Application Evolution System Using Production Data Analysis
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Solution Overview
Problem
There is a challenge in analyzing post-release data to correlate outcomes with development data and provide actionable, contextual guidance to different personnel in software development and post-release environments, which often involve distinct sets of personnel and data.
Innovation Solution
A development and production data-based application evolution system that uses artificial intelligence and analytics techniques like natural language processing, sentiment analysis, and graph analysis to create a coherent model of the impact of new releases on users, mapping this information to development context elements for informed decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional software release processes are used, then development and testing are completed, but post-release data analysis is disconnected from development context
Solution Approach 1:
The patent merges post-release data (production data, user feedback, analytics) with development context data (requirements, design documents, code repositories) into a unified analysis system. This integration allows correlation between release outcomes and development decisions, eliminating information loss and enabling continuous improvement feedback loops.
Solution Approach 2:
The system establishes a feedback mechanism where post-release data is continuously analyzed and fed back to development teams. This feedback loop provides actionable insights that inform future release planning, requirement refinement, and defect prevention, thereby improving release cycle efficiency while preserving contextual information.
2Reliability
If comprehensive testing is performed on all features, then application stability is improved, but testing phase time increases
Solution Approach 1:
The patent applies partial action by selectively testing only the most critical and high-risk features based on post-release data analysis. Instead of exhaustive testing of all features, the system identifies and prioritizes test cases that have the greatest impact on application stability, thereby reducing testing time while maintaining reliability.
Solution Approach 2:
The system performs preliminary analysis of post-release data before the testing phase to identify high-priority test cases. This preliminary action enables the testing team to focus on critical areas in advance, optimizing the testing schedule and reducing overall testing phase time while ensuring application stability.
3Ease of operation
If more test cases are prioritized and executed, then user experience is enhanced, but resource utilization increases
Solution Approach 1:
The patent applies partial action by executing only the most high-impact test cases that directly affect user experience. Instead of running all possible test cases, the system prioritizes and executes a selective subset that provides the greatest user experience improvement, thereby enhancing usability while minimizing resource consumption.
4Loss of information
If detailed post-release data analysis is performed, then actionable insights are generated, but processing complexity increases
Solution Approach 1:
The patent extracts and separates the most critical insights from post-release data using automated analysis techniques. By extracting key metrics and actionable insights from large volumes of data, the system reduces processing complexity while maintaining the generation of valuable actionable information for development teams.
Data Source
AI summary
According to examples, development and production data based application evolution may include ascertaining a goal, development data, and production data for a specified release of an application. Development and production data based application evolution may further include determining, from the development data, a feature, a use-case associated with the feature, and a plurality of tasks associated with the use-case. Further, development and production data based application evolution may include determining, from the production data, production log data and user feedback, determining, from the production log data and the user feedback data, criteria, ascertaining, a weightage for each criterion of the criteria, and determining, based on the weightage, a task priority for each task of the plurality of tasks to be applied to a different release of the application.