In presence of multiple objectives to be optimized in Search-Based Software Engineering
(SBSE), Pareto search has been commonly adopted. It searches for a good approximation of …

Studies in software effort estimation (SEE) have explored the use of hyper-parameter tuning
for machine learning algorithms (MLA) to improve the accuracy of effort estimates. In other …

Software Bug Prediction (SBP) is an integral process to the software's success that involves
predicting software bugs before their occurrence. Detecting software bugs early in the …

To ease the expensive measurements during configuration tuning, it is natural to build a
surrogate model as the replacement of the system, and thereby the configuration …

Context: Machine learning software can generate models that inappropriately discriminate
against specific protected social groups (eg, groups based on gender, ethnicity, etc.) …

Machine learning techniques applied to software engineering tasks can be improved by
hyperparameter optimization, ie, automatic tools that find good settings for a learner's control …

The immense increase in software technology has resulted in the convolution of software
projects. Software effort estimation is fundamental to commence any software project and …

Context: An analogy-based software effort estimation technique estimates the required effort
for a new software project based on the total effort used in completing past similar projects …

When security bugs are detected, they should be (a)~ discussed privately by security
software engineers; and (b)~ not mentioned to the general public until security patches are …

Learning and predicting the performance of a configurable software system helps to provide
better quality assurance. One important engineering decision therein is how to encode the …