Recommendation Systems for Software Comprehension and Maintenance Aided by Analytics

 Funded by FAPEMIG - Universal Call 2016.


Program comprehension is well-known to be a laborious and complex task during software maintenance. Our recent work has been using different data sources from software repositories to facilitate software comprehension and maintenance. Data includes source code, execution traces, issue tracker records, version control commits, social-technical posts. Recent literature have shown evidences on the repetitive nature of software, indicating that these data might present patterns that make feasible the use of probabilistic predictive techniques.

This project aims at proposing effective technique based on predictive data analysis to aid software developers during maintenance. This project will use and evolve the state-of-the-art techniques on software analytics and recommendation systems. The results should improve developer productivity and software quality.