Software Ecosystem Activity Scoring via Modular Rating Services
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Solution Overview
Problem
Existing technologies lack effective methods for scoring software components based on their ecosystem activity, which is crucial for assessing their maturity and available support.
Innovation Solution
A system and method for calculating an ecosystem activity score for software projects by gathering data from source code repositories and developer forums, and then consolidating individual scores into a unified ecosystem activity score using importance weights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data sources are integrated to calculate ecosystem activity score, then the comprehensiveness of the score is improved, but the system complexity increases
Solution Approach 1:
The patent segments the ecosystem activity scoring system into multiple independent rating services, each responsible for a specific aspect: popularity rating service, releases rating service, forks rating service, defects rating service, Q&A rating service, and solution rating service. Each service processes data from specific sources and generates individual scores that are later aggregated into a comprehensive ecosystem activity score, thereby managing system complexity through modular architecture while maintaining measurement comprehensiveness
Solution Approach 2:
The patent introduces a batch system as an intermediary component that coordinates between multiple data sources (GitHub API, GitLab API, Bitbucket API, Stack Overflow API) and the various rating services. The batch system manages data collection, processing queues, and score aggregation, acting as a mediator that simplifies the interaction complexity between diverse data sources and scoring mechanisms while ensuring accurate ecosystem activity measurement
2Measurement precision
If data is collected from multiple external sources, then the evaluation comprehensiveness is improved, but the data collection time increases
Solution Approach 1:
The patent implements preliminary action by pre-fetching and caching data from external sources (GitHub, GitLab, Bitbucket, Stack Overflow) before the actual scoring process. The batch system proactively collects data from multiple APIs and stores it in local databases, so that when rating services need this data, it is already available, significantly reducing the data collection time during score generation while maintaining comprehensive evaluation coverage
Solution Approach 2:
The patent establishes continuous data collection and updating mechanisms where the batch system periodically refreshes data from external sources and maintains ready-to-use data caches. This continuous action ensures that the ecosystem activity scores are based on current information without requiring time-consuming data collection at the moment of scoring, thus balancing comprehensiveness with efficiency
3Reliability
If multiple scoring dimensions are calculated, then the score reliability is improved, but the calculation complexity increases
Solution Approach 1:
The patent divides the scoring process into distinct rating services, each calculating a specific dimension: popularity score (based on stars, forks, watchers), releases score (based on release frequency and recency), forks score (based on fork activity), defects score (based on issue resolution), Q&A score (based on forum activity), and solution score (based on problem-solving effectiveness). This segmentation allows each service to focus on its specific calculation logic independently, improving reliability through specialized assessment while managing complexity through modular design
Solution Approach 2:
The patent merges the results from multiple independent rating services into a unified ecosystem activity score through a weighted aggregation process. The batch system combines individual scores (popularity, releases, forks, defects, Q&A, solution) using predetermined weights to produce a comprehensive final score. This merging approach maintains the reliability benefits of multi-dimensional assessment while simplifying the output to a single actionable metric that reflects overall ecosystem health
Data Source
AI summary
Systems and methods for automatically scoring ecosystem activity are disclose. The method includes receiving data from repositories and developer forums related to a software project, calculating a popularity score of the software project based on the received data, evaluating forks data of forks of the software project, calculating a project activity score of the software project based on the received data and the evaluated forks data, calculating a defect score, calculating a community score by evaluating posts of the developer forums on the software project, calculating a solution score by evaluating solutions provided as a response to queries on the software project, calculating a sentiment score by aggregating and analyzing identified positive and negative sentiments, calculating a userbase score, and calculating an ecosystem activity score based on one or more individual scores.