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Automatic Real-Time Mining Software Process Activities From SVN Logs Using a Naive Bayes Classifier

2019; Institute of Electrical and Electronics Engineers; Volume: 7; Linguagem: Inglês

10.1109/access.2019.2945608

2169-3536

Rui Zhu, Yichao Dai, Tong Li, Zifei Ma, Ming Zheng, Yahui Tang, Jiayi Yuan, Y. Huang,

Software Engineering Techniques and Practices

The abundance of event data in current software configuration management systems makes it possible to discover software process models automatically by using actual observed behavior. However, traditional process mining algorithms cannot be applied to event logs recorded in software configuration management (SCM) systems, such as SVN, because of missing activity attributes. To address this problem, a software process activity classifier is proposed to build event-activity mapping relationships from software development event streams, revealing activity attributes and associating the activity to the original SVN log. The proposed approach extracts activity from the SVN log based on semantic features and introduces a novel technique based on a naive Bayes approach to associate event activities dynamically. The approach has been applied to two real-world software development process logs, ArgoUML and jEdit, consisting of more than 80,000 events, covering development information from 1998 to 2015. With the application of our approach to such data, activities can be extracted from event logs and a classifier can be constructed for adding activity attributes to new events. The results of the classification are evaluated in terms of precision rate, recall rate, and the F-measure. Overall, two real-world software development process logs are used to validate the method, and the experimental results show that the approach can mine software process activities from SVN log events automatically and in real-time.