Value of quality in software is well recognised. Poor quality can incur a lot of cost during various phases in software cycle and can bring disaster to a business. Open source software can be used as a valid test case for the assessment of software characteristics.
Moreover, the powerful technique of data mining can be used to extract the characteristics of software and deal with possible bugs. A proper software testing can unveil a wide range of malfunctions that might happen to a software. Many tools have emerged to monitor the behaviour of a software. As more and more large and complex software are coming into picture, striving for the highest quality software without any defects is more important now than ever before.
Data Mining is defined as a process used to extract usable data from a larger set of any raw data. It implies analysing data patterns in large batches of data using one or more software. Some of the common and effective techniques of data mining include Support Vector Machine (SVM), Random Forest (RF), Boosting and Naïve Bayes (NB). The output of these methods could help in detecting violation of patterns and find the bugs. This powerful way of Data Mining therefore perform the essential function of Software testing and maintenance.