The Journal of Machine Learning Research
Determining Implementation Expertise from Bug Reports
MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
A theory of aspects as latent topics
Proceedings of the 23rd ACM SIGPLAN conference on Object-oriented programming systems languages and applications
Assigning bug reports using a vocabulary-based expertise model of developers
MSR '09 Proceedings of the 2009 6th IEEE 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
Fine-grained incremental learning and multi-feature tossing graphs to improve bug triaging
ICSM '10 Proceedings of the 2010 IEEE International Conference on Software Maintenance
"Not my bug!" and other reasons for software bug report reassignments
Proceedings of the ACM 2011 conference on Computer supported cooperative work
Topic-based defect prediction (NIER track)
Proceedings of the 33rd International Conference on Software Engineering
Fuzzy set and cache-based approach for bug triaging
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
A topic-based approach for narrowing the search space of buggy files from a bug report
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Topic-based, time-aware bug assignment
ACM SIGSOFT Software Engineering Notes
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Fixing defects is an essential software development activity. For commercial software vendors, the time to repair defects in deployed business-critical software products or applications is a key quality metric for sustained customer satisfaction. In this paper, we report on the analysis of about 1,500 defect records from an IBM middle-ware product collected over a five-year period. The analysis includes a characterization of each repaired defect by topic and a ranking of developers by inferred expertise on each topic. We find clear evidence that defect resolution time is strongly influenced by the specific developer and his/her expertise in the defect's topic. To validate our approach, we conducted interviews with the productâ聙聶s manager who provided us with his own ranking of developer expertise for comparison. We argue that our automated developer expertise ranking can be beneficial in the planning of a software project and is applicable beyond software support in the other phases of the software lifecycle.