An Extensible Feature-Oriented Approach for Fine-Grained Code Quality Analysis
Assessing code quality is crucial for effective software maintenance and evolution. Traditional tools like SonarQube offer valuable insights at the application level but lack the granularity needed for detailed, feature-specific analysis. This paper emphasizes the importance of feature-oriented code quality analysis, often overlooked by mainstream tools due to the challenge of correlating high-level feature descriptions with low-level code implementations. To tackle this issue, we leverage existing feature location techniques to introduce a novel approach enabling granular analysis tailored to specific application features. We discuss the motivations for this approach, highlighting its potential to improve precision in enhancement and maintenance strategies. Additionally, this paper introduces a tool-based approach known as InsightMapper. We also present a study demonstrating the benefits of this method through the analysis of two case studies, featuring a recognized benchmark in the feature location domain.
Tue 22 OctDisplayed time zone: Pacific Time (US & Canada) change
09:00 - 10:30 | |||
09:00 30mDay opening | GPCE24 Opening GPCE | ||
09:30 30mTalk | Automated Generation of Code Contracts - Generative AI to the Rescue? GPCE Sandra Greiner University of Southern Denmark, Noah Bühlmann University of Bern, Manuel Ohrndorf University of Bern, Christos Tsigkanos University of Athens, Greece, Oscar Nierstrasz feenk.com, Timo Kehrer University of Bern Link to publication DOI Pre-print | ||
10:00 30mTalk | An Extensible Feature-Oriented Approach for Fine-Grained Code Quality Analysis GPCE |