Software Defect Prediction Based on Effective Fusion of Multiple Features
In the field of software engineering, the goal of software defect prediction is to accelerate the detection of potential defective modules in products, thereby enhancing the efficiency of allocating testing resources effectively. Traditional software defect prediction models typically rely on a sing...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10549525/ |
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| author | Chaozheng Zhang Junhua Wu |
| author_facet | Chaozheng Zhang Junhua Wu |
| author_sort | Chaozheng Zhang |
| collection | DOAJ |
| description | In the field of software engineering, the goal of software defect prediction is to accelerate the detection of potential defective modules in products, thereby enhancing the efficiency of allocating testing resources effectively. Traditional software defect prediction models typically rely on a single feature or perform simple aggregation of multiple features during training. However, the former overlooks the potential contributions of other features in defect prediction, while the latter fails to fully exploit the value of different features in the model. Therefore, this paper proposes a software defect prediction model based on the effective fusion of multiple features, named DP-SSCT (Semantics, Source Code, and Tradition). The model integrates traditional features, source code features, and semantic features. To determine the importance of each feature in the model, a gate fusion mechanism and attention mechanism are introduced to capture significant information in the fused features. The proposed model is evaluated on nine open-source Java project samples from the PROMISE dataset. Experimental results demonstrate that the model outperforms existing prediction models, exhibiting superior performance and predictive capability. |
| format | Article |
| id | doaj-art-7d08e912745842189d8a0d87ab2e0a7d |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7d08e912745842189d8a0d87ab2e0a7d2025-08-20T01:52:03ZengIEEEIEEE Access2169-35362025-01-0113574555746410.1109/ACCESS.2024.340970910549525Software Defect Prediction Based on Effective Fusion of Multiple FeaturesChaozheng Zhang0https://orcid.org/0009-0005-8076-6894Junhua Wu1Nanjing Tech University, Nanjing, ChinaNanjing Tech University, Nanjing, ChinaIn the field of software engineering, the goal of software defect prediction is to accelerate the detection of potential defective modules in products, thereby enhancing the efficiency of allocating testing resources effectively. Traditional software defect prediction models typically rely on a single feature or perform simple aggregation of multiple features during training. However, the former overlooks the potential contributions of other features in defect prediction, while the latter fails to fully exploit the value of different features in the model. Therefore, this paper proposes a software defect prediction model based on the effective fusion of multiple features, named DP-SSCT (Semantics, Source Code, and Tradition). The model integrates traditional features, source code features, and semantic features. To determine the importance of each feature in the model, a gate fusion mechanism and attention mechanism are introduced to capture significant information in the fused features. The proposed model is evaluated on nine open-source Java project samples from the PROMISE dataset. Experimental results demonstrate that the model outperforms existing prediction models, exhibiting superior performance and predictive capability.https://ieeexplore.ieee.org/document/10549525/Software defect predictionconvolutional neural networklong short-term memory (LSTM)bidirectional long short-term memory |
| spellingShingle | Chaozheng Zhang Junhua Wu Software Defect Prediction Based on Effective Fusion of Multiple Features IEEE Access Software defect prediction convolutional neural network long short-term memory (LSTM) bidirectional long short-term memory |
| title | Software Defect Prediction Based on Effective Fusion of Multiple Features |
| title_full | Software Defect Prediction Based on Effective Fusion of Multiple Features |
| title_fullStr | Software Defect Prediction Based on Effective Fusion of Multiple Features |
| title_full_unstemmed | Software Defect Prediction Based on Effective Fusion of Multiple Features |
| title_short | Software Defect Prediction Based on Effective Fusion of Multiple Features |
| title_sort | software defect prediction based on effective fusion of multiple features |
| topic | Software defect prediction convolutional neural network long short-term memory (LSTM) bidirectional long short-term memory |
| url | https://ieeexplore.ieee.org/document/10549525/ |
| work_keys_str_mv | AT chaozhengzhang softwaredefectpredictionbasedoneffectivefusionofmultiplefeatures AT junhuawu softwaredefectpredictionbasedoneffectivefusionofmultiplefeatures |