Data Mining Techniques for Software Quality Prediction in Open Source Software

Abstract

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. *

Neural-Network Model of Software Quality Prediction Based on Quality Attributes

“…The paper [19] aims to compare different methods of data mining (for example, obtained by machine learning) to develop effective models for predicting software quality. To achieve this, the authors addressed various issues, such as collecting software metrics from open source repositories, evaluating forecasting models to identify software problems, and implementing statistical methods to evaluate data mining techniques.…”