Expertise browser: a quantitative approach to identifying expertise
Proceedings of the 24th International Conference on Software Engineering
Practices of Software Maintenance
ICSM '98 Proceedings of the International Conference on Software Maintenance
Proceedings of the 28th international conference on Software engineering
A machine learning approach to semi-automating workflow staff assignment
Proceedings of the 2007 ACM symposium on Applied computing
Does a programmer's activity indicate knowledge of code?
Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Mining usage expertise from version archives
Proceedings of the 2008 international working conference on Mining software repositories
Measuring developer contribution from software repository data
Proceedings of the 2008 international working conference on Mining software repositories
Improving bug triage with bug tossing graphs
Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Optimized assignment of developers for fixing bugs an initial evaluation for eclipse projects
ESEM '09 Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement
Reducing the effort of bug report triage: Recommenders for development-oriented decisions
ACM Transactions on Software Engineering and Methodology (TOSEM)
DREX: Developer Recommendation with K-Nearest-Neighbor Search and Expertise Ranking
APSEC '11 Proceedings of the 2011 18th Asia-Pacific Software Engineering Conference
Automatic categorization of bug reports using latent Dirichlet allocation
Proceedings of the 5th India Software Engineering Conference
DRETOM: developer recommendation based on topic models for bug resolution
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
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One question which frequently arises within the context of artifacts stored in a bug tracking repository is: who should work on this bug report? A number of approaches exist to semi-automatically identify and recommend developers, e.g. using machine learning techniques and social networking analysis. In this work, we propose a new approach for assignee recommendation leveraging user activities in a bug tracking repository. Within the bug tracking repository, an activity profile is created for each user from the history of all his activities (i.e. review, assign, and resolve). This profile, to some extent, indicates the users role, expertise, and involvement in this project. These activities influence and contribute to the identification and ranking of suitable assignees. In order to evaluate our work, we apply it to bug reports of three different projects. Our results indicate that the proposed approach is able to achieve an average hit ratio of 88%. Comparing this result to the LDA-SVMbased assignee recommendation technique, it was found that the proposed approach performs better.