Partial Commit Detection Using Matrix Transformations
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Solution Overview
Problem
Existing software systems are inefficient in detecting implicit file dependencies during partial commits, as they often miss dynamic dependencies and are not customizable to various programming languages, leading to incomplete changes and increased testing efforts.
Innovation Solution
A method and system that extract dependency information from version history and bug databases, create a dimensional matrix, and perform matrix transformations to identify missing files in a partial commit, using singular vector decomposition and weighted vectors to evaluate file dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static analysis tools are used to determine file dependencies, then structural dependencies can be tracked, but dynamic run-time dependencies are missed
Solution Approach 1:
The patent combines static analysis (examining source code and configuration files) with dynamic analysis (monitoring run-time behavior) to create a comprehensive dependency detection system. This merging allows the system to capture both structural relationships from code and actual runtime interactions, eliminating the limitation of using either approach alone.
2Measurement precision
If dynamic analysis tools are customized for a particular programming language, then language-specific dependencies can be detected, but they fail to operate on software systems built on a variety of languages
Solution Approach 1:
The patent creates a universal dependency detection system that can analyze multiple programming languages through a common framework. The system uses language-agnostic techniques such as parsing, static analysis, and runtime instrumentation that work across different languages, while still capturing language-specific dependency patterns through adaptive analysis rules.
3Reliability
If comprehensive dependency analysis is performed on all files, then all dependencies are identified, but the effort during testing phase increases
Solution Approach 1:
The patent extracts and analyzes only the relevant subset of files that are actually affected by a change, rather than performing comprehensive analysis on all files in the system. By using change detection mechanisms and dependency graphs, the system identifies and focuses only on the minimal set of files that need to be tested, significantly reducing testing effort while maintaining completeness for affected components.
4Loss of information
If configuration files are analyzed for dependency information, then static dependencies are captured, but dynamic run-time dependencies are not revealed
Solution Approach 1:
The patent performs preliminary static analysis of configuration files to identify potential dependencies before runtime execution. This preliminary action creates an initial dependency map that guides subsequent dynamic analysis, allowing the system to focus runtime monitoring on identified dependency paths and discover actual runtime interactions that confirm or update the static analysis results.
Data Source
AI summary
The present invention provides a method and system for detecting a partial commit of software. A dependency information of the software is extracted from a version history and a bug database. A dimensional matrix containing a set of commit, and relationship information with a set of files with each commit is created from the dependency information. A centrality matrix is computed by performing a first set of matrix transformations on the dimensional matrix. A set of missing files of a partial commit, is identified by performing a second set of matrix transformations on the centrality matrix and a file vector, the file vector including a file dependency information of the partial commit.