In the dynamic realm of technology, the fusion of Artificial Intelligence (AI) and Machine
Learning (ML) with DevSecOps practices stands out as a pivotal catalyst for bolstering …

Software testing is an important task in software development activities, and it requires most
of the resources, namely, time, cost and effort. To minimize this fatigue, software bug …

Prior identification of defects in software modules can help testers to allocate limited
resources efficiently. Defect prediction techniques are helpful for this situation because they …

This open access book provides a wealth of hands-on examples that illustrate how
hyperparameter tuning can be applied in practice and gives deep insights into the working …

Software defect prediction refers to the automatic identification of defective parts of software
through machine learning techniques. Ensemble learning has exhibited excellent prediction …

Effort-Aware Defect Prediction (EADP) ranks software modules based on the possibility of
these modules being defective, their predicted number of defects, or defect density by using …

We assert that it is the ethical duty of software engineers to strive to reduce software
discrimination. This paper discusses how that might be done. This is an important topic since …

Prior research has shown that cryptography is hard to use for developers. We aim to
understand what cryptography issues developers face in practice. We clustered 91 954 …

Significance: Physiological parameters extracted from diffuse reflectance spectroscopy
(DRS) provide clinicians quantitative information about tissue that helps aid in diagnosis …

The goal of this paper is to propose a hybrid technique to improve Support Vector Machines
classification accuracy using training data sampling and hyperparameter tuning. The …