APJIS Asia Pacific Journal of Information Systems


The Journal for Information Professionals

Asia Pacific Journal of Information Systems (APJIS), a Scopus and ABDC indexed journal, is a
flagship journal of the information systems (IS) field in the Asia Pacific region.

ISSN 2288-5404 (Print) / ISSN 2288-6818 (Online)

Editor : Seung Hyun Kim

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Current Issue

Date September 2020
Vol. No. Vol. 30 No. 3
DOI https://doi.org/10.14329/apjis.2020.30.3.457
Page 457~496
Title A Pragmatic Framework for Predicting Change Prone Files Using Machine Learning Techniques with Java-based Software
Author Loveleen Kaur, Ashutosh Mishra
Keyword Software Component, Software Change, Source Code Metrics, Software Prediction, Machine Learning
Abstract This study aims to extensively analyze the performance of various Machine Learning (ML) techniques for predicting version to version change proneness of source code Java files. 17 object-oriented metrics have been utilized in this work for predicting change-prone files using 31 ML techniques and the framework proposed has been implemented on various consecutive releases of two Java-based software projects available as plug-ins. 10-fold and inter-release validation methods have been employed to validate the models and statistical tests provide supplementary information regarding the reliability and significance of the results. The results of experiments conducted in this article indicate that the ML techniques perform differently under the different validation settings. The results also confirm the proficiency of the selected ML techniques in lieu of developing change-proneness prediction models which could aid the software engineers in the initial stages of software development for classifying change-prone Java files of a software, in turn aiding in the trend estimation of change-proneness over future versions.

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