Skip to main content

Advertisement

SpringerLink
Log in

Prediction of Software Reliability using Bio Inspired Soft Computing Techniques

  • Systems-level quality improvement
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

A lot of models have been made for predicting software reliability. The reliability models are restricted to using particular types of methodologies and restricted number of parameters. There are a number of techniques and methodologies that may be used for reliability prediction. There is need to focus on parameters consideration while estimating reliability. The reliability of a system may increase or decreases depending on the selection of different parameters used. Thus there is need to identify factors that heavily affecting the reliability of the system. In present days, reusability is mostly used in the various area of research. Reusability is the basis of Component-Based System (CBS). The cost, time and human skill can be saved using Component-Based Software Engineering (CBSE) concepts. CBSE metrics may be used to assess those techniques which are more suitable for estimating system reliability. Soft computing is used for small as well as large-scale problems where it is difficult to find accurate results due to uncertainty or randomness. Several possibilities are available to apply soft computing techniques in medicine related problems. Clinical science of medicine using fuzzy-logic, neural network methodology significantly while basic science of medicine using neural-networks-genetic algorithm most frequently and preferably. There is unavoidable interest shown by medical scientists to use the various soft computing methodologies in genetics, physiology, radiology, cardiology and neurology discipline. CBSE boost users to reuse the past and existing software for making new products to provide quality with a saving of time, memory space, and money. This paper focused on assessment of commonly used soft computing technique like Genetic Algorithm (GA), Neural-Network (NN), Fuzzy Logic, Support Vector Machine (SVM), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). This paper presents working of soft computing techniques and assessment of soft computing techniques to predict reliability. The parameter considered while estimating and prediction of reliability are also discussed. This study can be used in estimation and prediction of the reliability of various instruments used in the medical system, software engineering, computer engineering and mechanical engineering also. These concepts can be applied to both software and hardware, to predict the reliability using CBSE.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Wang, W. L., Chen, M. H., Heterogeneous software reliability modeling. 13th International Symposium on Software Reliability Engineering(ISSRE): 41–52, 2003.

  2. Zheng, J., Predicting software reliability with neural network ensembles. Expert Syst. Appl. 36(2):2116–2122, 2009.

    Article  Google Scholar 

  3. Singh, Y., and Kumar, P., Application of feed-forward neural networks for software reliability prediction. ACM SIGSOFT Softw. Eng. Notes 35(5):1–6, 2010.

    Article  Google Scholar 

  4. Bisi, M., and Goyal, N. K., Software reliability prediction using neural network with encoded input. Int. J. Comput. Appl. 47(22):46–52, 2012.

    Google Scholar 

  5. Arora, M., and Choudhary, S., Software reliability prediction using neural network. Int. J. Softw. Web Sci. 5(2):88–92, 2013.

    Google Scholar 

  6. Kumar, D., Kansal, Y., Kapur, P. K., Integrating Neural Networks with Software Reliability. 3rd International Conference on Computing for Sustainable Global Development (INDIACom): 4072–4077, 2016.

  7. Ramasamy, S., and Lakshmanan, I., Application of artificial neural network for software reliability growth modeling with testing effort. Indian J. Sci. Technol. 9(29):1–7, 2016.

    Article  Google Scholar 

  8. Aljahdali, S. H., El-Telbany, M. E, Software reliability prediction using multi-objective genetic algorithm. International Conference on Computer Systems and Applications(AICCSA 2009:293–300, 2009.

  9. Kim, T., Lee, K., and Baik, J., An effective approach to estimating the parameters of software reliability growth models using a real-valued genetic algorithm. J. Syst. Softw. 102:134–144, 2015.

    Article  Google Scholar 

  10. Sharma, A., Rishon, P., and Aggarwal, A., Software testing using genetic algorithms. Int. J. Comput. Sci. Eng. Surv 7(2):21–33, 2016.

    Article  Google Scholar 

  11. Fazel, F. S., A new method to predict the software fault using improved genetic algorithm. Bull. la Société R. des Sci. Liège 85:187–202, 2016.

    Google Scholar 

  12. Rotshtein, A., Katielnikov, D., Pustylnik, L., Reliability modeling and optimization using fuzzy logic and chaos theory. Int. J. Qual. Stat. Reliab.: 1–10, 2012.

  13. Qian, L., Methodology on qualitative simulation modeling of software reliability based on chaos theory. 5th IEEE International Conference on Software Engineering and Service Science (ICSESS): pp. 99–104, 2014.

  14. Tong, H., Han, R., Liu, B., Xu, B., Software reliability prediction using chaos theory and heterogeneous ensemble learning. Risk, Reliab. Saf. Innov. Theory Pract.:2396–2402, 2017.

  15. Becerra, R. L., Sagarna, R., Yao, X. An evaluation of differential evolution in software test data generation, IEEE Congress on in Evolutionary Computation( CEC’09): 2850–2857, 2009.

