A Logistic Regression Based Approach for Software Test Management
Software test management is a hot field of software engineering both in academia and industry. However, traditional research are focused on software quality rather than test quality which could be evaluated by test management. Meanwhile, previous works on evaluating software test management were mostly based on statistical methods. In recently years, machine learning algorithms and techniques are developing rapidly. Logistic Regression is one of the important and often used algorithms. It is widely used in many domains such as credit investigation, book classification and so on. Therefore, our objective is to establish a logistic regression based approach for software test management to evaluate test quality. In this paper, we introduce our methodology to build metrics framework for test management, and enumerate the definition, type and range of each metric. We also show some results of our experiments using some data samples from a huge data sets.
2016 International Conference on Cyber Enabled Distributed Computing and Knowledge Discovery (CyberC)