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Object-Oriented Metrics
Analyzing Machine Learning Techniques for Fault Prediction Using Web Applications
Ruchika Malhotra and Anjali Sharma
Page: 751~770, Vol. 14, No.3, 2018
10.3745/JIPS.04.0077
Keywords: Empirical Validation, Fault prediction, Machine Learning, Object-Oriented Metrics, Web Application Quality
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Analyzing Machine Learning Techniques for Fault Prediction Using Web Applications
Ruchika Malhotra and Anjali Sharma
Page: 751~770, Vol. 14, No.3, 2018

Keywords: Empirical Validation, Fault prediction, Machine Learning, Object-Oriented Metrics, Web Application Quality
Show / Hide Abstract
Web applications are indispensable in the software industry and continuously evolve either meeting a newer
criteria and/or including new functionalities. However, despite assuring quality via testing, what hinders a
straightforward development is the presence of defects. Several factors contribute to defects and are often
minimized at high expense in terms of man-hours. Thus, detection of fault proneness in early phases of
software development is important. Therefore, a fault prediction model for identifying fault-prone classes in a
web application is highly desired. In this work, we compare 14 machine learning techniques to analyse the
relationship between object oriented metrics and fault prediction in web applications. The study is carried out
using various releases of Apache Click and Apache Rave datasets. En-route to the predictive analysis, the
input basis set for each release is first optimized using filter based correlation feature selection (CFS) method.
It is found that the LCOM3, WMC, NPM and DAM metrics are the most significant predictors. The statistical
analysis of these metrics also finds good conformity with the CFS evaluation and affirms the role of these
metrics in the defect prediction of web applications. The overall predictive ability of different fault prediction
models is first ranked using Friedman technique and then statistically compared using Nemenyi post-hoc
analysis. The results not only upholds the predictive capability of machine learning models for faulty classes
using web applications, but also finds that ensemble algorithms are most appropriate for defect prediction in
Apache datasets. Further, we also derive a consensus between the metrics selected by the CFS technique and
the statistical analysis of the datasets.