  16. Nasar, M., Johri, P., and Chanda, U., A differential evolution approach for software testing effort allocation. J. Ind. Intell. Inf. 1(2):111–115, 2013.

    Google Scholar 

  17. Mahadevan, S., and Rebba, R., Validation of reliability computational models using bayes networks. Reliab. Eng. Syst. Saf. 87(2):223–232, 2005.

    Article  Google Scholar 

  18. Bai, C. G., Bayesian network-based software reliability prediction with an operational profile. J. Syst. Soft. 77(2):103–112, 2005.

    Article  Google Scholar 

  19. Pai, P. F., and Hong, W. C., Software reliability forecasting by support vector machines with simulated annealing algorithms. J. Syst. Softw. 79(6):747–755, 2006.

    Article  Google Scholar 

  20. Elish, K. O., and Elish, M. O., Predicting defect-prone software modules using support vector machines. J. Syst. Softw. 81(5):649–660, 2008.

    Article  Google Scholar 

  21. Kiran, N. R., and Ravi, V., Software reliability prediction by soft computing techniques. J. Syst. Softw. 81(4):576–583, 2008.

    Article  Google Scholar 

  22. Fenton, N., Neil, M., and Marquez, D., Using bayesian networks to predict software defects and reliability. Proc. Inst. Mech. Eng. Part O J. Risk Reliab. 222(4):701–712, 2008.

    Google Scholar 

  23. Doguc, O., and Ramirez-Marquez, J. E., A generic method for estimating system reliability using bayesian networks. Reliab. Eng. Syst. Saf. 94(2):542–550, 2009.

    Article  Google Scholar 

  24. Aljahdali, S., Sheta, A. F., Predicting the Reliability of Software Systems Using Fuzzy Logic,” 8th International Conference on Information Technology: New Generations (ITNG):36–40, 2011.

  25. Moura, M. D. C., Zio, E., Lins, I. D., and Droguett, E., Failure and reliability prediction by support vector machines regression of time series data. Reliab. Eng. Syst. Saf. 96(11):1527–1534.

  26. Singh, H., and Toora, V. K., Neuro fuzzy logic model for component-based software engineering. Int. J. Eng. Sci. 1:303–314, 2011.

    Google Scholar 

  27. Rana, S., and Yadav, R. K., A fuzzy improved association mining approach to estimate software quality. Int. J. Comput. Sci. Mob. e Comput. 2(6):116–122, 2013.

    Google Scholar 

  28. Ziauddin, N., Khan, S., and Nasir, J. A., A fuzzy logic based software cost estimation model. Int. J. Softw. Eng. It's Appl. 7(2):7–18, 2013.

    Google Scholar 

  29. Yang, S., Lu, M., Ge, L., Bayesian network-based software reliability prediction by dynamic simulation. 7th IEEE International Conference on Software Security and Reliability (SERE):13–20, 2013.

  30. Khosla, A., and Soni, L. G. M. K., Comparison of ABC and ant colony algorithm based fuzzy controller for an inverted pendulum. Int. J. Innov. Technol. Explore. Eng. 3(2):123–234, 2013.

    Google Scholar 

  31. Elloumi, W., Baklouti, N., Abraham, A., Alimi, A. M., Hybridization of fuzzy PSO and fuzzy ACO applied to TSP, 13th International Conference on Hybrid Intelligent Systems (HIS):105–110, 2013.

  32. Shanmugam, L., and Florence, L., Enhancement and comparison of ant colony optimization for software reliability models. J. Comput. Sci. 9(9):1232–1240, 2013.

    Article  Google Scholar 

  33. Roopa, Y. M., and Reddy, A. R. M., Particle swarm optimization approach for component-based software architecture. Int. J. 3(12):557–561, 2013.

    Google Scholar 

  34. Singh, C., Pratap, A., Singhal, A., An estimation of software reusability using fuzzy logic technique. International Conference on Signal Propagation and Computer Technology (ICSPCT):250–256, 2014.

  35. Okutan, A., and Yıldız, O. T., Software defect prediction using bayesian networks. Empir. Softw. Eng. 19(1):154–181, 2014.

    Article  Google Scholar 

  36. Tyagi, K., and Sharma, A., An adaptive neuro-fuzzy model for estimating the reliability of component-based software systems. Appl. Comput. Informatics 10(1):38–51, 2014.

    Article  Google Scholar 

  37. Lal, R., and Kumar, N., Design and analysis of reliability for component-based software system by using soft computing approaches. Int. J. Emerg. Technol. Adv. Eng. 4(6):929–932, 2014.

    Google Scholar 

  38. Tyagi, K., and Sharma, A., A heuristic model for estimating component-based software system reliability using ant colony optimization. World Appl. Sci. J. 31(11):1983–1991, 2014.

    Google Scholar 

  39. Diwaker, C., Rani, S., Tomar, P., Metrics used in component-based software engineering. IJITKM Spec. Issue: 46–50, 2014.

