Code Commit Anomaly Detection Using Behavioral Baselines
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing software systems lack efficient methods to detect anomalies in code commits, particularly those introduced by malicious insiders, leading to potential vulnerabilities and backdoors in software releases.
Innovation Solution
Anomaly detection techniques analyze behaviors of code committers and expected behaviors of code commits, using attribute values and machine learning models to identify anomalous patterns and potential backdoors, reducing false positives and enabling rapid risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive analysis of code commits is performed to detect anomalies, then detection accuracy is improved, but time consumption increases
Solution Approach 1:
The patent segments the code commit analysis into multiple independent attribute dimensions (commit message, code changes, timing patterns, committer behavior). Each attribute is analyzed separately using specific detection rules, allowing parallel processing that maintains high detection accuracy while reducing overall analysis time compared to comprehensive sequential review.
Solution Approach 2:
The system performs preliminary analysis by establishing baseline behaviors and expected patterns before actual anomaly detection. Historical commit data is pre-processed to create profiles of normal committer behavior, file modification patterns, and timing characteristics. This preliminary action enables faster real-time detection by comparing new commits against pre-established baselines rather than analyzing each commit from scratch.
2Reliability
If detailed review of source code is conducted to identify potential risks, then detection reliability is improved, but false positives increase
Solution Approach 1:
The patent applies different detection strictness levels to different code attributes and commit contexts. Critical security-related attributes (e.g., authentication code, encryption functions) undergo more stringent analysis, while less sensitive attributes use lighter validation. This localized quality approach maintains high reliability for critical areas while reducing false positives in non-critical areas.
Solution Approach 2:
The system dynamically adjusts detection parameters and thresholds based on the specific commit context, committer history, and code sensitivity. Detection sensitivity is modulated by analyzing multiple attributes simultaneously (timing, file types, code patterns, committer behavior), changing the effective detection parameters adaptively to reduce false positives while maintaining reliability.
3Measurement precision
If comprehensive attribute analysis is performed on code commits, then anomaly detection capability is improved, but system complexity increases
Solution Approach 1:
The complex anomaly detection system is segmented into independent attribute analysis modules (commit message analyzer, code change analyzer, timing pattern analyzer, committer behavior analyzer). Each module handles a specific attribute independently with dedicated detection rules, making the overall complex system manageable through modular design while maintaining comprehensive detection capability.
Solution Approach 2:
The patent implements a universal anomaly detection framework that handles multiple attribute types and detection scenarios through a common architecture. The same base infrastructure processes diverse attributes (textual, temporal, structural) using unified data structures and comparison mechanisms, reducing system complexity by avoiding separate specialized systems for each attribute type.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Systems, methods, and software can be used to detect anomalies in source code. In some aspects, a method comprises: obtaining, by a server, one or more attribute values associated with one or more code commits of source code; and generating, by the server and based on the one or more attribute values, an anomaly report indicating a risk level of the source code.