Fault Prediction Techniques using DMM and GGA

P. Patchaiammal*, R. Thirumalaiselvi**
* Assistant Professor, Computer Application Department, Sindhi College, Chennai.
** Assistant Professor, Computer Science Department, Govt. Arts College (Men), (Autonomous), Chennai.
Periodicity:July - September'2014
DOI : https://doi.org/10.26634/jse.9.1.3210

Abstract

Software Engineering has new methodologies, technologies, applications and processes. Machine learning involves computer programming solutions that are experienced based learning to improve performance at some task. The overlap between machine learning and software engineering has seen the development of machine learning application to address various problems in software engineering. Faults in software engineering systems are major problems that need to be resolved. Fault prediction in software is significant because it can help in directing test effort, reducing cost, and increasing the quality of software and its reliability. In this study, the authors have analyzed various fault prediction techniques and proposed a new model named DMM (Decision Making Model) based on decision logic to develop the prediction hypothesis by introducing a new algorithm called GGA (Genetic Gain Algorithm) for fault prediction.

Keywords

Machine Learning, Software Engineering, DMM, GGA, Fault and Fault Prediction.

How to Cite this Article?

Patchaiammal,P., and Thirumalaiselvi.R. (2014). Fault Prediction Techniques Using DMM And GGA. i-manager’s Journal on Software Engineering, 9(1), 17-26. https://doi.org/10.26634/jse.9.1.3210

References

[1]. Aditi Sanyal, Balraj Singh, (2014). “A Systematic Literature Survey on Various Techniques for Software Fault Prediction,” International Journal of Advanced Research in Computer Science and Software Engineering, January.
[2]. Er. Rohit Mahajan, Dr. Sunil Kumar Gupta, Rajeev Kumar Bedi, (2014). “Comparison of Various Approaches of Software Fault Prediction: A Review,” International Journal of Advanced Technology & Engineering Research (IJATER).
[3]. Gurvinder Singh, Baljit Singh Saini, and Neeraj Mohan, (2013). “A Systematic Literature Review on software Fault Prediction based on Qualitative and Quantative Factors,” ISSN (Print): 2319-2526, Vol.2, Issue - 2.
[4]. Cagatay Catal, Banu Diri, (2013). “A Fault Detection Strategy for Software Projects,” Technical Gazette, Vol.20, No.1, pp.1-7.
[5]. A. A. Shahrjooj Haghighi, M. A., (2012). “Appling Mining Schemes to Software Fault Prediction: A Proposed Approach Aimed at Test Cost Reduction,” Proceedings of the World Congress on Engineering.
[6]. Ruchika Malhotra., Ankita Jain., (2012). “Fault Prediction Using Statistical and Machine Learning Methods for Improving Software Quality”, Journal of Information Processing Systems, Vol.8, No.2.
[7]. Catal, C., (2012). “Performance Evaluation Metrics for Software Fault Prediction Studies,” Acta Polytechnica Hungarica.
[8]. Ritika Sharma, N. B., (2012). “Study of Predicting Fault Prone Software Modules,” International Journal of Advanced Research in Computer Science and Software Engineering.
[9]. Wasif Afzal, Richard Torkar, (2011). “On the application of genetic programming for software engineering predictive modeling: A systematic review,” Expert Systems with Applications-An International Journal, March.
[10]. Felix Salfner, MarenLenk, and Miroslaw Malek, (2010). “A Survey of Online Failure Prediction Methods,” ACM Computing Surveys, Vol. 42, No. 3.
[11]. S. G., A. K., M. A., & Kaur, D., (2010). “A Clustering Algorithm for Software Fault Prediction,” IEEE international conference on computer and communication technology, (pp. 603-607).
[12]. Sandhu, D. P., & Manpreet Kaur and Amandeep Kaur, (2010). “A Density Based Clustering Approach for Early Detection of Fault Prone Modules,” IEEE International Conference on Electronics and Information Engineering, (pp. 525-530).
[13]. Seliya, N., Tagi M. Khoshgoftaar, & Jason Van Hulse, (2010). “Predicting Faults in High Assurance Software ”, IEEE 12th International Symposium on High Assurance Systems Engineering, 2010.
[14]. Yue Jiang, J. L., (2009). “Variance Analysis in Software Fault Prediction Models,” IEEE 20th International Symposium on Software Reliability Engineering.
[15]. Salfner, F., (2008). “Event-based Failure Prediction: An Extended Hidden Markov Model Approach”, dissertation.de—Verlag in Internet GmbH, Berlin, Germany.
[16]. Salfner, F. and Malek, M. (2007). “Using hidden semi- Markov models for effective online failure prediction.” In Proceedings of the IEEE 26th International Symposium on Reliable Distributed Systems (SRDS).
[17]. Salfner, F., (2006). Modeling event-driven time series with generalized hidden semi-Markov models. Tech. rep. 208. Department of Computer Science, Humboldt- University at Berlin, Berlin, Germany.
[18]. BAI, C. G., HU, Q. P., XIE, M., and NG, S. H. (2005). Software failure prediction based on a Markov Bayesian network model. J. Syst. Software. Vol.74, No.3 (Feb.), pp.275–282.
[19]. Leangsuksun, C., Liu, T., Rao, T., Scott, S., and Libby, R. (2004). “A failure predictive and policy-based high availability strategy for Linux high performance computing cluster.” In Proceedings of the 5th LCI International Conference on Linux Clusters: The HPC Revolution. 18–20.
[20]. Du Zhang., Jeffrey J.P. Tsai., (2003). “Machine Learning and Software Engineering”. Software Quality Journal, Vol.11, pp.87–119.
[21]. http://andrew.gibiansky.com/blog/machinelearning/ machine-learning-the-basics/
[22]. http://edoc.hu-berlin.de/docviews/abstract.php? id=27653
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.