Impacted Work Item Risk Analysis from Commit Delta Lines
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Solution Overview
Problem
Existing impact analysis methods for software applications are inefficient and time-consuming, particularly in identifying risky work items that may cause regression, and lack automated mechanisms for deep prioritization based on real data, leading to ineffective manual processes and increased time lag in release processes.
Innovation Solution
An automated system and method using real data to filter and prioritize impacted work items by computing delta lines, code coverage, and static code analysis to identify risky artifacts without manual intervention, integrating with SCM and ALM tools to gather and process data for risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual cherry-picking of impacted work items is performed, then expertise and knowledge can be applied to identify risky items, but it consumes significant time and is not scalable
Solution Approach 1:
The patent introduces an intermediary automated system that acts as a bridge between manual expertise and large-scale work item analysis. The system uses machine learning models trained on historical data to capture expert knowledge, then applies this knowledge automatically to prioritize impacted work items, resolving the contradiction between human expertise accuracy and automated speed
Solution Approach 2:
The system performs preliminary actions by pre-training machine learning models on historical work item data before actual impact analysis. This preliminary training phase captures expert patterns and knowledge, enabling the system to quickly prioritize new impacted work items without requiring real-time manual review, thus reducing time loss while maintaining accuracy
2Productivity
If text-based keyword matching is used to prioritize impacted work items, then automation is achieved and time is saved, but the results are insufficient and depend on consistent keyword usage
Solution Approach 1:
The patent transforms the approach from simple text-based keyword matching to a multi-parameter machine learning analysis. Instead of relying solely on keyword presence, the system evaluates multiple parameters including code coverage metrics, change magnitude, work item relationships, and historical patterns, significantly improving prioritization accuracy while maintaining automation and speed
3Reliability
If all impacted work items are scanned manually, then comprehensive coverage is achieved, but the process becomes time-consuming and creates lag in release processes
Solution Approach 1:
The patent applies partial action by focusing computational resources on analyzing only the most critical aspects of impacted work items using machine learning prioritization. Instead of exhaustive manual review of all items, the system identifies and deeply analyzes the top-priority subset, achieving sufficient reliability for release decisions while dramatically reducing time lag
4Productivity
If automated impact analysis is implemented, then time consumption is reduced and productivity increases, but the system complexity increases
Solution Approach 1:
The patent segments the automated impact analysis system into distinct modular components: data collection module, machine learning model training module, impact analysis module, and prioritization module. This segmentation allows each component to be developed, maintained, and scaled independently, managing system complexity while maintaining high productivity
Data Source
AI summary
A method and system of identifying risky impacted work items is provided. The method includes identifying work items impacted by a commit between a start tag and an end tag, and information associated with one or more delta lines associated with the impacted work files; computing delta lines for each of the identified work items impacted by the commit, for determining commits that have touched the delta lines and the commits impacted as a result of changes between the start tag and the end tag and fetching work items against which impacted commit was done and impacted work item wise delta lines details; computing delta code coverage information for the work items; determining a list of critical, blocker open issues against all impacted work items and scanning the delta code coverage information for identifying and filtering risky impacted work items based on predetermined criteria and based on the determined list.