Software Defect Detection via Code Change and Performance Analysis
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Solution Overview
Problem
Software code often contains defects that lead to improper execution, and existing technologies face challenges in accurately identifying and categorizing these defects, resulting in inefficiencies and potential creation of more errors due to false positives.
Innovation Solution
A software defect detection tool comprising a retrieval engine, code analysis engine, performance analysis engine, and aggregation engine that monitors software code changes and performance in real-time to detect anomalies and identify defects, while a software defect analysis tool categorizes errors and generates corrective action plans using a universal logging system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing defect detection technologies are used to identify and categorize software defects, then defect detection capability is provided, but false positives occur and additional errors are created
Solution Approach 1:
The defect detection process is segmented into multiple specialized engines: a retrieval engine for obtaining code and defect data, a code analysis engine for determining code modifications, a performance analysis engine for detecting execution anomalies, and an aggregation engine for correlating findings. This segmentation allows each component to focus on specific aspects of defect detection, improving overall accuracy while reducing false positives through coordinated multi-perspective analysis.
Solution Approach 2:
The system performs preliminary actions by retrieving and analyzing the actual software code and its prior versions before defect detection occurs. The code analysis engine proactively determines code modifications by comparing current code against prior versions, establishing a baseline of expected changes. This preliminary code understanding enables more accurate defect detection and reduces false positives by distinguishing intentional modifications from actual defects.
2Loss of information
If manual defect analysis and categorization is performed, then detailed defect understanding is achieved, but time consumption and productivity loss occur
Solution Approach 1:
The system enables self-service automated defect analysis and categorization through its multi-engine architecture. The retrieval engine automatically obtains code and defect information, the code analysis engine autonomously determines modifications, the performance analysis engine independently detects anomalies, and the aggregation engine automatically correlates all findings to identify defects. This automated self-service process eliminates manual intervention while maintaining high-quality defect categorization, thereby preserving software development productivity.
3Reliability
If real-time performance monitoring is implemented, then defect detection capability is improved, but system complexity and computational resources increase
Solution Approach 1:
The performance analysis engine serves multiple functions: it monitors software code execution in real-time, detects performance anomalies, determines timing information, and provides data to the aggregation engine for defect identification. This multi-functionality reduces overall system complexity by consolidating diverse monitoring tasks into a single versatile component, while maintaining high defect detection reliability through comprehensive real-time analysis.
Data Source
AI summary
A software defect detection tool determines a modification in a software code at a first time and analyzes an execution of the software code to detect a performance issue at a second time. The software defect detection tool detects a defect in the software code by a comparison of the first time and a second time. A software defect analysis tool generates a cause/category combination for a software code defect. The software defect analysis tool determines whether the cause/category combination is an approved combination and whether the software code defect is a false positive. The software defect analysis tool generates a corrective action plan indicating measures to implement to reduce software defects.