Towards understanding software change request assignment: a survey with practitioners
Proceedings of the 17th International Conference on Evaluation and Assessment in Software Engineering
PorchLight: a tag-based approach to bug triaging
Proceedings of the 2013 International Conference on Software Engineering
Proceedings of the 10th Working Conference on Mining Software Repositories
A dataset from change history to support evaluation of software maintenance tasks
Proceedings of the 10th Working Conference on Mining Software Repositories
WikiWho: precise and efficient attribution of authorship of revisioned content
Proceedings of the 23rd international conference on World wide web
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There is a tremendous wealth of code authorship information available in source code. Motivated with the presence of this information, in a number of open source projects, an approach to recommend expert developers to assist with a software change request (e.g., a bug fixes or feature) is presented. It employs a combination of an information retrieval technique and processing of the source code authorship information. The relevant source code files to the textual description of a change request are first located. The authors listed in the header comments in these files are then analyzed to arrive at a ranked list of the most suitable developers. The approach fundamentally differs from its previously reported counterparts, as it does not require software repository mining. Neither does it require training from past bugs/issues, which is often done with sophisticated techniques such as machine learning, nor mining of source code repositories, i.e., commits. An empirical study to evaluate the effectiveness of the approach on three open source systems, ArgoUML, JEdit, and MuCommander, is reported. Our approach is compared with two representative approaches: 1) using machine learning on past bug reports, and 2) based on commit logs. The presented approach is found to provide recommendation accuracies that are equivalent or better than the two compared approaches. These findings are encouraging, as it opens up a promising and orthogonal possibility of recommending developers without the need of any historical change information.