By creating an effective prediction model, software defect prediction seeks to predict
potential flaws in new software modules in advance. However, unnecessary and duplicated …

Redundancy, correlation, feature irrelevance, and missing samples are just a few problems
that make it difficult to analyze software defect data. Additionally, it might be challenging to …

The chapter examines ML methods that appear to be applied in implementing systems with
intelligent behaviour. It depends on two workshops on learning in system of intelligent …

Software failure prediction is a crucial task in software quality assurance. Although time
series forecasting techniques and conventional software reliability models are used for the …

Big data delivers high velocity, high volume, and high veracity data in vast quantities.
Analyzing and handling this data requires new software, fast and high-capacity hardware …

Context Software reliability<? show [AQ ID= GQ2 POS=-24pt]?><? show [AQ ID= GQ5 POS=
12pt]?> prediction uses historical data analysis to identify software components prone to …

This paper presents an ensemble model for software defect prediction using methodlevel
features of a Spring Framework open-source Java project called Broadleaf Commerce. The …

Software defect prediction is pivotal for ensuring the reliability of new software systems. This
study introduces an innovative approach to the domain, leveraging software reliability …

The emergence of constant sicknesses like COVID-19 has re-underlined the need for
residents all around the world to approach pressing healthcare. The recent pandemic has …

Sažetak Software reliability frameworks are extremely effective for estimating the probability
of software failure over time. Numerous approaches for predicting software dependability …