Cross-Platform Bug Localization Strategies: Utilizing Machine Learning for Diverse Software Environment Adaptability
This paper introduces a novel hybrid machine learning model that combines Long Short-Term Memory (LSTM) networks and SHapley Additive exPlanations (SHAP) to enhance bug localization across multiple software platforms. The aim is to adapt to the variability inherent in different operating systems and...
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| Format: | Article |
| Language: | English |
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Engiscience Publisher
2024-04-01
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| Series: | Emerging Technologies and Engineering Journal |
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| Online Access: | https://engiscience.com/index.php/etej/article/view/219 |
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| author | Waqas Ali Mariam Sabir |
| author_facet | Waqas Ali Mariam Sabir |
| author_sort | Waqas Ali |
| collection | DOAJ |
| description | This paper introduces a novel hybrid machine learning model that combines Long Short-Term Memory (LSTM) networks and SHapley Additive exPlanations (SHAP) to enhance bug localization across multiple software platforms. The aim is to adapt to the variability inherent in different operating systems and provide transparent, interpretable results for software developers. Our methodology includes comprehensive preprocessing of bug report data using advanced natural language processing techniques, followed by feature extraction through word embeddings to accommodate the sequential nature of text data. The LSTM model is trained and evaluated on a dataset of simulated bug reports, with the results interpreted using SHAP values to ensure clarity in decision-making. The results demonstrate the model’s robustness, adaptability, and consistent performance across platforms, as evidenced by accuracy, precision, recall, and F1 scores. The dataset's distribution of bug categories and statuses further provides valuable insights into common software development issues. |
| format | Article |
| id | doaj-art-f2c2038f63294c0ba0da0e9a0eaf1b8e |
| institution | Kabale University |
| issn | 3007-2875 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Engiscience Publisher |
| record_format | Article |
| series | Emerging Technologies and Engineering Journal |
| spelling | doaj-art-f2c2038f63294c0ba0da0e9a0eaf1b8e2024-12-14T14:31:20ZengEngiscience PublisherEmerging Technologies and Engineering Journal3007-28752024-04-0111152510.53898/etej2024112219Cross-Platform Bug Localization Strategies: Utilizing Machine Learning for Diverse Software Environment AdaptabilityWaqas Ali0Mariam Sabir1https://orcid.org/0009-0001-7133-6818School of Information Engineering, Yangzhou University, Yangzhou, 225000, ChinaFaculty of Agriculture, University of Agriculture Faisalabad, Faisalabad, 38000, PakistanThis paper introduces a novel hybrid machine learning model that combines Long Short-Term Memory (LSTM) networks and SHapley Additive exPlanations (SHAP) to enhance bug localization across multiple software platforms. The aim is to adapt to the variability inherent in different operating systems and provide transparent, interpretable results for software developers. Our methodology includes comprehensive preprocessing of bug report data using advanced natural language processing techniques, followed by feature extraction through word embeddings to accommodate the sequential nature of text data. The LSTM model is trained and evaluated on a dataset of simulated bug reports, with the results interpreted using SHAP values to ensure clarity in decision-making. The results demonstrate the model’s robustness, adaptability, and consistent performance across platforms, as evidenced by accuracy, precision, recall, and F1 scores. The dataset's distribution of bug categories and statuses further provides valuable insights into common software development issues.https://engiscience.com/index.php/etej/article/view/219machine learningbug localizationcross-platform software lstm networksexplainable ai (xai) shap valuesnatural language processingsoftware development feature engineering |
| spellingShingle | Waqas Ali Mariam Sabir Cross-Platform Bug Localization Strategies: Utilizing Machine Learning for Diverse Software Environment Adaptability Emerging Technologies and Engineering Journal machine learning bug localization cross-platform software lstm networks explainable ai (xai) shap values natural language processing software development feature engineering |
| title | Cross-Platform Bug Localization Strategies: Utilizing Machine Learning for Diverse Software Environment Adaptability |
| title_full | Cross-Platform Bug Localization Strategies: Utilizing Machine Learning for Diverse Software Environment Adaptability |
| title_fullStr | Cross-Platform Bug Localization Strategies: Utilizing Machine Learning for Diverse Software Environment Adaptability |
| title_full_unstemmed | Cross-Platform Bug Localization Strategies: Utilizing Machine Learning for Diverse Software Environment Adaptability |
| title_short | Cross-Platform Bug Localization Strategies: Utilizing Machine Learning for Diverse Software Environment Adaptability |
| title_sort | cross platform bug localization strategies utilizing machine learning for diverse software environment adaptability |
| topic | machine learning bug localization cross-platform software lstm networks explainable ai (xai) shap values natural language processing software development feature engineering |
| url | https://engiscience.com/index.php/etej/article/view/219 |
| work_keys_str_mv | AT waqasali crossplatformbuglocalizationstrategiesutilizingmachinelearningfordiversesoftwareenvironmentadaptability AT mariamsabir crossplatformbuglocalizationstrategiesutilizingmachinelearningfordiversesoftwareenvironmentadaptability |