Tracking down software bugs using automatic anomaly detection
Proceedings of the 24th International Conference on Software Engineering
The Journal of Machine Learning Research
An Information Retrieval Approach to Concept Location in Source Code
WCRE '04 Proceedings of the 11th Working Conference on Reverse Engineering
Proceedings of the 28th international conference on Software engineering
Detection of Duplicate Defect Reports Using Natural Language Processing
ICSE '07 Proceedings of the 29th international conference on Software Engineering
Combining Formal Concept Analysis with Information Retrieval for Concept Location in Source Code
ICPC '07 Proceedings of the 15th IEEE International Conference on Program Comprehension
An approach to detecting duplicate bug reports using natural language and execution information
Proceedings of the 30th international conference on Software engineering
Using information retrieval based coupling measures for impact analysis
Empirical Software Engineering
Proceedings of the International Conference on Advances in Computing, Communication and Control
Towards the next generation of bug tracking systems
VLHCC '08 Proceedings of the 2008 IEEE Symposium on Visual Languages and Human-Centric Computing
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
A discriminative model approach for accurate duplicate bug report retrieval
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
Automated topic naming to support cross-project analysis of software maintenance activities
Proceedings of the 8th Working Conference on Mining Software Repositories
Towards more accurate retrieval of duplicate bug reports
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
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Bug-tracking and issue-tracking systems tend to be populated with bugs, issues, or tickets written by a wide variety of bug reporters, with different levels of training and knowledge about the system being discussed. Many bug reporters lack the skills, vocabulary, knowledge, or time to efficiently search the issue tracker for similar issues. As a result, issue trackers are often full of duplicate issues and bugs, and bug triaging is time consuming and error prone. Many researchers have approached the bug-deduplication problem using off-the-shelf information-retrieval tools, such as BM25F used by Sun et al. In our work, we extend the state of the art by investigating how contextual information, relying on our prior knowledge of software quality, software architecture, and system-development (LDA) topics, can be exploited to improve bug-deduplication. We demonstrate the effectiveness of our contextual bug-deduplication method on the bug repository of the Android ecosystem. Based on this experience, we conclude that researchers should not ignore the context of software engineering when using IR tools for deduplication.