Predicting software defects using machine learning techniques

A huge variety of software systems are relied upon in such domains as aviation, healthcare, manufacturing and robotics, and therefore, h systems and that they are reliable. Software defect prediction helps improve software reliability by identifying potential bugs during software maintenance. Tradit...

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Main Authors: Aquil, M.A.I., Ishak, W.H.W.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.23167 /
Published: World Academy of Research in Science and Engineering 2020
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090287178&doi=10.30534%2fijatcse%2f2020%2f352942020&partnerID=40&md5=241189e8b745ce63e0ccfa5e0494ffd4
http://eprints.utp.edu.my/23167/
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spelling utp-eprints.231672021-08-19T06:10:10Z Predicting software defects using machine learning techniques Aquil, M.A.I. Ishak, W.H.W. A huge variety of software systems are relied upon in such domains as aviation, healthcare, manufacturing and robotics, and therefore, h systems and that they are reliable. Software defect prediction helps improve software reliability by identifying potential bugs during software maintenance. Traditionally, the focus of software defect prediction was on the design of static code metrics, which help with predicting the defect probabilities of a code when input into machine learning classifiers. While machine learning techniques such as Deep Learning technique, Ensembling, Data Mining, Clustering and Classification are known to help predict the location of defects in code bases, researchers have not yet agreed on which is the best predictor model. This paper will use 13 software defect datasets in evaluating the performance of the different predictor models. The results show that consistency in high accuracy prediction was achieved using Ensembling techniques. © 2020, World Academy of Research in Science and Engineering. All rights reserved. World Academy of Research in Science and Engineering 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090287178&doi=10.30534%2fijatcse%2f2020%2f352942020&partnerID=40&md5=241189e8b745ce63e0ccfa5e0494ffd4 Aquil, M.A.I. and Ishak, W.H.W. (2020) Predicting software defects using machine learning techniques. International Journal of Advanced Trends in Computer Science and Engineering, 9 (4). pp. 6609-6616. http://eprints.utp.edu.my/23167/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description A huge variety of software systems are relied upon in such domains as aviation, healthcare, manufacturing and robotics, and therefore, h systems and that they are reliable. Software defect prediction helps improve software reliability by identifying potential bugs during software maintenance. Traditionally, the focus of software defect prediction was on the design of static code metrics, which help with predicting the defect probabilities of a code when input into machine learning classifiers. While machine learning techniques such as Deep Learning technique, Ensembling, Data Mining, Clustering and Classification are known to help predict the location of defects in code bases, researchers have not yet agreed on which is the best predictor model. This paper will use 13 software defect datasets in evaluating the performance of the different predictor models. The results show that consistency in high accuracy prediction was achieved using Ensembling techniques. © 2020, World Academy of Research in Science and Engineering. All rights reserved.
format Article
author Aquil, M.A.I.
Ishak, W.H.W.
spellingShingle Aquil, M.A.I.
Ishak, W.H.W.
Predicting software defects using machine learning techniques
author_sort Aquil, M.A.I.
title Predicting software defects using machine learning techniques
title_short Predicting software defects using machine learning techniques
title_full Predicting software defects using machine learning techniques
title_fullStr Predicting software defects using machine learning techniques
title_full_unstemmed Predicting software defects using machine learning techniques
title_sort predicting software defects using machine learning techniques
publisher World Academy of Research in Science and Engineering
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090287178&doi=10.30534%2fijatcse%2f2020%2f352942020&partnerID=40&md5=241189e8b745ce63e0ccfa5e0494ffd4
http://eprints.utp.edu.my/23167/
_version_ 1741196632100175872
score 11.62408

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