Test effort estimation and prediction of traditional and rapid release models using machine learning algorithms

Issue title: Special Section: Ambient advancements in intelligent computational sciences

Guest editors: Shailesh Tiwari, Munesh Trivedi and Mohan L. Kohle

Article type: Research Article

Authors: Vig, Vidhi^{; *} | Kaur, Arvinder

Affiliations: University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, Dwarka, New Delhi, India

Correspondence: [*] Corresponding author. Vidhi Vig, University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, Dwarka, New Delhi, India.E-mail vidhi.ipu@gmail.com.

Abstract: Recently, many software companies have shifted to shorter release cycles from the traditional multi-month release cycle. Evolution and transition of release cycles may affect the test effort in the system. This paper analyses 25 traditional releases containing 1210 classes and 69 rapid releases containing 2616 classes of four Open Source Java systems. Correlations between 48 Object Oriented metrics and 2 test metrics were evaluated to identify the best indicators of test effort. The results show that (i) correlation between OO and test metrics remain irrespective of release models, (ii) test effort required in Rapid Release (RR) models (shorter release cycles) is slightly more as compared to Traditional Release (TR) models, (iii) Out of 18 machine learning algorithms instance based machine learning algorithms IBK and K star followed by Multi-Layer Perceptron (MLP) and additive regression are able to predict the test effort accurately in classes.

Keywords: Release cycles, machine learning, prediction, software metrics, test effort

DOI: 10.3233/JIFS-169703

Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1657-1669, 2018