SpringerLink
Log in

Enhancing software reliability prediction using fuzzy AHP-based mathematical model and ANN integration

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

In recent years, intensive research in software engineering has been dedicated to predict the reliability of software systems. Multiple methodologies have been explored to evaluate and estimate the reliability of software systems. Software developers can streamline the process of creating new software by incorporating crucial elements such as reusability, component interaction, component dependency, component complexity, and failure. This study introduces two innovative models for software reliability assessment. The first model utilizes a mathematical framework, considering five key factors—reusability, component interaction, component dependency, component complexity, and failure—to construct a comprehensive mathematical model for software reliability assessment. The incorporation of Fuzzy Analytical Hierarchy Process is employed to determine the pertinent weights of these factors, thus contributing to a nuanced evaluation of software reliability. The second model leverages the power of Artificial Neural Network for software reliability assessment. Both proposed models exhibit superior reliability values when compared to various existing models. Notably, the average reliability scores computed across 100 programs for the proposed AHP-based model and ANN-based model are 0.438269 and 0.416136, respectively.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

The data used in this article is taken from [15].

References

  1. Sharma S, Vijayvargiya S (2022) Modeling of software project effort estimation: a comparative performance evaluation of optimized soft computing-based methods. Int J Inf Technol 14:2487–2496. https://doi.org/10.1007/s41870-022-00962-5

    Article  Google Scholar 

  2. Garg D, Dahiya T, Shrivastava AK (2022) Develo** a new heuristic algorithm for efficient reliability optimization. Int J Inf Technol 14:2505–2511. https://doi.org/10.1007/s41870-022-00975-0

    Article  Google Scholar 

  3. Siddiqui T, Mustaqeem Mv (2023) Performance evaluation of software defect prediction with NASA dataset using machine learning techniques. Int J Inf Technol 15:4131–4139. https://doi.org/10.1007/s41870-023-01528-9

    Article  Google Scholar 

  4. Mohammad CW, Shahid M, Hussain SZ (2021) Fuzzy attributed goal oriented software requirements analysis with multiple stakeholders. Int J Inf Technol 13:1–9. https://doi.org/10.1007/s41870-017-0073-0

    Article  Google Scholar 

  5. Pham H (2007) System software reliability. Springer Science & Business Media, Berlin

    Google Scholar 

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

    Google Scholar 

  7. Yakovyna V, Seniv M, Symets I (2020) The relation between software development methodologies and factors affecting software reliability. In: 2020 IEEE 15th international conference on computer sciences and information technologies (CSIT), vol 1. IEEE, pp 377–381. https://doi.org/10.1109/CSIT49958.2020.9321937

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

    Google Scholar 

  9. Lal R, Kumar N (2014) 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

    Google Scholar 

  10. Tyagi K, Sharma A (2012) A rule-based approach for estimating the reliability of component-based systems. Adv Eng Softw 54:24–29

    Article  Google Scholar 

  11. Thakur P, Sharma SK (2020) Estimation of complexity in software reliability growth modeling. Adv Appl Math Sci 19(6):563–572

    Google Scholar 

  12. Awasthia V, Sharma SK (2021) A study of various software reliability systems by using ANN. J Univ Shanghai Sci Technol 23(7):968–976

    Google Scholar 

  13. Zhen L, Liu Y, Dongsheng W, Wei Z (2020) Parameter estimation of software reliability model and prediction based on hybrid wolf pack algorithm and particle swarm optimization. IEEE Access 8:29354–29369

    Article  Google Scholar 

  14. Lin JS, Huang CY (2022) Queueing-based simulation for software reliability analysis. IEEE Access 10:107729–107747

    Article  Google Scholar 

  15. Diwaker C, Tomar P, Solanki A, Nayyar A, Jhanjhi NZ, Abdullah A, Supramaniam M (2019) A new model for predicting component-based software reliability using soft computing. IEEE Access 7:147191–147203

    Article  Google Scholar 

  16. Aloysius A, Maheswaran K (2015) A review on component based software metrics. Int J Fuzzy Math Arch 7(2):185–194

    Google Scholar 

  17. Yacoub S, Cukic B, Ammar HH (2004) A scenario-based reliability analysis approach for component-based software. IEEE Trans Reliab 53(4):465–480

    Article  Google Scholar 

  18. Chatterjee S, Singh JB, Roy A (2015) A structure-based software reliability allocation using fuzzy analytic hierarchy process. Int J Syst Sci 46(3):513–525

    Article  MathSciNet  Google Scholar 

  19. Sofian H, Yunus NAM, Ahmad R (2022) Systematic map**: Artificial intelligence techniques in software engineering. IEEE Access 10:51021–51040

    Article  Google Scholar 

  20. Dam HK (2019) Artificial intelligence for software engineering. XRDS: Crossroads, The ACM Magazine for Students 25(3):34–37

  21. Wangoo DP (2018) Artificial intelligence techniques in software engineering for automated software reuse and design. In: 2018 4th International conference on computing communication and automation (ICCCA). IEEE, pp 1–4

