Zoe
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Machine Learning for Software Engineering
A Tertiary Study
Machine learning (ML) techniques increase the effectiveness of software engineering (SE) lifecycle activities. We systematically collected, quality-assessed, summarized, and categorized 83 reviews in ML for SE published between 2009 and 2022, covering 6,117 primary studies. Th ...
The establishment of the Mining Software Repositories (MSR) data showcase conference track has encouraged researchers to provide data sets as a basis for further empirical studies. The objective of this study is to examine the usage of data papers published in the MSR proceedi ...
GitHub projects can be easily replicated through the site's fork process or through a Git clone-push sequence. This is a problem for empirical software engineering, because it can lead to skewed results or mistrained machine learning models. We provide a dataset of 10.6 millio ...
In order to understand the state and evolution of the entirety of open source software we need to get a handle on the set of distinct software projects. Most of open source projects presently utilize Git, which is a distributed version control system allowing easy creation of ...
We present a dataset of open source software developed mainly by enterprises rather than volunteers. This can be used to address known generalizability concerns, and, also, to perform research on open source business software development. Based on the premise that an enterpris ...
Introduction: The establishment of the Mining Software Repositories (MSR) Data Showcase conference track has encouraged researchers to provide more data sets as a basis for further empirical studies. Objectives: Examine the usage of the data papers published in the MSR proceed ...