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A Comprehensive Review on Software Reliability Growth Models utilizing Soft Computing Approaches

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Intelligent Systems Technologies and Applications 2016 (ISTA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 530))

Abstract

Software Reliability Engineering is an area that created from family history in the dependability controls of electrical, auxiliaryAbstract, and equipment building. Reliability models are the most prevailing devices in Programming Dependability Building for approximating, insidious, gauging, and assessing the unwavering quality of the product. In order to attain solutions to issues accurately, speedily and reasonably, a huge amount of soft computing approaches has been established. However, it is extremely difficult to discover among the capabilities which is the utmost one that can be exploited all over. These various soft computing approaches can able to give better prediction, dynamic behavior, and extraordinary performance of modelling capabilities. In this paper, we show a wide survey of existing delicate processing methodologies and after that diagnostically inspected the work which is finished by various analysts in the area of software reliability.

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References

  1. Agarwal kk, Singh Y “Software Engineering.”, New Age International Publisher, New Delhi, 2005.

    Google Scholar 

  2. Hsu, Chao-Jung, and Chin-Yu Huang.”Optimal weighted combinational models for software reliability estimation and analysis.” Reliability, IEEE Transactions, Vol. 63, No. 3, pp. 731-749.

    Google Scholar 

  3. Mohanty, Ramakanta, V. Ravi, and ManasRanjanPatra. “Software reliability prediction using group method of data handling.” Springer, Berlin, Heidelberg on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, pp. 344-351, 2009.

    Google Scholar 

  4. Lakshmanan, Indhurani, and SubburajRamasamy. “An artificial neural-network approach to software reliability growth modeling.” Procedia Computer Science Vol.57, pp. 695-702, 2015.

    Google Scholar 

  5. Kapur, P. K., and R. B. Garg”Cost, reliability optimum release policies for a software system with testing effort.” Operations Research, Vol. 27, No. 2, pp. (109-116).

    Google Scholar 

  6. Musa JD “Software Reliability Engineering: More Reliable Software, Faster Development and Testing” McGraw-Hill, 1999.

    Google Scholar 

  7. Musa JD, Iannino A, Komodo K “Software Reliability: Measurement, Prediction and Application.” McGraw-Hill, 1987.

    Google Scholar 

  8. Chiu K. C, Huang, Y. S, and Lee T. Z,” A study of software reliability growth from the perspective of learning effects.” Reliability Engineering & System Safety, Vol.93, No.10, pp. 1410-1421, 2008.

    Google Scholar 

  9. Chang Y. C, and Liu C. T, “A generalized JM model with applications to imperfect debugging in software reliability.” Applied Mathematical Modelling, Vol.33, No.9, pp. 3578-3588, 2009.

    Google Scholar 

  10. Upadhyay R, and Johri P, “Review on Software Reliability Growth Models and Software Release Planning.” International Journal of Computer Applications, Vol.73, No.12, 2013.

    Google Scholar 

  11. CagatayCatal, “Software fault prediction: A literature review and current trends”, Expert systems with applications, Vol.38, No.4, pp. 4626-36, 2011.

    Google Scholar 

  12. Thanh-TrungPham, XavierDefago and Quyet-ThangHuynh, “Reliability prediction for component-based software systems: Dealing with concurrent and propagating errors”, Science of Computer Programming, Vol.97, pp. 426-57, 2015.

    Google Scholar 

  13. Zio E, “Reliability engineering: Old problems and new challenges”, Reliability Engineering and System Safety, Vol.94, No.2, pp. 125-41, 2009.

    Google Scholar 

  14. Ahmet Okutan and OlcayTanerYıldız, “Software defect prediction using Bayesian networks”, Empirical Software Engineering, Vol.19, No.1, pp. 154-181, 2014.

    Google Scholar 

  15. Bonissone P, Chen YT, Goebel, K, Khedkar P “Hybrid Soft Computing Systems: Industrial and Commercial Applications.” Proceedings of the IEEE, Vol.87, pp. 1641-1667, 1999.

    Google Scholar 

  16. Das, Santosh Kumar, Abhishek Kumar, Bappaditya Das, and A. P. Burnwal”On soft computing techniques in various areas.” Int J Inform Tech ComputSci, Vol. 3, pp. 59-68.

    Google Scholar 

  17. Streichert F “Introduction to Evolutionary Algorithms.” Frankfurt Math Finance Workshop, University of Tubingen, Germany, 2002.

    Google Scholar 

  18. Sydenham, Peter H., and Richard Thorn, “Handbook of measuring system design”, Vol. 2

    Google Scholar 

  19. Boccaletti, S., Grebogi, C., Lai, Y.C., Mancini, H. And Maza, D, “The control of chaos: theory and applications.” Physics reports Vol.329, No. 3, pp. 103-197.

