This paper presents a comprehensive method for identifying fine-grained change patterns in the source code of large-scale software projects. Source code changes are computed by differencing abstract syntax trees of adjacent versions and transferred to a set of logical statements called a factbase. A factbase contains information for tracking and relating source code entities across versions and can be used to integrate analysis results of other tools such as call graphs and control flows. Users can obtain a list of change pattern instances by querying the factbase. Continue reading A Comprehensive and Scalable Method for Analyzing Fine-Grained Source Code Change Patterns (SANER’15)
In this paper, we reinforce history-based concern mining techniques with fine-grained change analysis based on tree differencing on abstract syntax trees. Source code changes are recorded as facts over source code regions according to the RDF (Resource Description Framework) data model so that the analysis can be performed in terms of factbase queries.
In this paper, we propose an automated method for detecting and tracking homologous code in genealogy of evolving software using fine-grained tree differencing on source code. Such a tool would help software developers/maintainers to better understand the source code and to detect/prevent inconsistent modifications that may lead to latent errors.