Abstract:
Defect severity assessment is the most crucial step in major companies and organizations
where the complexity of the software is growing at an exponential rate. One of most active
research areas in software engineering is software defect prediction (SDP). SDP is a
technique for envisioning software defects. Early defect discovery during the software
development life cycle (SDLC) results in early repairs and ultimately on-time delivery of
maintainable software, which pleases the client and fosters his confidence in the
development team. By consistently predicting bugs, removing bugs, and identifying defect
modules, the software industry aims to improve the quality of its products. The requirement
for high-quality and affordable software that can be maintained is growing as the need for
automated online software systems rises daily. Predicting software defects is one of the
major goals of the quality assurance process, which improves quality while reducing costs
by reducing overall testing and maintenance activities.
Artificial Neural Networks (ANN), one of the commonly utilized machine learning
approaches, are used in the majority of suggested frameworks and models for defect
prediction. Using Artificial Neural Networks (ANN) approach, Laverberg-Marquardt
(LM) and Bayesian Regularization (BR) we can simply predict software defect. To use the
MATLAB simulation tool, a framework is built and used to the NASA software dataset
being considered for performance study. By considering the performance I can select which
ANN method algorithm is more perfect in predicting software defect. This study will
benefit the researchers and serve as a benchmark for future improvements, analyses, and
evaluations. An experimental investigation demonstrates that the suggested approach can
offer superior performance for predicting software defects.
