Open Access   Article Go Back

A Framework of Software Defect Prediction By Data Mining Techniques Using Historical Data Set and Intelligent Agents

Amitava Bondyopadhyay1

Section:Review Paper, Product Type: Journal Paper
Volume-07 , Issue-01 , Page no. 245-248, Jan-2019

Online published on Jan 20, 2019

Copyright © Amitava Bondyopadhyay . 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.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Amitava Bondyopadhyay, “A Framework of Software Defect Prediction By Data Mining Techniques Using Historical Data Set and Intelligent Agents,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.245-248, 2019.

MLA Style Citation: Amitava Bondyopadhyay "A Framework of Software Defect Prediction By Data Mining Techniques Using Historical Data Set and Intelligent Agents." International Journal of Computer Sciences and Engineering 07.01 (2019): 245-248.

APA Style Citation: Amitava Bondyopadhyay, (2019). A Framework of Software Defect Prediction By Data Mining Techniques Using Historical Data Set and Intelligent Agents. International Journal of Computer Sciences and Engineering, 07(01), 245-248.

BibTex Style Citation:
@article{Bondyopadhyay_2019,
author = {Amitava Bondyopadhyay},
title = {A Framework of Software Defect Prediction By Data Mining Techniques Using Historical Data Set and Intelligent Agents},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2019},
volume = {07},
Issue = {01},
month = {1},
year = {2019},
issn = {2347-2693},
pages = {245-248},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=626},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=626
TI - A Framework of Software Defect Prediction By Data Mining Techniques Using Historical Data Set and Intelligent Agents
T2 - International Journal of Computer Sciences and Engineering
AU - Amitava Bondyopadhyay
PY - 2019
DA - 2019/01/20
PB - IJCSE, Indore, INDIA
SP - 245-248
IS - 01
VL - 07
SN - 2347-2693
ER -

           

Abstract

Defect prediction for a software system is a technique that is used extensively nowadays to predict defects from historical database. But only a good data mining model is not enough to extract defect from software bug record. Intelligent agents are helpful in this case by making the decision process easier at some point. This paper describes frame work to generate software defect from the historical database and also propose one algorithm that is used find policy to forecast software defects efficiently than the current methods.

Key-Words / Index Term

Cost, Classification, Intelligent agents ,Data mining, Database, Defect, Testing

References

[1]. Ms. P.J Kaur, Ms. Pallavi, “Data Mining Techniques for Software Defect Prediction” , International Journal of Software and Ib Sciences (IJSWS)
[2]. W Sunindyo, T Moser, D Winkler , “Improving Open Source Software Process Quality based on Defect Data Mining” , Christian Doppler Laboratory for Software Engineering Integration for Flexible Automation Systems,Vienna University of Technology.
[3]. K.B.S Sastry, Dr.B.V.Subba Rao, Dr K.V.Sambasiva Rao, “Software Defect Prediction from Historical Data” ,International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 8, August 2013
[4]. N Azeem, S Usmani, “Analysis of Data Mining Based Software Defect Prediction Techniques”,Global Journal of Computer Science and Technology, Volume 11 Issue 16 Version 1.0 September 2011
[5]. T Xie, S Thummalapenta, D Lo, and C Liu, “Data mining for software engineering.” Computer, 42(8):55-62, 2009.
[6]. M Baojun, K Dejaeger, J Vanthienen, and B Baesens, “Software defect prediction based on association rule classification” SSRN 1785381, 2011.