  22. Ahmad A, Feng C, Khan M, Khan A, Ullah A, Nazir S, Tahir A (2020) A systematic literature review on using machine learning algorithms for software requirements identification on stack overflow. Secur Commun Netw 2020:1–19

    Google Scholar 

  23. Alsolai H, Roper M (2020) A systematic literature review of machine learning techniques for software maintainability prediction. Inf Softw Technol 119:106214

    Article  Google Scholar 

  24. Babu S, Singh R (2022) Neural network-based model for the quality assessment of object-oriented software. Int J Open Source Softw Process (IJOSSP) 13(1):1–13

    Article  Google Scholar 

  25. Jasra B, Dubey SK (2019) Reliability assessment of component-based software system using fuzzy-AHP. In: Software engineering: proceedings of CSI 2015. Springer Singapore, Singapore, pp 663–670

  26. Goswami P, Noorwali A, Kumar A, Khan MZ, Srivastava P, Batra S (2023) Appraising early reliability of a software component using fuzzy inference. Electronics 12(5):1137

    Article  Google Scholar 

  27. Tyagi K, Sharma A (2014) An adaptive neuro fuzzy model for estimating the reliability of component-based software systems. Appl Comput Inform 10(1–2):38–51

    Article  Google Scholar 

  28. ChauPattnaik S, Ray M, Nayak M (2023) Fuzzy set-based reliability estimation. Int J Softw Innov (IJSI) 11(1):1–14. https://doi.org/10.4018/IJSI.315733

    Article  Google Scholar 

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

    Google Scholar 

  30. Jaiswal GP, Giri RN (2015) Software reliability estimation of component based software system using fuzzy logic. Int J Comput Sci Inf Secur 13(7):66

    Google Scholar 

  31. Garg R, Raheja S, Garg RK (2021) Decision support system for optimal selection of software reliability growth models using a hybrid approach. IEEE Trans Reliab 71(1):149–161

    Article  Google Scholar 

  32. Wu CY, Huang CY (2021) A study of incorporation of deep learning into software reliability modeling and assessment. IEEE Trans Reliab 70(4):1621–1640

    Article  Google Scholar 

  33. Iqbal J, Firdous T, Shrivastava AK et al (2022) Modelling and predicting software vulnerabilities using a sigmoid function. Int J Inf Tecnol 14:649–655. https://doi.org/10.1007/s41870-021-00844-2

    Article  Google Scholar 

  34. Wang Y, Liu H, Yuan H, Zhang Z (2023) Comprehensive evaluation of software system reliability based on component-based generalized GO models. PeerJ Comput Sci 9:e1247

    Article  Google Scholar 

  35. Jagtap M, Katragadda P, Satelkar P (2022) Software reliability: development of software defect prediction models using advanced techniques. In: 2022 Annual reliability and maintainability symposium (RAMS). IEEE, pp 1–7

  36. Babu S, Singh R (2022) A model for prediction of understandability and modifiability of object-oriented software. In: Congress on intelligent systems. Springer Nature Singapore, Singapore, pp 275–286

  37. Chau Pattnaik S, Ray M, Nayak MM (2022) Reliability estimation using fuzzy failure rate. In: Intelligent and cloud computing: proceedings of ICICC 2021. Springer Nature Singapore, Singapore, pp 199–205

  38. Pattnaik S, Laha SR, Pattanayak BK, Mohanty R, Alnabhan M, Mohanty MN (2022) Software reliability reckoning by applying neural network algorithm. J Inf Optim Sci 43(5):1061–1071

    Google Scholar 

  39. Lakshminarayana P, Kumar TS (2022) Kinetic gas molecular optimized (KGMO) artificial neural network (ANN) based software reliability prediction for banking applications. In: Information systems and management science: conference proceedings of 3rd international conference on information systems and management science (ISMS) 2020. Springer International Publishing, Berlin, pp 160–170

  40. Kaliraj S, Bharathi A (2019) Path testing based reliability analysis framework of component based software system. Measurement 144:20–32

    Article  Google Scholar 

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

    Article  Google Scholar 

  42. Saaty TL, Kearns KP (2014) Analytical planning: the organization of system, vol 7. Elsevier, Amsterdam

    Google Scholar 

  43. Dwi Putra MS, Andryana S, Gunaryati A (2018) Fuzzy analytical hierarchy process method to determine the quality of gemstones. Adv Fuzzy Syst. https://doi.org/10.1155/2018/9094380

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Harcourt Butler Technical University, HBTU East Campus, Nawabganj, Kanpur, Uttar Pradesh, 208 002, India

    Sumit Babu & Raghuraj Singh

Authors
  1. Sumit Babu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raghuraj Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sumit Babu.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interests regarding the publications of this manuscript.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Babu, S., Singh, R. Enhancing software reliability prediction using fuzzy AHP-based mathematical model and ANN integration. Int. j. inf. tecnol. (2024). https://doi.org/10.1007/s41870-024-01914-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41870-024-01914-x

Keywords

Search

Navigation