Ranking Subject Matter Experts via Eigenvector Transformation
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Solution Overview
Problem
As software projects grow in volume and complexity, it becomes increasingly difficult to identify and rank subject matter experts (SMEs) associated with specific code segments, leading to challenges in error detection and resolution due to the sheer size and proprietary nature of the code.
Innovation Solution
A system that analyzes code segments to determine associated SMEs by generating graphical representations of their attributes using radar charts, transforming these into eigenvectors and eigenvalues for numerical ranking, allowing for efficient identification and prioritization of experts based on their skills and contributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If code libraries and software projects grow in volume and complexity to provide more functionality, then the capability and versatility of the software system improve, but the difficulty of identifying and ranking subject matter experts associated with specific code segments increases
Solution Approach 1:
The patent introduces an intermediary system that uses version control metadata and code analysis tools as mediators between the complex codebase and the SME identification process. This intermediary layer automatically extracts contributor information, code ownership data, and collaboration patterns, transforming the difficult task of SME identification into a manageable process by filtering and organizing the complex information flow.
Solution Approach 2:
The patent replaces manual SME identification (mechanical human review process) with automated computational methods. By using algorithms to analyze version control metadata, code comments, and contribution patterns, the system substitutes human effort with machine-based analysis, significantly reducing the difficulty of identifying SMEs in large codebases while maintaining accuracy.
2Measurement precision
If multiple attributes are collected for each SME to improve ranking accuracy, then the measurement precision of SME expertise improves, but the device complexity of the ranking system increases
Solution Approach 1:
The patent merges multiple SME attributes (code contributions, reviews, comments, collaboration patterns) into a unified ranking model. By combining these diverse data sources into a single integrated system that processes all attributes simultaneously, the patent achieves precise SME measurement without proportionally increasing system complexity, as the merging process creates synergies that reduce overall computational burden.
Solution Approach 2:
The patent transforms multiple qualitative and quantitative SME attributes into standardized numerical parameters that can be processed uniformly. By converting diverse attributes (such as code review quality, contribution frequency, expertise domains) into comparable numerical values, the system achieves precise measurement while maintaining manageable complexity through consistent parameter handling and normalization.
3Productivity
If comprehensive SME data is analyzed to improve ranking accuracy, then the productivity of error detection and resolution improves, but the loss of time required for data processing increases
Solution Approach 1:
The patent performs preliminary actions by continuously collecting and pre-processing version control metadata and code analysis information as code is being developed and modified. Rather than analyzing all SME data from scratch when an error occurs, the system maintains pre-computed profiles of SME expertise, contribution patterns, and code ownership, enabling rapid error resolution by leveraging previously processed information.
Solution Approach 2:
The patent implements continuous data collection and analysis, where SME attributes are updated and refined continuously as new code is committed, reviewed, and modified. This continuous process ensures that the SME ranking system always has current, accurate information without requiring periodic batch processing, thereby reducing the time loss associated with data processing while maintaining high productivity in error detection and resolution.
Data Source
AI summary
Aspects of the invention include determining, by a processor, a code segment of a computer code, analyzing the code segment to determine one or more other code segments associated with the code segment, determining a set of subject matter experts (SMEs) associated with the code segment and the one or more other code segments, obtaining SME data for each SME in the set of SMEs, wherein the SME data comprises a set of attributes associated with the SME, generating, by the processor, a graphical representation of the set of attributes for each SME in the set of SMEs, transforming the graphical representations into an eigenvectors and eigenvalues, and ranking the SMEs based on their associated eigenvectors and eigenvalues.