APPLICABILITY OF SEQUENTIAL MODELS IN SOFTWARE DEFECT PREDICTION

Abstract

This study conducts a comprehensive literature review on Software Defect with a focus on the application of sequential models. Software defects can have severe repercussions, ranging from system failures to security vulnerabilities, necessitating the development of effective defect prediction techniques. While traditional methods have relied on handcrafted features and statistical models, the emergence of sequential models in the form of LSTM, RNN, and GRU, originally designed for sequence data, has garnered attention in the software engineering field. This review aims to provide an in-depth analysis of existing research on the use of sequential models for SDP. The study highlights methodologies, datasets, evaluation metrics, advantages, and challenges encountered in applying LSTM, RNN, and GRU for this purpose. The findings of this review are intended to guide researchers and practitioners in leveraging sequential models to enhance software quality. SDP plays a crucial role in software quality assurance, aiming to identify and address potential issues before they lead to failures or other adverse consequences. Traditional techniques often face limitations in capturing complex relationships between software artifacts and defects. Sequential models have emerged as promising alternatives for defect prediction tasks, showcasing improved performance and adaptability. This paper provides an introduction to software defect prediction, highlighting its importance and the challenges it poses, along with conventional approaches. We then delve into sequential models, discussing their principles, structure, functionality, and advantages in the context of defect prediction. Our review assesses current research on LSTM, RNN, and GRU-based software defect prediction, examining key contributions, methodologies, and results. We also explore various aspects of sequential models, including data pre-processing, network architecture design, and performance evaluation metrics. Through this analysis, we identify the strengths and limitations of these models, as well as the challenges and open research questions in the field. We conclude by highlighting areas for improvement in terms of interpretability, scalability, and robustness of these models. Finally, we propose future research directions and potential avenues for advancements in software defect prediction using sequential. These recommendations include the exploration of novel network architectures, the incorporation of domain-specific knowledge, and the development of hybrid models that combine the strengths of both traditional and deep learning techniques. Emphasis is also placed on the need for extensive empirical studies and benchmarking efforts to facilitate the comparison and evaluation of different sequential model approaches.