Leveraging an Enhanced CodeBERT-Based Model for Multiclass Software Defect Prediction via Defect Classification
Ensuring software reliability through early-stage defect prevention and prediction is crucial, particularly as software systems become increasingly complex. Automated testing has emerged as the most practical approach to achieving bug-free and efficient code. In this context, machine learning-driven...
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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/10820528/ |
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| Summary: | Ensuring software reliability through early-stage defect prevention and prediction is crucial, particularly as software systems become increasingly complex. Automated testing has emerged as the most practical approach to achieving bug-free and efficient code. In this context, machine learning-driven methods, especially those leveraging natural language models, have gained significant traction for developing effective techniques. This paper introduces a novel framework for automating software defect prediction, focusing on eight specific defects: SIGFPE, NZEC, LOGICAL, SYNTAX, SIGSEGV, SIGABRT, SEMANTIC, and LINKER. Our research involves a specialized dataset comprising nine classes, including eight common programming errors and one error-free class. The goal is to enhance software testing and development processes by identifying defects within code snippets. The proposed framework utilizes a CodeBERT-based algorithm for defect prediction, optimizing model hyperparameters to achieve superior accuracy. Comparative analysis against established models such as RoBERTa, Microsoft CodeBERT, and GPT-2 demonstrates that our approach yields significant improvements in prediction performance, with accuracy gains of up to 20% and 7% respectively in binary and multi class experimentation. Empirical studies validate the effectiveness of neural language models like CodeBERT for software defect prediction, highlighting substantial advancements in software testing and development techniques. These findings underscore the potential benefits of incorporating advanced machine learning models into the software development lifecycle. |
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| ISSN: | 2169-3536 |