Abstract:
Upon receiving a new bug report, developers need to find its cause in the source code. Bug localization can be helped by a tool that ranks all source files according to h...Show MoreMetadata
Abstract:
Upon receiving a new bug report, developers need to find its cause in the source code. Bug localization can be helped by a tool that ranks all source files according to how likely they include the bug. This problem was thoroughly examined by numerous scientists. We introduce a novel adaptive bug localization algorithm. The concept behind it is based on new feature weighting approaches and an adaptive selection algorithm utilizing pointwise learn–to–rank method. The algorithm is evaluated on publicly available datasets, and is competitive in terms of accuracy and required computational resources compared to state–of–the–art. Additionally, to improve reproducibility we provide extended datasets that include computed features and partial steps, and we also provide the source code.
Published in: IEEE Transactions on Software Engineering ( Volume: 48, Issue: 7, 01 July 2022)
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- IEEE Keywords
- Index Terms
- Adaptive Algorithm ,
- Bug Localization ,
- Buggy Files ,
- Source Code ,
- Feature Weights ,
- Numerous Scientists ,
- Bug Reports ,
- Learning To Rank ,
- Comparable Results ,
- Training Set ,
- Scoring Function ,
- Mean Average Precision ,
- Software Defect ,
- Latent Dirichlet Allocation ,
- Target Training ,
- Project Characteristics ,
- Ranking Algorithm ,
- Dependency Graph ,
- Pairwise Approach ,
- Training Folds ,
- Source Code Files ,
- Vector Space Model ,
- Percentage Of Reports ,
- Software Repositories ,
- Version Control System ,
- Cut-off Function ,
- Maximum Absolute Difference ,
- Bug Fixes ,
- Adaptive Approach
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Adaptive Algorithm ,
- Bug Localization ,
- Buggy Files ,
- Source Code ,
- Feature Weights ,
- Numerous Scientists ,
- Bug Reports ,
- Learning To Rank ,
- Comparable Results ,
- Training Set ,
- Scoring Function ,
- Mean Average Precision ,
- Software Defect ,
- Latent Dirichlet Allocation ,
- Target Training ,
- Project Characteristics ,
- Ranking Algorithm ,
- Dependency Graph ,
- Pairwise Approach ,
- Training Folds ,
- Source Code Files ,
- Vector Space Model ,
- Percentage Of Reports ,
- Software Repositories ,
- Version Control System ,
- Cut-off Function ,
- Maximum Absolute Difference ,
- Bug Fixes ,
- Adaptive Approach
- Author Keywords