Abstract:

Over the years, numerous software defect prediction models have been developed to tackle the challenges inherent in software project development. Emphasizing software reliability is crucial for enhancing overall software quality, as it involves the analysis and projection of software quality based on defect prediction. Many software enterprises are actively striving to enhance software quality while concurrently reducing software development expenses. Among the diverse models available, the Pareto Type IV model stands out as a pivotal approach for scrutinizing software flaws using generated data. Complementing this, Statistical Process Control (SPC) emerges as a statistical technique offering contextual quality assurance. In this research endeavor, an enhanced software defect prediction model takes center stage, serving to anticipate errors that materialize during distinct phases of software development. This innovative model seeks to leverage insights from historical data, refined algorithms, and a comprehensive understanding of influencing factors. Through such proactive measures, the goal is to optimize software quality, minimize defects, and streamline the software development lifecycle

Description:

Pareto type IV distribution model, NHPP, MLE method, Statistical Process Control, Order Statistics.