Implementation of a software product is wholly dependent on the quality of the software developed. During the development process, it is difficult for software developers to anticipate the quality of a software product prior to its implementation in real-world applications. Despite this, few studies have been conducted on this topic to date. Most researchers have focused their efforts on predicting software quality using various machine-learning techniques. In addition, software quality prediction must be performed early in the software development life cycle to minimize the developer's effort in creating the software product. In this paper, we conduct an in-depth analysis of the machine-learning techniques used to predict software quality.
Ray, Niharika; Pattnaik, Saumendra; Pattanayak, Binod Kumar; and Dash, Lucy
"Exploring Machine Learning Techniques for Accurate Software Quality Prediction in Software Development,"
International Journal of Computer and Communication Technology: Vol. 9:
2, Article 3.
Available at: https://www.interscience.in/ijcct/vol9/iss2/3