research-article

Feature Transformation for Improved Software Bug Detection Models

Authors:

Shamse Tasnim Cynthia,

Debajyoti MondalAuthors Info & Claims

ISEC '22: Proceedings of the 15th Innovations in Software Engineering Conference

Article No.: 16, Pages 1 - 10

https://doi.org/10.1145/3511430.3511444

Published: 24 February 2022 Publication History

Abstract

Testing software is considered to be one of the most crucial phases in software development life cycle. Software bug fixing requires a significant amount of time and effort. A rich body of recent research explored ways to predict bugs in software artifacts using machine learning based techniques. For a reliable and trustworthy prediction, it is crucial to also consider the explainability aspects of such machine learning models. In this paper, we show how the feature transformation techniques can significantly improve the prediction accuracy and build confidence in building bug prediction models. We propose a novel approach for improved bug prediction that first extracts the features, then finds a weighted transformation of these features using a genetic algorithm that best separates bugs from non-bugs when plotted in a low-dimensional space, and finally, trains the machine learning model using the transformed dataset. In our experiment with real-life bug datasets, the random forest and k-nearest neighbor classifier models that leveraged feature transformation showed 4.25% improvement in recall values on an average of over 8 software systems when compared to the models built on original data.

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Kukkar AKumar Lilhore UFrnda JKaur Sandhu JPrava Das RGoyal NKumar AMuduli KRezac F(2023)ProREJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2022.12.01735:1(483-498)Online publication date: 1-Jan-2023
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cover image ACM Other conferences

ISEC '22: Proceedings of the 15th Innovations in Software Engineering Conference

February 2022

235 pages

ISBN:9781450396189

DOI:10.1145/3511430

Editors:
Saurabh Tiwari
DA-IICT Gandhinagar, India
,
Sanjay Chaudhary
Ahmedabad University, India
,
Chanchal K. Roy
University of Saskatchewan, Canada
,
Meenakshi D'Souza
IIIT Bangalore, India
,
Richa Sharma
Lock Haven University, USA
,
Lov Kumar
BITS-Pilani, Hyderabad, India

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Published: 24 February 2022

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ISEC 2022: 15th Innovations in Software Engineering Conference

February 24 - 26, 2022

Gandhinagar, India

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https://doi.org/10.48084/etasr.8829
Mostafa SCynthia SRoy BMondal D(2024)Feature transformation for improved software bug detection and commit classificationJournal of Systems and Software10.1016/j.jss.2024.112205(112205)Online publication date: Sep-2024
https://doi.org/10.1016/j.jss.2024.112205
Kukkar AKumar Lilhore UFrnda JKaur Sandhu JPrava Das RGoyal NKumar AMuduli KRezac F(2023)ProREJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2022.12.01735:1(483-498)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.jksuci.2022.12.017
Siva RS KHariharan BPremkumar N(2023)Automatic Software Bug Prediction Using Adaptive Artificial Jelly Optimization With Long Short-Term MemoryWireless Personal Communications: An International Journal10.1007/s11277-023-10694-9132:3(1975-1998)Online publication date: 23-Aug-2023
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Siva RS KHariharan BPremkumar N(2023)Automatic software bug prediction using adaptive golden eagle optimizer with deep learningMultimedia Tools and Applications10.1007/s11042-023-16666-283:1(1261-1281)Online publication date: 5-Sep-2023
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Qiu SWang STian XHuang MHuang Q(2022)Visualization-Based Software Defect Prediction via Convolutional Neural Network with Global Self-Attention2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)10.1109/QRS57517.2022.00029(189-198)Online publication date: Dec-2022
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