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Software Fault Prediction using Data Mining Techniques: A Survey

Ashwni Kumar1 , D.L.Gupta 2

Section:Survey Paper, Product Type: Journal Paper
Volume-7 , Issue-6 , Page no. 671-674, Jun-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i6.671674

Online published on Jun 30, 2019

Copyright © Ashwni Kumar, D.L.Gupta . 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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IEEE Style Citation: Ashwni Kumar, D.L.Gupta, “Software Fault Prediction using Data Mining Techniques: A Survey,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.671-674, 2019.

MLA Style Citation: Ashwni Kumar, D.L.Gupta "Software Fault Prediction using Data Mining Techniques: A Survey." International Journal of Computer Sciences and Engineering 7.6 (2019): 671-674.

APA Style Citation: Ashwni Kumar, D.L.Gupta, (2019). Software Fault Prediction using Data Mining Techniques: A Survey. International Journal of Computer Sciences and Engineering, 7(6), 671-674.

BibTex Style Citation:
@article{Kumar_2019,
author = {Ashwni Kumar, D.L.Gupta},
title = {Software Fault Prediction using Data Mining Techniques: A Survey},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2019},
volume = {7},
Issue = {6},
month = {6},
year = {2019},
issn = {2347-2693},
pages = {671-674},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4610},
doi = {https://doi.org/10.26438/ijcse/v7i6.671674}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i6.671674}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4610
TI - Software Fault Prediction using Data Mining Techniques: A Survey
T2 - International Journal of Computer Sciences and Engineering
AU - Ashwni Kumar, D.L.Gupta
PY - 2019
DA - 2019/06/30
PB - IJCSE, Indore, INDIA
SP - 671-674
IS - 6
VL - 7
SN - 2347-2693
ER -

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Abstract

In recent studies, it is found that a fault prediction technique plays an important role especially in software development. Software fault prediction implies a decent investment in better style in future system to avoid building a fault prone modules. Faulty modules are expected using data mining techniques such as various classifiers which are used to classify faulty or non faulty modules. Many researchers have been produces different approaches for predicting fault in the software. In this paper it is found that various fault prediction techniques have been used and also found out the way to judge the performance of fault prediction methodologies in recent year. The main objective of survey is to identify best prediction techniques for detecting fault in early stage, and also determine the problem area in software fault prediction methodology which provides improvement in software development system. This paper presents the survey on fault prediction using data mining techniques which will helpful for further research in field of software fault prediction.

Key-Words / Index Term

Software fault prediction, Data Mining, Prediction techniques

References

[1] Naik, K., & Tripathy, “Software Testing and Quality Assurance”, John Wiley & Sons, Inc, pp. (2008).
[2] Wangshu Liu, Shulong Liu, Qing Gu, Member, IEEE, Jiaqiang Chen, Xiang Chen, Member, IEEE, and Daoxu Chen, Member, IEEE , “Empirical Studies of a Two-Stage Data Preprocessing Approach for Software Fault Prediction” in IEEE TRANSACTIONS ON RELIABILITY Volume: PP Year: 2015.
[3] Yasutaka Kamei, Akito Monden, Shuji Morisaki, Ken-ichi Matsumoto, “A Hybrid faulty module Prediction using Association Rule Mining and Logistic Regression Analysis”, Proceedings of the Second ACM-IEEE International Symposium on Empirical Software Engineering and measurement, Pages 279-281.
[4] Catal. C, Sevim. U and Diri. B, “Software fault prediction of unlabeled program modules”, London,UK., WCE 2009, July 1-3
[5] Shanthini. A Chandrasekaran.RM ,”Applying Machine Learning for Fault Prediction Using Software Metrics”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 6, June 2012, ISSN: 2277 128X.
[6] Meenakshi P.C, Meenu S, Mithra M, Leela Rani P,” Fault Prediction using Quad Tree and Expectation Maximization Algorithm”, International Journal of Applied Information Systems (IJAIS) – ISSN : 2249-0868, Volume 2– No.4, May 2012
[7] Yuanxun SHAO, Bin LIU, Guoqi LI, Shihai WANG,” Software Defect Prediction Based on Class Association Rules”, The Second International Conference on Reliability Systems Engineering (ICRSE 2017)
[8] N.P. Gopalan, B. Sivaselvan, “Data Mining Techniques and Trends”, PHP Learning Private Limited, New Delhi-110001, India, ISBN-978-81-203-3812-B, 2009
[9] G.K. Gupta, “Introduction To Data Mining With Case Studies”, Second Edition, PHP Learning Private Limited, New Delhi-110001, India, ISBN-978-81-203-4326-9, 2011
[10] D. Hand, H. Mannila and P. Smyth, “Principles of data mining”, MIT, 2001.
[11] Fong, P.K. and Weber-Jhanke, J.H , “Privacy Preserving Decision Tree Learning using Unrealized Data Sets”, IEEE Transactions on knowledge and Data Engineering, Vol.24,No.2, February 2012, pp. 353-364.
[12] Langley P, Iba W, Thompson K., “An analysis of Bayesian classifiers”, in Proceedings of the 10th National Conference on Artificial Intelligence, 1992, pp. 223-228.