Software Defect Prediction using Average Probability Ensemble Technique

T. Vara Prasad*, C. Silpa**, Srinivasulu Asadi***
* M.Tech Scholar, Department of IT, Sree Vidyanikethan Engineering college (Autonomous), Rangampet, Andhra Pradesh, India.
** Assistant Professor, Department of IT, Sree Vidyanikethan Engineering college (Autonomous), Rangampet, Andhra Pradesh, India.
*** Ph.d Scholar, Jawaharlal Nehru Technological University, Hyderabad, India.
Periodicity:April - June'2015
DOI : https://doi.org/10.26634/jse.9.4.3533

Abstract

The present generation software testing plays a major role in defect predication. Software defect data includes redundancy, correlation, feature irrelevance and missing value. It is hard to ensure that the software is defective or nondefective. Software applications on day-to-day businesses activities and software attribute prediction such as effort estimation, maintainability, and defect and quality classification are growing interest from both academic and industry communities. Software defect predication using several methods, in that random forest and gradient boosting are effective. Even though the defect datasets contain incomplete or irrelevant features. The proposed system Average probability ensemble technique used to overcome those problems and gives high classification result to compare another method. Because it has integrated with three algorithms to use classification performance and it gives more accurate result in publicly-available software datasets.

Keywords

Software Defect Prediction, Software Metrics, Ensemble Learning Models.

How to Cite this Article?

Prasad, T. V., Silpa, C., and Srinivasulu, A. (2015). Software Defect Prediction using Average Probability Ensemble Technique. i-manager’s Journal on Software Engineering, 9(4), 32-39. https://doi.org/10.26634/jse.9.4.3533

References

[1]. Issam H. Laradji, Mohammad Alshayeb, Lahouari Ghouti. Software defect prediction using ensemble learning on selected features Information, Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.
[2]. M. Riaz, E. Mendes, E. Tempero, (2009). A systematic review of software maintainability prediction and metrics, rd in: Proceedings of the 2009 3 International Symposium on Empirical Software Engineering and Measurement, pp.367–377.
[3]. Y. Ma, G. Luo, X. Zeng, A. Chen, (2012). Transfer learning for cross-company software defect prediction, Inform. Softw. Technol. Vol.54, pp.248–256.
[4]. Tosun, A. Bener, B. Turhan, T. Menzies, (2010). Practical considerations in deploying statistical methods for defect prediction:a case study within the Turkish telecommunications industry, Inform. Softw. Technol. Vol.52, pp.1242–1257.
[5]. Q. Song, Z. Jia, M. Shepperd, S. Ying, J. Liu, (2011). A general software defect-proneness prediction framework, IEEE Trans. Softw. Eng. Vol.37, pp.356–370.
[6]. N.V. Chawla, K.W. Bowyer, L.O. Hall, W.P. Kegelmeyer, (2011). SMOTE: synthetic minority over-sampling technique, arXiv:1106.1813.
[7]. T.M. Khoshgoftaar, E. Geleyn, L. Nguyen, L. Bullard, (2002). Cost-sensitive boosting in software quality modeling, in: Proceedings, 7th IEEE International Symposium on High Assurance Systems Engineering, pp.51–60.J.R.
[8]. Miyamoto, J. Miyakoshi, K. Matsuzaki, T. Irie, (2013). False-positive reduction of liver tumor detection using ensemble learning method, in: SPIE Medical Imaging, pp. 86693B–86693B.
[9]. G. Wu, E. Chang, (2003). Adaptive feature-space conformal transformation for imbalanced-data learning, in: International Conference on Machine Learning (ICML 2003).
[10]. D. Gray, D. Bowes, N. Davey, Y. Sun, B. Christianson, (2011). The misuse of the NASA metrics data program data sets for automated software defect prediction, in: Evaluation and Assessment in Software Engineering EASE 25, pp.12–25.
[11]. D. Zhong, J. Han, X. Zhang, Y. Liu, (2010). Neighborhood discriminant embedding in face recognition, Opt. Eng. 49. 077203-077203-7.
[12]. T.G. Dietterichl, (2002). Ensemble learning, in: The Handbook of Brain Theory and Neural Networks, pp.405–408.
[13]. F. Markowetz, (2001). Support Vector Machines in Bioinformatics, Master's thesis, University of Heidelberg.
[14]. H. Chen, H. Ye, L. Chen, H. Su, (2004). Application of support vector machine learning to leak detection and location in pipelines, in: Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference, IMTC 04, Vol.3, pp.2273–2277.
[15]. T. Menzies, B. Caglayan, E. Kocaguneli, J. Krall, F. Peters, B. kurhan, (2012). The PROMISE repository of empirical software engineering data.
[16]. T. Hall, S. Beecham, D. Bowes, D. Gray, S. Counsell, (2012). A systematic literature review on fault prediction performance in software engineering, IEEE Trans. Softw. Eng. Vol.38, pp.1276–1304.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.