Deep learning for software defect prediction

Abstract

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.