Unlocking the Potential of AI/ML in DevSecOps: Effective Strategies and Optimal Practices
NG Camacho - Journal of Artificial Intelligence General science …, 2024 - ojs.boulibrary.com
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 …
Learning (ML) with DevSecOps practices stands out as a pivotal catalyst for bolstering …
A systematic review of hyperparameter tuning techniques for software quality prediction models
BACKGROUND: Software quality prediction models play a crucial role in identifying
vulnerable software components during early stages of development, and thereby …
vulnerable software components during early stages of development, and thereby …
Hyper-parameter optimization of classifiers, using an artificial immune network and its application to software bug prediction
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 …
of the resources, namely, time, cost and effort. To minimize this fatigue, software bug …
Empirical investigation of hyperparameter optimization for software defect count prediction
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 …
resources efficiently. Defect prediction techniques are helpful for this situation because they …
[LIBRO][B] Hyperparameter tuning for machine and deep learning with R: A practical guide
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 …
hyperparameter tuning can be applied in practice and gives deep insights into the working …
Software defect prediction using stacking generalization of optimized tree-based ensembles
Software defect prediction refers to the automatic identification of defective parts of software
through machine learning techniques. Ensemble learning has exhibited excellent prediction …
through machine learning techniques. Ensemble learning has exhibited excellent prediction …
An empirical study of learning to rank techniques for effort-aware defect 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 …
these modules being defective, their predicted number of defects, or defect density by using …
Software engineering for fairness: A case study with hyperparameter optimization
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 …
discrimination. This paper discusses how that might be done. This is an important topic since …
Hurdles for developers in cryptography
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 …
understand what cryptography issues developers face in practice. We clustered 91 954 …
Machine learning to extract physiological parameters from multispectral diffuse reflectance spectroscopy
Significance: Physiological parameters extracted from diffuse reflectance spectroscopy
(DRS) provide clinicians quantitative information about tissue that helps aid in diagnosis …
(DRS) provide clinicians quantitative information about tissue that helps aid in diagnosis …