage habits, identify software issues, improve processes quickly and agilely, enhance user experience, increase user satisfaction and loyalty, continuously optimize the software, adapt to market changes, and improve the software's market competitiveness.

[0043] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A software development management system, comprising: a project planning phase; introducing an AI-powered intelligent requirements analysis and dynamic programming engine; a team building and task allocation phase; building a talent skills map and intelligent task matching platform; a software development phase; adopting a low-code development framework and AI-assisted code review; a software testing phase; implementing big data-based intelligent test case generation and automated defect prediction; a software deployment and launch phase; automating deployment using containerization technology and canary release strategies; a project maintenance and continuous improvement phase; and building a user behavior data analysis platform and agile improvement processes, characterized by: The project planning phase includes requirements gathering and analysis and project plan formulation; the team building and task allocation phase includes team member recruitment and selection and task allocation; the software development phase includes system design, code development and code review; the software testing phase includes test plan formulation, test execution, defect repair and regression testing; the software deployment and launch phase includes deployment environment preparation, software deployment and launch; and the project maintenance and continuous improvement phase includes software maintenance, project evaluation and summary and continuous improvement.

2. The software development management system according to claim 1, characterized in that: The step of introducing AI-powered intelligent requirements analysis and dynamic planning engine utilizes natural language processing and machine learning technologies. The AI-powered intelligent requirements analysis tool can quickly read and understand requirements documents collected from various channels, extract key information, automatically generate requirements analysis reports, and predict potential requirements by comparing with historical project data. When formulating project plans, the dynamic planning engine adjusts the project plan in real time based on the project's progress, resource changes, and market environment changes to ensure that the plan is always reasonable and feasible.

3. The software development management system according to claim 1, characterized in that: The steps of building a talent skills map and intelligent task matching platform involve collecting and organizing data such as team members' past project experience, skill level, and training experience to construct a talent skills map, which intuitively displays the strengths of each member. The intelligent task matching platform automatically matches the most suitable team members for each task based on factors such as the technical requirements, difficulty level, and time constraints of the project task, combined with the talent skills map.

4. The software development management system according to claim 1, characterized in that: The low-code development framework and AI-assisted code review process allow developers to quickly build software functional modules through a visual interface and a small amount of code, reducing development difficulty and workload and accelerating development speed. The AI-assisted code review tool uses deep learning algorithms to perform multi-dimensional checks on the code, including code style, potential vulnerabilities, performance optimization, etc., and automatically provides review opinions and improvement suggestions.

5. The software development management system according to claim 1, characterized in that: The implementation steps of intelligent test case generation and automated defect prediction based on big data utilize a big data-based intelligent test case generation tool. By analyzing the software's functional characteristics, historical test data, and testing experience from similar projects, it automatically generates comprehensive and targeted test cases. The automated defect prediction uses machine learning algorithms to analyze the data generated during the testing process in real time, predicting the location and type of potential software defects and alerting testers and developers in advance.

6. The software development management system according to claim 1, characterized in that: The process of using containerization technology and automating the canary release strategy involves packaging the software and its dependencies into an independent container, enabling environment-independent deployment and improving deployment flexibility and efficiency. The automated canary release strategy uses automated tools to gradually push the new version of the software to a subset of users according to predetermined rules and proportions, while monitoring user feedback and software running status in real time, and deciding whether to continue expanding the release scope based on the feedback.

7. The software development management system according to claim 1, characterized in that: The steps of building a user behavior data analysis platform and an agile improvement process are as follows: The user behavior data analysis platform collects user operation data in real time during the use of the software, such as click frequency, usage duration, and function usage sequence. Through data analysis, user needs and software problems are identified. Based on the data analysis results, the agile improvement process quickly adjusts software functions and optimizes user experience to achieve continuous software iteration.

Images