    Google Scholar 

  20. Su Y. S, and Huang C. Y, “Neural-network-based approaches for software reliability estimation using dynamic weighted combinational models. “Journal of Systems and Software, Vol.80, No.4, pp. 606-615, 2007.

    Google Scholar 

  21. Karunanithi, Nachimuthu, Yashwant K. Malaiya, and Darrell Whitley. “Prediction of software reliability using neural networks.” Software Reliability Engineering,International Symposium, IEEE, pp. 124-130.

    Google Scholar 

  22. Karunanithi, Nachimuthu, Darrell Whitley, and Yashwant K. Malaiya, “Using neural networks in reliability prediction.” IEEE, Vol. 9, No. 4, pp. 53-59.

    Google Scholar 

  23. Cai KY, Cai L, Wang WD, Yu ZY, Zhang D, “On the neural network approach in software reliability modeling.”, Journal of Systems and Software, Vol.58, No. 1, pp. 47-62.

    Google Scholar 

  24. Tian L, Noore A “Evolutionary neural network modeling for software cumulative failure time prediction.” Reliability Engineering and System Safety, Vol.87, pp. 45-51, 2005.

    Google Scholar 

  25. Aljahdali AS, Buragga KB “Employing four ANNs Paradigms for Software Reliability Prediction: an Analytical Study.” ICGST-AIML Journal, 2008.

    Google Scholar 

  26. Singh, Yogesh, and Pradeep Kumar “Prediction of software reliability using feed forward neural networks.” Computational Intelligence and Software Engineering (CiSE), IEEE, pp. (1-5).

    Google Scholar 

  27. Khoshgoftaar TM, Szabo RM, Guasti PJ “Exploring the Behavior of Neural-network Software Quality Models.” Software Engg J, Vol.10, pp. 89-96, 1995

    Google Scholar 

  28. Sitte R “Comparison of Software Reliability Growth Predictions: Neural networks vs. parametric recalibration.” IEEE transactions on Reliability, Vol. 48, pp. 285- 291, 1999.

    Google Scholar 

  29. Jain, Sandeep Kumar, and Manu Pratap Singh”Estimation for Faults Prediction from Component Based Software Design using Feed Forward Neural Networks.”, 2013.

    Google Scholar 

  30. Aljahdali, Sultan, and Narayan C. Debnath,”Improved Software Reliability Prediction through Fuzzy Logic Modeling.”, 2004.

    Google Scholar 

  31. Cai KY, Wen CY, Zhang ML “A critical review of software reliability modeling.” Reliability Engineering and System Safety, Vol. 32, pp. 357-371, 1991.

    Google Scholar 

  32. Reformat, Marek. “A fuzzy‐based multimodal system for reasoning about the number of software defects.”, International journal of intelligent systems, Vol.20, No. 11, pp. 1093-1115, 2005.

    Google Scholar 

  33. Aljahdali, Sultan “Development of software reliability growth models for industrial applications using fuzzy logic.” Journal of Computer Science, Vol.7, No. 10, pp. 1574.

    Google Scholar 

  34. Chatterjee, S., Nigam, S., Singh, J.B. And Upadhyaya, L.N. “Application of fuzzy time series in prediction of time between failures & faults in software reliability assessment.”Fuzzy Information and Engineering, Vol.3, No. 3, pp. 293-309, 2011.

    Google Scholar 

  35. Tyagi, Kirti, and Arun Sharma “An adaptive Neuro fuzzy model for estimating the reliability of component-based software systems.”applied Computing and informatics, Vol.10, No. 1, pp. 38-51, 2014.

    Google Scholar 

  36. Tian L, Noore A “On-line prediction of software reliability using an evolutionary connectionist model.” Journal of Systems and Software, Vol.77: pp. 173- 180, 2005.

    Google Scholar 

  37. Aljahdali, Sultan H., and Mohammed E. El-Telbany “Software reliability prediction using multi-objective genetic algorithm.” Computer Systems and Applications, IEEE/ACS International Conference, pp. (293-300), 2009.

    Google Scholar 

  38. SatyaPrasad, R., O. NagaRaju, and R. R. LKantam. “SRGM with Imperfect Debugging by Genetic Algorithms.”

    Google Scholar 

  39. Costa, Eduardo Oliveira, Silvia R. Vergilio, Aurora Pozo, and Gustavo Souza. “Modeling software reliability growth with genetic programming.” Software Reliability Engineering,IEEE International Symposium, ISSRE, (pp. 10-pp) 2005.

