Deep learning for software defect prediction

What is it about?

One of the most important jobs in the SDLC’s Testing Phase is software defect prediction. It determines which modules are prone to failure and require extensive testing. This allows for efficient use of testing resources while staying within the limits. Predicting software faults is a significant and effective method for increasing software quality and reliability. Modeling which components in a large software system will have the most difficulties in the next release helps project managers better manage projects, such as early detection of potential release delays and cost-effectively guiding remedial actions to improve software quality. Developing robust fault prediction models, on the other hand, is a difficult task, and numerous solutions have been proposed in the literature. In this survey, we look at convolutional neural networks, defect prediction via attention mechanism, and other deep learning techniques that can help detect software defects. Program defect prediction is used to aid developers in spotting probable flaws and prioritizing their testing efforts in order to increase software reliability.

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Why is it important?

Software defect prediction techniques are commonly used in code reviews to help developers and testers discover potential mistakes, and defect prediction has developed as an important study field in software engineering. The static code metrics value of a problematic code snippet may be the same as that of a clean one, making it impossible to tell which is the defective one. As a result, semantic data buried in software is more representative than data obtained from other sources. It should be used to improve the accuracy of a model based on static measurements.