Bug localization using latent Dirichlet allocation
Information and Software Technology
Augmented bug localization using past bug information
Proceedings of the 48th Annual Southeast Regional Conference
Proceedings of the 8th Working Conference on Mining Software Repositories
A static technique for fault localization using character n-gram based information retrieval model
Proceedings of the 5th India Software Engineering Conference
A case study of software quality and reuse
Proceedings of the 50th Annual Southeast Regional Conference
Proceedings of the 50th Annual Southeast Regional Conference
Automatically detecting the quality of the query and its implications in IR-based concept location
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Analyzing and mining a code search engine usage log
Empirical Software Engineering
Identifying Linux bug fixing patches
Proceedings of the 34th International Conference on Software Engineering
Combining lexical and structural information for static bug localisation
International Journal of Computer Applications in Technology
Concept location using formal concept analysis and information retrieval
ACM Transactions on Software Engineering and Methodology (TOSEM)
Is text search an effective approach for fault localization: a practitioners perspective
Proceedings of the 3rd annual conference on Systems, programming, and applications: software for humanity
Sando: an extensible local code search framework
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
Empirical Software Engineering
Proceedings of the 10th Working Conference on Mining Software Repositories
Assisting code search with automatic query reformulation for bug localization
Proceedings of the 10th Working Conference on Mining Software Repositories
Studying software evolution using topic models
Science of Computer Programming
Hi-index | 0.00 |
In bug localization, a developer uses information about a bug to locate the portion of the source code to modify to correct the bug. Developers expend considerable effort performing this task. Some recent static techniques for automatic bug localization have been built around modern information retrieval (IR) models such as latent semantic indexing (LSI); however, latent Dirichlet allocation (LDA), a modular and extensible IR model, has significant advantages over both LSI and probabilistic LSI (pLSI). In this paper we present an LDA-based static technique for automating bug localization. We describe the implementation of our technique and three case studies that measure its effectiveness. For two of the case studies we directly compare our results to those from similar studies performed using LSI. The results demonstrate our LDA-based technique performs at least as well as the LSI-based techniques for all bugs and performs better, often significantly so, than the LSI-based techniques for most bugs.