    Google Scholar 

  40. Oliveira, E. O., Aurora Pozo, and Silvia Regina Vergilio, “Using boosting techniques to improve software reliability models based on genetic programming.”, Tools with Artificial Intelligence, IEEE International Conference, ICTAI, pp. (653-650),2006.

    Google Scholar 

  41. Yongqiang, Zhang, and Chen Huashan. “Predicting for MTBF failure data series of software reliability by genetic programming algorithm.” Intelligent Systems Design and Applications, ISDA’06. Sixth International Conference, Vol. 1, pp. (666-670), 2006.

    Google Scholar 

  42. Costa, Eduardo Oliveira, Aurora Trinidad Ramirez Pozo, and Silvia Regina Vergilio. “A genetic programming approach for software reliability modeling.” Reliability,IEEE Transactions, Vol. 59, No. 1, pp. (222-230), 2010.

    Google Scholar 

  43. Al-Rahamneh, Zainab, Mohammad Reyalat, Alaa F. Sheta, SuliemanBani-Ahmad, and Saleh Al-Oqeili, “A New Software Reliability Growth Model: Genetic-Programming-Based Approach.” Journal of Software Engineering and Applications, Vol.4, No. 8, pp. 476, 2011.

    Google Scholar 

  44. Sharma, Tarun Kumar, Millie Pant, and Ajith Abraham “Dichotomous search in ABC and its application in parameter estimation of software reliability growth models.”Nature and Biologically Inspired Computing (NaBIC), Third World Congress, IEEE, (pp. 207-212), 2011.

    Google Scholar 

  45. Li, Weili, Qiaoyu Yin, and Xiaochen Zhang, “Continuous quantum ant colony optimization and its application to optimization and analysis of induction motor structure.” Bio-Inspired Computing: Theories and Applications (BIC-TA), Fifth International Conference, IEEE, (pp. 313-317), 2010.

    Google Scholar 

  46. Shanmugam, Latha, and Lilly Florence, “A Comparison of Parameter best Estimation Method for software reliability models.”International Journal of Software Engineering & Applications, Vol. 3, No. 5, pp. 91, 2012.

    Google Scholar 

  47. Shanmugam, Latha, and Lilly Florence, “Enhancement and comparison of ant colony optimization for software reliability models.” Journal of Computer Science, Vol. 9, No. 9, pp. 1232, 2013.

    Google Scholar 

  48. Kirkpatrick S, Gelatt C, Vecchi M “Optimization by simulated annealing.” Science, Vol.220, pp. 498– 516, 1983.

    Google Scholar 

  49. Pai PF, Hong WC “Software reliability forecasting by Support vector machines with simulated vector machines with simulated annealing algorithms.” The Journal of Systems and Software, Vol.79, pp. 747–755, 2006.

    Google Scholar 

  50. AL-Saati, Dr, NajlaAkram, and Marwa Abed-AlKareem, “The Use of Cuckoo Search in Estimatingthe Parameters of Software Reliability Growth Models.” 2013.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Research Scholar, Research and Development Centre, Bharathiar University, Coimbatore,, India

    Shailee Lohmor

  2. Assistant Professor, Birla Institute of Technology, Mesra- Ranchi, India

    Dr. B B Sagar

Authors
  1. Shailee Lohmor
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  2. Dr. B B Sagar
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Corresponding author

Correspondence to Shailee Lohmor .

Editor information

Editors and Affiliations

  1. Dept. of CS, School of Science, University of Salamanca Dept. of CS, School of Science, Salamanca, Spain

    Juan Manuel Corchado Rodriguez

  2. Machine Intelligence Unit, Indian Statistical Institute Machine Intelligence Unit, Kolkata, India

    Sushmita Mitra

  3. and Management – Kerala (IIITM-K), Indian Inst. of Information Technology and Management – Kerala (IIITM-K), Trivandrum, Kerala, India

    Sabu M. Thampi

  4. King Fahd Univ of Petroleum and Minerals , Dhahran, Saudi Arabia

    El-Sayed El-Alfy

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Lohmor, S., Sagar, B.B. (2016). A Comprehensive Review on Software Reliability Growth Models utilizing Soft Computing Approaches. In: Corchado Rodriguez, J., Mitra, S., Thampi, S., El-Alfy, ES. (eds) Intelligent Systems Technologies and Applications 2016. ISTA 2016. Advances in Intelligent Systems and Computing, vol 530. Springer, Cham. https://doi.org/10.1007/978-3-319-47952-1_40

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  • DOI: https://doi.org/10.1007/978-3-319-47952-1_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47951-4

  • Online ISBN: 978-3-319-47952-1

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