Assessment and Analysis of Software Reliability Using Machine Learning Techniques

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Software reliability models access the reliability by fault prediction. Reliability is a real world phenomenon with many associated real time problems and to obtain solutions to problems quickly, accurately and acceptably a large no. of soft computing techniques has been developed. We attempt to address the software failure problems by modeling software failure data using the machine learning techniques such as support vector machine (SVM) regression and generalized additive models. The study of software reliability can be categorized into three parts: modeling, measurement, improvement. Programming unwavering quality demonstrating has developed to a point that important outcomes can be acquired by applying appropriate models to the issue; there is no single model all inclusive to every one of the circumstances. We propose different machine learning methods for the evaluation of programming unwavering quality, for example, artificial neural networks, support vector machine calculation approached. We at that point break down the outcomes from machine getting the hang of demonstrating, and contrast them with that of some summed up direct displaying procedures that are proportional to programming dependability models.

     

     


  • Keywords


    Software-reliability, artificial neural networks, Support vector machine, Machine learning techniques.

  • References


      [1] IEEE Transactions on Software Engineering,Vol.43,No.1,January 2011.

      [2] Lina Chato*, Shahab Tayeb and Shahram Latifi,” A Genetic Algorithm to Optimize the Adaptive Support Vector Regression Model for Forecasting the Reliability of Diesel Engine Systems “ 2017.

      [3] Akshi Kumar, Rajat Chugh, Rishab Girdhar and Simran Aggarwa,” Classification of errors in Web Applications using Machine Learning” 2017.

      [4] Harsh Lal and Gaurav Pahwa,” Root Cause Analysis of Software Bugs using Machine Learning Techniques”2017.

      [5] Alweshah, M., Ahmed, W., & Aldabbas, H. (2016).”Evolution of Software Reliability Growth Models: A Comparison of Auto-Regression and Genetic Programming Models”,2015.

      [6] Ruchika Malhotra,” A Systematic Review of Machine Learning Techniques for Software Fault Prediction”, 2016.

      [7] Sankardas Roy1, Jordan DeLoach2, Yuping Li3, Nic Herndon2, Doina Caragea2, Xinming Ou 3, Venkatesh Prasad Ranganath2, Hongmin Li2, and Nicolais Guevara2 “Experimental Study with Real-world Data for Android App Security Analysis using Machine Learning”,2015.

      [8] Ramakanta Mohanty and M. R. Patra,” Application of Machine learning techniques to Predict software reliability”, 2010.

      [9] Kuldeep Singh Kaswan, Sunita Choudhary and Kapil Sharma,”Software Reliability Modeling uses Soft Computing Techniques: Critical Review”, 2015.

      [10] Martin Shepperd, David Bowes and Tracy Hall,” Researcher Bias: The Use of Machine Learning in Software Defect Prediction”, 2014.

      [11] T. Hall, S. Beecham, D. Bowes, D. Gray, and S. Counsell, “A systematic literature review on fault prediction performance in software engineering,” , 2012.

      [12] Chih-Chung Chang and Chih-Jen Lin,”LIBSVM: A Library for Support Vector Machines”, last Updated 2013.

      [13] Pradeep Kumar and Yogesh Singh,” An empirical survey of software reliability prediction using machine learning techniques”, 2012.

      [14] Malhotra R, Kaur A, Singh Y , “Empirical validation of object oriented metrics for predicting fault proneness at different severity levels using support vector machines”, 2011 .

      [15] Malhotra R, Singh Y, Kaur A “Comparative analysis of regression and machine learning methods for predicting fault proneness models”, 2009.

      [16] H. Pham, “System Software Reliability”, Reliability Engineering Series, Springer, 2006.

      [17] Ji, S., Xu, W., Yang, M., Yu, K., “3D Convolutional Neural Networks for Human Action Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence” 2013.


 

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Article ID: 15567
 
DOI: 10.14419/ijet.v7i2.32.15567




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