Reverse engineering: a roadmap
Proceedings of the Conference on The Future of Software Engineering
Supporting program comprehension using semantic and structural information
ICSE '01 Proceedings of the 23rd International Conference on Software Engineering
An Information Retrieval Approach to Concept Location in Source Code
WCRE '04 Proceedings of the 11th Working Conference on Reverse Engineering
Static Techniques for Concept Location in Object-Oriented Code
IWPC '05 Proceedings of the 13th International Workshop on Program Comprehension
Empirical Validation of Object-Oriented Metrics on Open Source Software for Fault Prediction
IEEE Transactions on Software Engineering
Enriching Reverse Engineering with Semantic Clustering
WCRE '05 Proceedings of the 12th Working Conference on Reverse Engineering
Combining Probabilistic Ranking and Latent Semantic Indexing for Feature Identification
ICPC '06 Proceedings of the 14th IEEE International Conference on Program Comprehension
Semantic clustering: Identifying topics in source code
Information and Software Technology
IEEE Transactions 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
Feature location via information retrieval based filtering of a single scenario execution trace
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
Source Code Retrieval for Bug Localization Using Latent Dirichlet Allocation
WCRE '08 Proceedings of the 2008 15th Working Conference on Reverse Engineering
A static technique for fault localization using character n-gram based information retrieval model
Proceedings of the 5th India Software Engineering Conference
| Hi-index | 0.02 |
Traditional bug localization techniques involve a developer using his or her knowledge of the software system to locate bugs in source code. Various automated techniques simulate knowledge of the system using source code retrieval models such as latent semantic indexing (LSI) and latent Dirichlet allocation (LDA). While these methods do an adequate job, they do not make use of another wealth of information stored in the form of past bug reports. In this paper, I present an extension to the LSI model for bug localization in which the information stored in past bug reports augments the LSI model of bug localization. I describe the details of implementing this process along with the novel patch cartographer tool that is necessary for its execution. Presented along with this description is a pair of case studies verifying the effectiveness of the patch cartographer and process respectively. Results show that the patch cartographer indeed correctly identifies affected methods from a patch file. Additionally, the study of the augmented process shows significant improvement in performance compared to LSI alone.