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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10549525/ |
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| Summary: | 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. |
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| ISSN: | 2169-3536 |