Automated Performance Bug Detection via Code Recommendation Tables
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Solution Overview
Problem
Performance bugs in software systems are difficult to detect due to the absence of fail-stop symptoms, leading to significant performance degradation, excessive resource usage, and reduced throughput, which are challenging to identify and remediate, especially in complex software systems.
Innovation Solution
An automated system that analyzes source code repositories to detect performance bugs by constructing a code recommendation table from performance-related pull requests using a machine learning classifier, extracting structural and performance features, and providing code recommendations based on similarities with improved code snippets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional bug detection methods are used, then functional bugs and compiler errors can be detected, but performance bugs cannot be detected due to absence of fail-stop symptoms
Solution Approach 1:
The patent introduces an automated analysis system as an intermediary between the software code and performance bug detection. This system uses machine learning classifiers and code recommendation tables to mediate the detection process, enabling identification of performance bugs that would otherwise be undetectable through traditional methods by analyzing code patterns and comparing against known performance improvement cases
Solution Approach 2:
The patent replaces manual performance bug detection mechanisms with automated machine learning-based analysis. Instead of relying on human reviewers to identify performance issues, the system automatically analyzes code repositories, trains classifiers on performance-related pull requests, and generates detections through computational comparison of code features against a code recommendation table
2Productivity
If code analysis is performed manually, then detailed performance evaluation is possible, but the process is time-consuming and scales poorly to complex software systems
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning classifiers on historical performance-related pull requests and pre-building a code recommendation table with performance improvement patterns before actual bug detection begins. This preliminary preparation enables rapid automated analysis of new code without requiring time-consuming manual review during the detection phase
Solution Approach 2:
The system enables self-service by allowing the automated analysis tool to independently detect performance bugs without requiring manual intervention. The machine learning model automatically analyzes code repositories, identifies potential performance issues, and generates detections by comparing code against the pre-built recommendation table, eliminating the need for human reviewers to perform time-consuming manual analysis
3Measurement precision
If comprehensive code analysis is performed to detect performance bugs, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the performance bug detection system into distinct modular components: a training module that trains machine learning classifiers on performance-related pull requests, a code analysis module that extracts features from target code, and a detection module that compares code against a code recommendation table. This segmentation allows each component to be developed and maintained independently while working together to achieve accurate detection
4Productivity
If performance improvements are made to code, then software performance increases, but it becomes difficult to distinguish performance improvements from other modifications
Solution Approach 1:
The patent uses machine learning classifiers to 'color' or label pull requests based on their content characteristics. The trained classifiers identify patterns that distinguish performance-related modifications from other types of changes, effectively tagging performance improvements with a recognizable signature that can be automatically detected and analyzed by the system
Data Source
AI summary
An automated system for detecting performance bugs in a program and for providing code recommendations to improve the performance of the program generates a code recommendation table from performance-related pull requests. The performance-related pull requests are identified in part from a classifier trained on semi-supervised data. A code recommendation table is generated from performance-related pull requests and is searched for similarly-improved code based on a set of difference features that includes structural and performance features of the before-code of a pull request that is not in the after-code.