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Mining issue tracking systems using topic models for corpus exploration, trend analysis and understanding evolution
| Content Provider | Indraprastha Institute of Information Technology, Delhi |
|---|---|
| Author | Aggarwal, Ayushi |
| Abstract | Software systems employ Issue Tracking Systems (also known as Defect or Bug Tracking Sys- tems) such as Google Code Hosting or Bugzilla to facilitate software maintenance activities through bug reporting, archiving and xing. Manual examination of these systems for statisti- cal analysis and extraction of actionable information is a cumbersome task for two reasons - all reports are written in natural language and the amount of unstructured text is large. This in- formation overload can be handled by applying text mining techniques such as Topic Modeling, which discovers latent topics (or themes) across large collection of free-form textual data and the relationship between topics and documents. This project focuses on applying a Topic Mod- eling technique called Latent Dirichlet Allocation (LDA) on Google Chromium Browser Project bug database for trend analysis, corpus exploration and understanding the evolution of software system over time. Multi-dimensional analysis on this bug database leads to the discovery of prevalent trends and patterns which are presented in the form of visualizations. We hope our results will facilitate expertise modeling, resource allocation and knowledge management in the software development team. |
| File Format | |
| Language | English |
| Publisher | IIIT Delhi |
| Access Restriction | Authorized |
| Subject Keyword | Latent Dirichlet Allocation Topic Models Issue Tracking System Defect Tracking System Software Quality Assurance Software Maintenance Information Retrieval Bug Reports |
| Content Type | Text |
| Educational Degree | Bachelor of Technology (B.Tech.) |
| Resource Type | Thesis |
| Subject | Data processing & computer science |
