Class level software fault prediction using step wise linear regression

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Programming measurements was utilized for foreseeing issue in modules of programming ventures. Convenient forecast of flaws enhances programming quality and subsequently its dependability. In this paper, a framework towards subspace grouping of large data set was pro-posed at class level to minimize the error. We composed an iterative calculation for grouping of high dimensional datasets for improvement of a target work. At that point the bunched data sets were examined utilizing Step-Wise Linear Regression to investigate the relationship among a structure variable and the autonomous factors in order to anticipate of damaged and non-faulty classes. To evaluate the supportive-ness of the model, we drove a practical learning on the Attitude Survey Data. The proposed strategy specifically managed blunder variables and consequently gave precise fault prediction least standard error (0.003) when contrasted with the current technique (4.687). Root mean square error which measures the distinction between the assessed error and the real error was (0.8) in the proposed technique. The results demonstrated that the forecast models based on subspace clustering were essentially predominant to the current techniques.



  • Keywords

    Software Metrics Model; Fault Prediction; Subspace Clustering; Stepwise Linear Regression; Standard Error; Root Mean Square Error.

  • References

      [1] Mizuno O. and Hata H.2010. An integrated approach to detect fault-prone modules using complexity and text feature metrics. Advances in Computer Science and Information Technology, Springer Berlin Heidelberg.457-468.

      [2] Abaei G. and Selamat A.2014.Software fault prediction based on improved fuzzy clustering.Distributed Computing and Artificial Intelligence.11th International Conference. Springer International Publishing. 165-172.

      [3] Catal C., Sevim U. and Diri B.Metrics-driven software quality prediction without prior fault data. Electronic Engineering and Computing Technology. Springer Netherlands. 189-199.

      [4] Tan X., Peng X., Pan S. and ZhaoW...2011. Assessing software quality by program clustering and defect prediction. Reverse Engineering (WCRE).18thWorkingConferenceIEEE. 244-248.

      [5] Scanniello G., Gravino C., Marcus A. and Menzies T.2010.Class level fault prediction using software clustering. Automated Software Engineering (ASE). IEEE/ACM 28th International Conference. 640-645.

      [6] Oyetoyan T.D.Conradi R. and Soares Cruzes D.2013.Criticality of defects in cyclic dependent components. Source Code Analysis and Manipulation (SCAM). IEEE 13th International Working Conference. 21-30.

      [7] Faragó C., Hegedűs P. and Ferenc R.2015.Code Ownership Impact on Maintainability Computational Science and its Applications ICCSA,Springer International Publishing. 3-19.

      [8] Mende T. and Koschke R.2010.Effort-aware defect prediction models. Software Maintenance and Reengineering (CSMR).14th European Conference IEEE. 107-116.

      [9] Zafar. H, Rana Z. and .Shamail S.M.M.2012.inding focused item sets from software defect data.Multitopic Conference (INMIC).15th International conference IEEE, 418-423.

      [10] Shekofteh.,Maryam.,Keyvan Mohebbi., and Javad Kamyabi., 2015.Software defect prediction using participation of nodes in software coupling.Journal of Theoretical and Applied Information Technology .82(3): 440-446.

      [11] Sashidharan R. and Sriram P.2013.Hyper-quad tree based K means algorithm for software fault prediction. Advances in intelligent system and computing, Proceding of ICC3.246:107-118.

      [12] wang D.,Han B. and Huang M.2012.Application of fuzzy c-means clustering algorithm based on particle swarm optimization in computer forensics,International conference on applied physcis and industrial engineering.24:1186-1191.

      [13] Elhamifar E. and Vidal R.2013.Sparse subspace clustering: Algorithm, theory, and applications,Pattern Analysis and Machine Intelligence. IEEE Transactions .35(11).2765-2781.

      [14] Xu J.,.Ho D. and.Capretz L.F..2015. An empirical estimation models. arXiv preprint arXiv:1507.06925,2015.

      [15] Chatteree S. and A.S. HadA.S.1991.Regression Analysis by Example. Second Edition, John Wiley and Sons.




Article ID: 14881
DOI: 10.14419/ijet.v7i2.17.14881

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.