Do Not Take It for Granted: Comparing Open-Source Libraries for Software Development Effort Estimation

Understand

In the past two decades, several Machine Learning (ML) libraries have become freely available.

• Many studies have used such libraries to carry out empirical investigations on predictive Software Engineering (SE) tasks.
• However, the differences stemming from using one library over another have been overlooked, implicitly assuming that using any of these libraries would provide the user with the same or very similar results.
• This paper aims at raising awareness of the differences incurred when using different ML libraries for software development effort estimation (SEE), one of most widely studied SE prediction tasks.