Software Change Impact Analysis
Software Change Impact Analysis
A State-of-the-Art Survey on Software Merging
IEEE Transactions on Software Engineering
Detection of Logical Coupling Based on Product Release History
ICSM '98 Proceedings of the International Conference on Software Maintenance
An XML-Based Lightweight C++ Fact Extractor
IWPC '03 Proceedings of the 11th IEEE International Workshop on Program Comprehension
Predicting Source Code Changes by Mining Change History
IEEE Transactions on Software Engineering
Dex: A Semantic-Graph Differencing Tool for Studying Changes in Large Code Bases
ICSM '04 Proceedings of the 20th IEEE International Conference on Software Maintenance
Predicting Change Propagation in Software Systems
ICSM '04 Proceedings of the 20th IEEE International Conference on Software Maintenance
An Empirical Study of Fine-Grained Software Modifications
ICSM '04 Proceedings of the 20th IEEE International Conference on Software Maintenance
Meta-differencing: an infrastructure for source code difference analysis
Meta-differencing: an infrastructure for source code difference analysis
Mining Version Histories to Guide Software Changes
IEEE Transactions on Software Engineering
Fine-Grained Analysis of Change Couplings
SCAM '05 Proceedings of the Fifth IEEE International Workshop on Source Code Analysis and Manipulation
Mining sequences of changed-files from version histories
Proceedings of the 2006 international workshop on Mining software repositories
Combining Single-Version and Evolutionary Dependencies for Software-Change Prediction
MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
Mining Software Repositories for Traceability Links
ICPC '07 Proceedings of the 15th IEEE International Conference on Program Comprehension
Mining evolutionary dependencies from web-localization repositories: Special Issue Articles
Journal of Software Maintenance and Evolution: Research and Practice - Web Site Evolution (WSE 2006)
Journal of Software Maintenance and Evolution: Research and Practice
Clustering source code files to predict change propagation during software maintenance
Proceedings of the 50th Annual Southeast Regional Conference
Hi-index | 0.00 |
The thesis proposes a software-change prediction approach that is based on mining fine-grained evolutionary couplings from source code repositories. Here, fine-grain refers to identifying couplings between source code entities such as methods, control structures, or even comments. This differs from current source code mining techniques that typically only identify couplings between files or fairly high-level entities. Furthermore, the model combines the mined evolutionary couplings with the estimated changes identified by traditional impact analysis techniques (e.g., static analysis of call and program-dependency graphs). The research hypothesis is that software-change prediction using the proposed synergistic approach results in an overall improved expressiveness (i.e., granularity and context given to a developer) and effectiveness (i.e., accuracy of the prediction)