Ae-RF- An Enhanced Methodology for Just In Time Software Defect Detection

Ruchika Malhotra¹ , Bibi Aisha Nazari²

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.4 , pp.127-135, Aug-2020

Online published on Aug 31, 2020

Copyright © Ruchika Malhotra, Bibi Aisha Nazari . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Just in time Software Defects Detection (JIT-SDD) is one of the interesting areas in predicting software faults at change level. Identifying just in time bugs or defects in the development process would not only ensure consistency and quality of the software, but also allows developers to test and address bugs quickly as well. Deep learning has acquired a prominent role in the literature on machine learning. It may be utilized to enhance the JIT-SDD?s performance. This paper suggests a methodology named Ae-RF (Auto-encoder ? Random Forest) which exploits deep learning algorithms to detect changes that are susceptible to defects. We first use the Autoencoder algorithm to extract a group of semantic features among various basic change features. We use six big public projects to probe our method (i.e., Mozilla, JDT, Bugzilla, Platform, PostgreSQL, and Columba) comprising a total amount of 137,417 changes. We use just one of the mentioned datasets (Platform) to construct and train the Autoencoder Model. Using this trained model, we can extract the relevant features of the other five datasets. Then we use the Random Forest algorithm for the classification phase. We selected the Area Under the Curve (AUC) as the main evaluation metric in this study. We gained the AUC values of 0.91 for Mozilla, 0.84 for JDT, and 0.71 for Bugzilla, 0.85 for Platform, 0.80 for PostgreSQL, and Columba with 0.78 AUC value.

Key-Words / Index Term :
Just In Time Software Defect Detection, Deep Learning, Autoencoder, Random Forest

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