Investigating how to effectively combine static concern location techniques
Proceedings of the 3rd International Workshop on Search-Driven Development: Users, Infrastructure, Tools, and Evaluation
Portfolio: a search engine for finding functions and their usages
Proceedings of the 33rd International Conference on Software Engineering
Using structural and textual information to capture feature coupling in object-oriented software
Empirical Software Engineering
Remodularizing Java programs for improved locality of feature implementations in source code
Science of Computer Programming
Proceedings of the 50th Annual Southeast Regional Conference
Concept location using formal concept analysis and information retrieval
ACM Transactions on Software Engineering and Methodology (TOSEM)
Information and Software Technology
Improving feature location practice with multi-faceted interactive exploration
Proceedings of the 2013 International Conference on Software Engineering
Combining concern input with program analysis for bloat detection
Proceedings of the 2013 ACM SIGPLAN international conference on Object oriented programming systems languages & applications
Portfolio: Searching for relevant functions and their usages in millions of lines of code
ACM Transactions on Software Engineering and Methodology (TOSEM) - Testing, debugging, and error handling, formal methods, lifecycle concerns, evolution and maintenance
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Data fusion is the process of integrating multiple sources of information such that their combination yields better results than if the data sources are used individually. This paper applies the idea of data fusion to feature location, the process of identifying the source code that implements specific functionality in software. A data fusion model for feature location is presented which defines new feature location techniques based on combining information from textual, dynamic, and web mining analyses applied to software. A novel contribution of the proposed model is the use of advanced web mining algorithms to analyze execution information during feature location. The results of an extensive evaluation indicate that the new feature location techniques based on web mining improve the effectiveness of existing approaches by as much as 62%.