Several prediction approaches are contained in the arena of software engineering such as
prediction of effort, security, quality, fault, cost, and re-usability. All these prediction …

Abstract Interest in Machine Learning applications to tackle clinical and biological problems
is increasing. This is driven by promising results reported in many research papers, the …

Abstract Machine learning models always make a prediction, even when it is likely to be
inaccurate. This behavior should be avoided in many decision support applications, where …

Context Software defect prediction strives to detect defect-prone software modules by mining
the historical data. Effective prediction enables reasonable testing resource allocation …

Existing machine learning solutions for network-based intrusion detection cannot maintain
their reliability over time when facing high-speed networks and evolving attacks. In this …

Software defect prediction aims at helping developers allocate existing resources by
predicting defect-prone modules prior to the testing phase. In the past decade, cross-project …

Software reliability is an important attribute of software quality. To achieve higher reliability,
software development must include a testing phase in which faults can be detected and …

Unlike most other software quality attributes, testability cannot be evaluated solely based on
the characteristics of the source code. The effectiveness of the test suite and the budget …

Although testing effort estimation is a very important task in software project management, it
is rarely described in the literature. There are many difficulties in finding any useful methods …

Predicting defects during software testing reduces an enormous amount of testing effort and
help to deliver a high‐quality software system. Owing to the skewed distribution of public …