| Issue |
EPJ Web Conf.
Volume 214, 2019
23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
|
|
|---|---|---|
| Article Number | 05007 | |
| Number of page(s) | 8 | |
| Section | T5 - Software development | |
| DOI | https://doi.org/10.1051/epjconf/201921405007 | |
| Published online | 17 September 2019 | |
https://doi.org/10.1051/epjconf/201921405007
Data Mining Techniques for Software Quality Prediction in Open Source Software
An Initial Assessment
INFN-CNAF Viale Berti Pichat 6/2,
40126,
Bologna
* e-mail: marco.canaparo@cnaf.infn.it
** e-mail: elisabetta.ronchieri@cnaf.infn.it
Published online: 17 September 2019
Software quality monitoring and analysis are among the most productive topics in software engineering research. Their results may be effectively employed by engineers during software development life cycle. Open source software constitutes a valid test case for the assessment of software characteristics. The data mining approach has been proposed in literature to extract software characteristics from software engineering data.
This paper aims at comparing diverse data mining techniques (e.g., derived from machine learning) for developing effective software quality prediction models. To achieve this goal, we tackled various issues, such as the collection of software metrics from open source repositories, the assessment of prediction models to detect software issues and the adoption of statistical methods to evaluate data mining techniques. The results of this study aspire to identify the data mining techniques that perform better amongst all the ones used in this paper for software quality prediction models.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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