Software fault prediction (SFP) aims to improve software quality with a possible minimum
cost and time. Various machine learning models have been proposed in the past for …

Data preprocessing remains an important step in machine learning studies. This is because
proper preprocessing of imbalanced data can enable researchers to reduce defects as …

System-on-Chips (SoCs) form the crux of modern computing systems. SoCs enable high-
level integration through the utilization of multiple Intellectual Property (IP) cores. However …

Software dependency network metrics extracted from the dependency graph of the software
modules by the application of Social Network Analysis (SNA metrics) have been shown to …

Software fault prediction aims to improve software quality by anticipating faults early in the
software development process. It is possible to anticipate software faults with the aid of …

Improving software quality by predicting faults during the early stages of software
development is a primary goal of software fault prediction (SFP). Various machine learning …

Software evolution is an important activity in the life cycle of a modern software system. In
the process of software evolution, the repair of historical defects and the increasing …

Streaming data analysis faces two primary challenges: concept drifts and class imbalance.
The co-occurrence of virtual drifts and class imbalance is a common real-world scenario …

Abstract In software development, Software Fault Prediction (SFP) is essential for optimising
resource allocation and improving testing efficiency. Traditional SFP methods typically use …

Aiming at the problem that the accuracy of the results obtained by traditional methods is low,
the spectral detection in soil pollution component detection method is used to design …