Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Scandinavian Journal of Information Systems
Recent advances of grammatical inference
Theoretical Computer Science - Special issue on algorithmic learning theory
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Evaluating expertise recommendations
GROUP '01 Proceedings of the 2001 International ACM SIGGROUP Conference on Supporting Group Work
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning
Modern Information Retrieval
Two case studies of open source software development: Apache and Mozilla
ACM Transactions on Software Engineering and Methodology (TOSEM)
Expertise browser: a quantitative approach to identifying expertise
Proceedings of the 24th International Conference on Software Engineering
Identifying Reasons for Software Changes Using Historic Databases
ICSM '00 Proceedings of the International Conference on Software Maintenance (ICSM'00)
An Approach to Classify Software Maintenance Requests
ICSM '02 Proceedings of the International Conference on Software Maintenance (ICSM'02)
Management of Interdependencies in Collaborative Software Development
ISESE '03 Proceedings of the 2003 International Symposium on Empirical Software Engineering
GROUP '05 Proceedings of the 2005 international ACM SIGGROUP conference on Supporting group work
Proceedings of the 28th international conference on Software engineering
Supporting change request assignment in open source development
Proceedings of the 2006 ACM symposium on Applied computing
Detection of Duplicate Defect Reports Using Natural Language Processing
ICSE '07 Proceedings of the 29th international conference on Software Engineering
How Long Will It Take to Fix This Bug?
MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
Determining Implementation Expertise from Bug Reports
MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
An approach to detecting duplicate bug reports using natural language and execution information
Proceedings of the 30th international conference on Software engineering
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Characterizing and predicting which bugs get fixed: an empirical study of Microsoft Windows
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
Automatic categorization of bug reports using latent Dirichlet allocation
Proceedings of the 5th India Software Engineering Conference
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Developer prioritization in bug repositories
Proceedings of the 34th International Conference on Software Engineering
Characterizing and predicting which bugs get reopened
Proceedings of the 34th International Conference on Software Engineering
Factors characterizing reopened issues: a case study
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
DRETOM: developer recommendation based on topic models for bug resolution
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
Issue ownership activity in two large software projects
ACM SIGSOFT Software Engineering Notes
Who is going to mentor newcomers in open source projects?
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
Towards an early software estimation using log-linear regression and a multilayer perceptron model
Journal of Systems and Software
PorchLight: a tag-based approach to bug triaging
Proceedings of the 2013 International Conference on Software Engineering
It's not a bug, it's a feature: how misclassification impacts bug prediction
Proceedings of the 2013 International Conference on Software Engineering
Cassandra: proactive conflict minimization through optimized task scheduling
Proceedings of the 2013 International Conference on Software Engineering
YODA: young and newcomer developer assistant
Proceedings of the 2013 International Conference on Software Engineering
Proceedings of the 10th Working Conference on Mining Software Repositories
Bug report assignee recommendation using activity profiles
Proceedings of the 10th Working Conference on Mining Software Repositories
A new perspective on the socialness in bug triaging: a case study of the eclipse platform project
Proceedings of the 2013 International Workshop on Social Software Engineering
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A key collaborative hub for many software development projects is the bug report repository. Although its use can improve the software development process in a number of ways, reports added to the repository need to be triaged. A triager determines if a report is meaningful. Meaningful reports are then organized for integration into the project's development process. To assist triagers with their work, this article presents a machine learning approach to create recommenders that assist with a variety of decisions aimed at streamlining the development process. The recommenders created with this approach are accurate; for instance, recommenders for which developer to assign a report that we have created using this approach have a precision between 70% and 98% over five open source projects. As the configuration of a recommender for a particular project can require substantial effort and be time consuming, we also present an approach to assist the configuration of such recommenders that significantly lowers the cost of putting a recommender in place for a project. We show that recommenders for which developer should fix a bug can be quickly configured with this approach and that the configured recommenders are within 15% precision of hand-tuned developer recommenders.