Fault detection technique for test cases in software engineering

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    The processing of software and performing various operations on it is known as a software engineering process. The application of test cases for detecting the faults within the software is done through the testing process. There are various types of faults that occur within a software or test case which are to be identified and preventive approaches are to be applied to prevent them. In this paper, the Learn-to-rank algorithm is utilized which helps in detecting the faults from the software. The Back-Propagation technique is included with the LRA approach for enhancing its performance and improving the detection of fault rate. 10 test cases of different types are used for running various experiments and the MATLAB tool is utilized for performing various simulations. It is seen through the various simulation results that the fault detection rate is increased as well as the execution time is minimized with the help of this approach. 


  • Keywords


    : Faults; Test Cases; Neural Networks; Back Propagation; Learn-to-Rank.

  • References


      [1] Graves T. L. , Karr A. F. , Marron J. S. , and Siy H. , “Predicting fault incidence using software change history,” in Proc. IEEE Trans. Softw. Eng., Vol.26, no. 7, pp. 653–661, 2000. https://doi.org/10.1109/32.859533.

      [2] Ostrand T. J., Weyuker E. J., and Bell R. M., “Predicting the location and number of faults in large software systems,” IEEE Trans. Softw. Eng., Vol. 31, no. 4, pp. 340–355, 2005. https://doi.org/10.1109/TSE.2005.49.

      [3] Gao K. and Khoshgoftaar T.M. , “A comprehensive empirical study of count models for software defect prediction,” in Proc. IEEE 28th Int. Conf. Trans. Rel., Vol. 56, no. 2, pp. 223–236, June. 2007

      [4] Zimmermann T. , Premraj R. , and Zeller A. , “Predicting defects for eclipse,” in Proc. IEEE Int. Workshop Predictor Models in Software Engineering(PROMISE'07), pp. 9–15, 2007. https://doi.org/10.1109/PROMISE.2007.10.

      [5] Jiang Y., Cukic B., and Ma Y., “Techniques for evaluating fault prediction models,” in Proc. Empiric. Softw. Eng., Vol. 13, no. 5, pp. 561–595, 2008. https://doi.org/10.1007/s10664-008-9079-3.

      [6] Lessmann S. , Baesens B. , Mues C. , and Pietsch S. , “Benchmarking classification models for software defect prediction: A proposed frame work and novel findings,” in Proc. IEEE Trans. Software Engineering., Vol. 34, no. 4, pp. 485–496, 2008. https://doi.org/10.1109/TSE.2008.35.

      [7] Moser R. , Pedrycz W. , and Succi G. , “A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction,” in Proc. ACM/IEEE 30th Int. Conf. Software Engineering, pp. 181–190 , Dec.2008. https://doi.org/10.1145/1368088.1368114.

      [8] Mende T., and Koschke R., “Revisiting the evaluation of defect prediction models,” in Proc. 5th Int. Conf. Predictor Models in Software Engineering, 2009, pp. 1–10. https://doi.org/10.1145/1540438.1540448.

      [9] Arisholm E. , Briand L. C. , and Johannessen E. B., “A systematic and comprehensive investigation of methods to build and evaluate fault prediction models,” in Proc. J. Syst. Softw., Vol. 83, no. 1, pp. 2–17, 2010. https://doi.org/10.1016/j.jss.2009.06.055.

      [10] Weyuker E.G., Ostrand T. J. and Bell R. M., “Comparing the effectiveness of several modeling methods for fault prediction,” in Proc. IEEE Int. J. Empiric. Softw. Eng., Vol. 15, no. 3, pp. 277–295, 2010. https://doi.org/10.1007/s10664-009-9111-2.

      [11] D'Ambros M., Lanza M., and. Robbes R., “Evaluating defect prediction approaches:A benchmark and an extensive comparison,” in Proc. IEEE Conf. Softw. Eng., pp. 1–47, 2011

      [12] Wang H., Khoshgoftaar T. M., and Seliya N., “How many software metrics should be selected for defect prediction,” in Proc. 24th Int. Florida Artificial Intelligence Research Society Conf., pp. 69–74, 2011.

      [13] Khoshgoftaar T. M., Gao K., and Napolitano A., “An empirical study of feature ranking techniques for software quality prediction,” in Proc. IEEE Int. J. Softw. Eng. Knowl. Eng., Vol. 22, no. 2, pp. 161–183, 2012. https://doi.org/10.1142/S0218194012400013.

      [14] Wang Y., Cai Z., and Zhang Q., “Differential evolution with composite trial vector generation strategies and control parameters,” in Proc. IEEE Trans. Evol. Computat, Vol. 15, no. 1, pp. 55–66, 2011. https://doi.org/10.1109/TEVC.2010.2087271.

      [15] Rawat S. M, Dubey K .S, “Software Defect Prediction Models for Quality Improvement: A Literature Study”, in Proc. International Journal of Computer Science Issues, Vol. 9, Issue 5, No 2, September 2012 ISSN (Online): 1694-0814.

      [16] Yang X., Tang K., and Yao X., “A effective algorithm for constructing defect prediction models,” Int. J. in Intelligent Data Engineering and Automated Learning-IDEAL, pp. 167–175, 2012.


 

View

Download

Article ID: 7870
 
DOI: 10.14419/ijet.v7i1.7870




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