Static Code Analysis Exclusion Recommendation System
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Solution Overview
Problem
Developers face inefficiencies in identifying and excluding unnecessary suggestions from static code analysis results, requiring manual review of numerous check results to determine exclusion requests, which is time-consuming and can lead to quality degradation.
Innovation Solution
A system comprising development terminals, servers, and a manager's terminal that analyzes static code check results, identifies exclusion requests based on past responses, and outputs reference information to help developers differentiate between exclusion and correction recommendations, thereby streamlining the exclusion process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If developers manually review all static code analysis check results to determine exclusion requests, then comprehensive review is achieved, but time consumption increases significantly
Solution Approach 1:
The system enables self-service by automatically analyzing check results and generating exclusion recommendations without requiring manual review of each item. The recommendation generation unit autonomously processes check results, compares them against historical data and exclusion criteria, and produces prioritized exclusion candidates, freeing developers from time-consuming manual analysis while maintaining review quality through automated intelligence.
Solution Approach 2:
The recommendation generation unit acts as an intermediary between the static code analysis system and developers. It processes raw check results, applies complex analysis algorithms considering historical exclusion data and project-specific criteria, and transforms the output into actionable exclusion recommendations. This intermediary layer handles the time-consuming analysis work, allowing developers to focus on reviewing and making final decisions on pre-filtered recommendations.
2Loss of information
If all check results are presented to developers, then complete information is provided, but difficulty in identifying useful vs. useless suggestions increases
Solution Approach 1:
The system extracts and separates potentially useful exclusion candidates from the bulk of check results by applying automated analysis criteria. The recommendation generation unit identifies check results that match historical exclusion patterns or meet predefined exclusion criteria, extracting these as prioritized recommendations. This extraction process filters out clearly useful suggestions that should be corrected, making it easier for developers to focus on identifying genuine exclusion candidates without being overwhelmed by all check results.
Solution Approach 2:
The system applies different quality levels of analysis to different check results based on their characteristics. High-priority exclusion candidates that strongly match historical patterns or critical criteria receive detailed analysis and top positioning in recommendations. Less certain cases are analyzed with different criteria. This local quality approach ensures that the most important distinctions between useful and useless suggestions are highlighted with appropriate emphasis, making identification easier for developers.
3Adaptability or versatility
If manual exclusion request process is used, then developer control is maintained, but productivity decreases due to repetitive review work
Solution Approach 1:
The system provides dynamic adaptability where developers can adjust exclusion criteria, priority thresholds, and analysis parameters based on project needs and feedback. The recommendation generation unit adapts to different project contexts, team preferences, and codebase characteristics. Developers maintain control by configuring system behavior and reviewing recommendations, while the automated process dramatically increases productivity by handling the repetitive analysis and prioritization work that would otherwise require manual effort for each check result.
Data Source
AI summary
Provided is a non-transitory computer readable storage medium storing a program causing a computer to execute a process, the process including: obtaining an analysis result of a program hierarchically structured by a plurality of hierarchies; identifying an exclusion request of a check content of a same kind as a specific check content by referring to a storage unit storing information about a past exclusion request of a check content when the specific check content in the analysis result is displayed in association with a part corresponding to the specific check content of the program; outputting reference information for an exclusion request of the specific check content based on a request result of the exclusion request of the check content of the same kind, and a difference between positions in the plurality of hierarchies of the specific check content and the check content of the same kind in the program.