Software effort estimation accuracy is a key factor in effective planning, controlling, and
delivering a successful software project within budget and schedule. The overestimation and …

The software engineering community is rapidly adopting machine learning for transitioning
modern-day software towards highly intelligent and self-learning systems. However, the …

Classifier specific (CS) and classifier agnostic (CA) feature importance methods are widely
used (often interchangeably) by prior studies to derive feature importance ranks from a …

At an early stage in the development process, a development team must obtain insight into
the software being developed to establish a reliable plan. Thus, the team members should …

Context: Over the last decade, Agile methods have changed the software development
process in an unparalleled way and with the increasing popularity of Big Data, optimizing …

Predictive models are one of the most important techniques that are widely applied in many
areas of software engineering. There have been a large number of primary studies that …

Early effort estimation is important for efficiently planning the use of resources in an
Information Technology (IT) project. However, limited research has been conducted on the …

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 …

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 …

Currently, in Scrum, there are different methods to estimate user stories in terms of effort or
complexity. Most of the existing techniques consider factors in a fine grain level; these …