An empirical study on the potential of word embedding techniques in bug report management tasks
Context Representing the textual semantics of bug reports is a key component of bug report management (BRM) techniques. Existing studies mainly use classical information retrieval-based (IR-based) approaches, such as the vector space model (VSM) to do semantic extraction. Little attention is paid to...
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| Vydáno v: | Empirical software engineering : an international journal Ročník 29; číslo 5; s. 122 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Springer US
01.09.2024
Springer Nature B.V |
| Témata: | |
| ISSN: | 1382-3256, 1573-7616 |
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| Abstract | Context
Representing the textual semantics of bug reports is a key component of bug report management (BRM) techniques. Existing studies mainly use classical information retrieval-based (IR-based) approaches, such as the vector space model (VSM) to do semantic extraction. Little attention is paid to exploring whether word embedding (WE) models from the natural language process could help BRM tasks.
Objective
To have a general view of the potential of word embedding models in representing the semantics of bug reports and attempt to provide some actionable guidelines in using semantic retrieval models for BRM tasks.
Method
We studied the efficacy of five widely recognized WE models for six BRM tasks on 20 widely-used products from the Eclipse and Mozilla foundations. Specifically, we first explored the suitable machine learning techniques under the use of WE models and the suitable WE model for BRM tasks. Then we studied whether WE models performed better than classical VSM. Last, we investigated whether WE models fine-tuned with bug reports outperformed general pre-trained WE models.
Key Results
The Random Forest (RF) classifier outperformed other typical classifiers under the use of different WE models in semantic extraction.We rarely observed statistically significant performance differences among five WE models in five BRM classification tasks, but we found that small-dimensional WE models performed better than larger ones in the duplicate bug report detection task. Among three BRM tasks (i.e., bug severity prediction, reopened bug prediction, and duplicate bug report detection) that showed statistically significant performance differences, VSM outperformed the studied WE models. We did not find performance improvement after we fine-tuned general pre-trained BERT with bug report data.
Conclusion
Performance improvements of using pre-trained WE models were not observed in studied BRM tasks. The combination of RF and traditional VSM was found to achieve the best performance in various BRM tasks. |
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| AbstractList | Context
Representing the textual semantics of bug reports is a key component of bug report management (BRM) techniques. Existing studies mainly use classical information retrieval-based (IR-based) approaches, such as the vector space model (VSM) to do semantic extraction. Little attention is paid to exploring whether word embedding (WE) models from the natural language process could help BRM tasks.
Objective
To have a general view of the potential of word embedding models in representing the semantics of bug reports and attempt to provide some actionable guidelines in using semantic retrieval models for BRM tasks.
Method
We studied the efficacy of five widely recognized WE models for six BRM tasks on 20 widely-used products from the Eclipse and Mozilla foundations. Specifically, we first explored the suitable machine learning techniques under the use of WE models and the suitable WE model for BRM tasks. Then we studied whether WE models performed better than classical VSM. Last, we investigated whether WE models fine-tuned with bug reports outperformed general pre-trained WE models.
Key Results
The Random Forest (RF) classifier outperformed other typical classifiers under the use of different WE models in semantic extraction.We rarely observed statistically significant performance differences among five WE models in five BRM classification tasks, but we found that small-dimensional WE models performed better than larger ones in the duplicate bug report detection task. Among three BRM tasks (i.e., bug severity prediction, reopened bug prediction, and duplicate bug report detection) that showed statistically significant performance differences, VSM outperformed the studied WE models. We did not find performance improvement after we fine-tuned general pre-trained BERT with bug report data.
Conclusion
Performance improvements of using pre-trained WE models were not observed in studied BRM tasks. The combination of RF and traditional VSM was found to achieve the best performance in various BRM tasks. ContextRepresenting the textual semantics of bug reports is a key component of bug report management (BRM) techniques. Existing studies mainly use classical information retrieval-based (IR-based) approaches, such as the vector space model (VSM) to do semantic extraction. Little attention is paid to exploring whether word embedding (WE) models from the natural language process could help BRM tasks.ObjectiveTo have a general view of the potential of word embedding models in representing the semantics of bug reports and attempt to provide some actionable guidelines in using semantic retrieval models for BRM tasks.MethodWe studied the efficacy of five widely recognized WE models for six BRM tasks on 20 widely-used products from the Eclipse and Mozilla foundations. Specifically, we first explored the suitable machine learning techniques under the use of WE models and the suitable WE model for BRM tasks. Then we studied whether WE models performed better than classical VSM. Last, we investigated whether WE models fine-tuned with bug reports outperformed general pre-trained WE models.Key ResultsThe Random Forest (RF) classifier outperformed other typical classifiers under the use of different WE models in semantic extraction.We rarely observed statistically significant performance differences among five WE models in five BRM classification tasks, but we found that small-dimensional WE models performed better than larger ones in the duplicate bug report detection task. Among three BRM tasks (i.e., bug severity prediction, reopened bug prediction, and duplicate bug report detection) that showed statistically significant performance differences, VSM outperformed the studied WE models. We did not find performance improvement after we fine-tuned general pre-trained BERT with bug report data.ConclusionPerformance improvements of using pre-trained WE models were not observed in studied BRM tasks. The combination of RF and traditional VSM was found to achieve the best performance in various BRM tasks. |
| ArticleNumber | 122 |
| Author | Chen, Bingting Cai, Biyu Chen, Lin Zou, Weiqin Meng, Qianshuang Liu, Wenjie Li, Piji |
| Author_xml | – sequence: 1 givenname: Bingting surname: Chen fullname: Chen, Bingting organization: Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics – sequence: 2 givenname: Weiqin surname: Zou fullname: Zou, Weiqin email: weiqin@nuaa.edu.cn organization: Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, State Key Lab. for Novel Software Technology, Nanjing University – sequence: 3 givenname: Biyu surname: Cai fullname: Cai, Biyu organization: Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics – sequence: 4 givenname: Qianshuang surname: Meng fullname: Meng, Qianshuang organization: Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics – sequence: 5 givenname: Wenjie surname: Liu fullname: Liu, Wenjie organization: Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics – sequence: 6 givenname: Piji surname: Li fullname: Li, Piji organization: Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics – sequence: 7 givenname: Lin surname: Chen fullname: Chen, Lin organization: Department of Computer Science and Technology, Nanjing University |
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| Keywords | Pre-trained models Bug report Word embedding Vector space model |
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| Title | An empirical study on the potential of word embedding techniques in bug report management tasks |
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