Predicting source code changes by mining change history

Predicting Bugs in Source Code Changes with Incremental Learning Method

Software is constructed by a series of changes and each change has the risk to introduce bugs. Predicting the existence of bugs in source code changes could help developers detect and fix bugs immediately upon the completion of a change, which accelerates the bug fixing process and save the limited time and human resources effectively. However, because of altering nature in the underlying bug g...

Classifying Code Changes and Predicting Defects Using Change Genealogies

Identifying bug fixes and using them to estimate or even predict software quality is a frequent task when mining version archives. The number of applied bug fixes serves as code quality metric identifying defect-prone and non-defect-prone code artifacts. But when is a set of applied code changes, we call it change set, considered a bug fix and which metrics should be used to building high quali...

A Source Code History Navigator

Facilitating Program Comprehension by Mining Association Rules from Source Code

Program comprehension is an important part of software maintenance, especially when program structure is complex and documentation is unavailable or outdated. Data mining can produce structural views of source code thus facilitating legacy systems understanding. This paper presents a method for mining association rules from code aiming at capturing program structure and achieving better system ...

Visually characterizing source code changes

Revision Control Systems (e.g., SVN, Git, Mercurial) include automatic and advanced merging algorithms that help developers to merge their modifications with development repositories. While these systems can help to textually detect conflicts, they do not help to identify the semantic consequences of a change. Unfortunately, there is little support to help release masters (integrators) to take ...