Open Source Quality Assessment Using Relative Feature Weighting
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Solution Overview
Problem
Existing methods for assessing the quality of open source projects are inefficient and lack transparency due to scattered data across various sources, making it difficult to evaluate software metrics and access good quality data.
Innovation Solution
A method involving extracting open source project data, transforming it into relative features using probability distributions, weighting these features, and aggregating them into practices and metrics to provide a comprehensive quality assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is collected from multiple scattered data sources to improve assessment comprehensiveness, then measurement accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the quality assessment system into multiple independent data sources (GitHub, StackOverflow, Google, NVD, package managers) that can be collected and processed separately. Each data source is treated as an independent module, allowing the system to gather comprehensive data without managing all sources as a single complex entity.
Solution Approach 2:
The patent introduces an intermediary processing layer that receives data from multiple scattered sources, standardizes the data formats, and transforms them into a unified assessment model. This intermediary layer abstracts the complexity of multiple data sources, presenting a simplified interface for quality assessment while maintaining comprehensive data collection.
2Loss of information
If more data sources are integrated to improve evaluation completeness, then information completeness improves, but data access difficulty increases
Solution Approach 1:
The patent creates a universal data collection framework that can access multiple different data sources (GitHub, StackOverflow, Google, NVD, package managers) through a single unified interface. This multi-functional system handles diverse data formats and access protocols, reducing the difficulty of accessing information from various sources while maintaining complete evaluation coverage.
Solution Approach 2:
The patent transforms data from different sources by changing their parameters and formats into a standardized structure. By applying parameter transformations and normalization techniques, the system makes diverse data from multiple sources accessible and comparable, reducing access difficulty while preserving information completeness.
3Reliability
If absolute quality metrics are used to improve measurement objectivity, then measurement reliability improves, but contextual relevance decreases
Solution Approach 1:
The patent implements a dynamic quality assessment model where the importance weights of different data sources and metrics can be adjusted based on project context, organization preferences, and specific assessment goals. This dynamic weighting system maintains objective measurement through structured data while adapting to different contextual requirements by changing the relative importance of various factors.
Solution Approach 2:
The patent creates a composite quality metric that combines multiple absolute measurements from different data sources into a unified assessment. By synthesizing data from GitHub, StackOverflow, Google, NVD, and package managers into a composite quality score, the system maintains objective reliability while the flexible combination allows contextual adaptation through different weighting schemes.
Data Source
AI summary
A method (300) for determining latent variables related to quality of an open source project. The method (300) comprising extracting (S302) open source project data (106) related to the open source project from one or more databases (102), determining (S304) one or more absolute features (108) from the open source project data (106) according to pre-set rules, retrieving (S306), for each absolute feature (108), a transform (110) related to a probability distribution, wherein the transform (110) is determined based on a multitude of open source projects, transforming (S308) the one or more absolute features (108) into one or more relative features (112) by using the transform (110) of each absolute feature (108), weighting (S310) the one or more relative features (112) using a first set of weights (114), thereby forming one or more weighted relative features, determining (S312) one or more practices (116), each related to a quality aspect of the open source project, as one or more sums of combinations of the one or more weighted relative features.