  40. Jaiswal, G. P., and Giri, R. N., Software reliability estimation of component-based software system using fuzzy logic. Int. J. Comput. Sci. Inf. Secure. 127(7):16–20, 2015.

    Google Scholar 

  41. Diwaker, C., and Tomar, P., Assessment of Ant Colony using Component-Based Software Engineering Metrics. Indian J. Sci. Technol. 9(44):1–5, 2016.

    Article  Google Scholar 

  42. Diwaker, C., Tomar, P., Optimization and appraisal of PSO for CBS using CBSE metrics. 3rd International Conference on Computing for Sustainable Global Development (INDIACom:1024–1028, 2016.

  43. Bolisetty, P. K., and Yalla, P., An efficient component-based software architecture model using hybrid PSO–CS algorithm. Int. J. Intelligen t Eng. Syst. 9(3):46–52, 2016.

    Article  Google Scholar 

  44. Diwaker, C., and Tomar, P., Evaluation of swarm optimization techniques using CBSE reusability metrics. IJCTA 2(22):189–197, 2016.

    Google Scholar 

  45. Preethi, W., Rajan, M. R. B., Survey on Different Strategies for Software Reliability Prediction. International Conference on Circuit, Power and Computing Technologies (ICCPCT): 1–3, 2016.

  46. Rizvi, S. W. A., Khan, R. A., and Singh, V. K., Software reliability prediction using fuzzy inference system: early stage perspective. Int. J. Comput. Appl. 145(10):16–23, 2016.

    Google Scholar 

  47. Rizvi, S. W. A., Singh, V. K., and Khan, R. A., Fuzzy logic based software reliability quantification framework: early stage perspective (FL SRQF). Procedia Comput. Sci. 89:359–368, 2016.

    Article  Google Scholar 

  48. Dubey, S. K., Jasra, B., Reliability assessment of component-based software systems using fuzzy and ANFIS techniques. Int. J. Syst. Assur. Eng. Manag. 8(2):1319–1326, 2017.

  49. Sharma, R. K., and Gandhi, P., Estimate reliability of component-based software sys-tem using modified neuro-fuzzy model. Int. J. Eng. Technol. 6(2):45–49, 2017.

    Article  Google Scholar 

  50. Hermawanto, D., Genetic algorithm for solving simple mathematical equality problem. 1–9, 2013.

  51. Albertos, P., Sala, A., Fuzzy logic controllers: advantages and drawbacks. 8 th International Congress of Automatic Control, 3: 833–844, 1998.

  52. Awodele, O., Jegede, O., Neural networks and its application in engineering, Proceedings of Informing Science & IT Education Conference (InSITE):83–95, 2009.

  53. Haykin, S. S., Neural Networks and Learning Machines. NJ: Pearson Upper Saddle River, 2009.

    Google Scholar 

  54. Tu, J. V., Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J. Clin. Epidemiol. 49(11):1225–1231, 1996.

    Article  PubMed  CAS  Google Scholar 

  55. Das, S., Abraham, A., Konar, A., Particle swarm optimization and differential evolution algorithms: technical analysis, applications and hybridization perspectives. Adv. Comput. Intell. Ind. Syst: 1–38, 2008.

  56. Karamizadeh, S., Abdullah, S. M., Halimi, M., Shayan, J., Javad, R. M., Advantage and Drawback of Support Vector Machine Functionality. International Conference on Computer, Communications, and Control Technology (I4CT: 63–65, 2014.

  57. Katiyar, S., Ibraheem, N., and Ansari, A. Q., Ant colony optimization: a tutorial review. MR Int. J. Eng. Technol. 7(2):35–41, 2015.

    Google Scholar 

  58. Karaboga, D., and Akay, B., A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214(1):108–132, 2009.

    Google Scholar 

  59. Diwaker, C., and Tomar, P., Identification of factors and techniques to design and develop component-based reliability model. Int. J. Sci. Res. Comput. Sci. Eng. 5(3):107–114, 2017.

    Google Scholar 

  60. Tyagi, K., and Sharma, A., Significant factors for reliability estimation of component-based software systems. J. Softw. Eng. Appl. 7(11):934–943, 2014.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Kurukshetra University, Kurukshetra, India

    Chander Diwaker

  2. Department of Computer Science and Engineering, School of ICT, Gautam Buddha University, Greater Noida, UP, India

    Pradeep Tomar

  3. Amity Institute of Information Technology, Amity University Rajasthan, Jaipur, India

    Ramesh C. Poonia & Vijander Singh

Authors
  1. Chander Diwaker
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pradeep Tomar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ramesh C. Poonia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vijander Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vijander Singh.

Additional information

This article is part of the Topical Collection on Systems-Level Quality Improvement

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Diwaker, C., Tomar, P., Poonia, R.C. et al. Prediction of Software Reliability using Bio Inspired Soft Computing Techniques. J Med Syst 42, 93 (2018). https://doi.org/10.1007/s10916-018-0952-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-018-0952-3

Keywords

Search

Navigation