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Software dependability analysis under neutrosophic environment using optimized Elman recurrent neural network-based classification algorithm and Mahalanobis distance-based ranking algorithm

Author

Listed:
  • Subhashis Chatterjee

    (Indian Institute of Technology (ISM) Dhanbad)

  • Deepjyoti Saha

    (Indian Institute of Technology (ISM) Dhanbad)

Abstract

Dependability of software systems is one of the challenging issues for software developers. Main software dependability issues include reliability, security, performability, availability, maintainability, and aging. Software becomes non-dependable due to the overconfidence of developers, lack of knowledge about dependability issues, or ignorance of dependability attributes during software development. Classification and ranking of these non-dependable software modules based on above-mentioned dependability attribute values in early phase are the main aspects of this article. Hence, computation of dependability attribute values becomes a primitive concern here. The values of software dependability attributes depend on various software metrics like: requirement stability, cyclomatic complexity, essential complexity, lines of code, and so on. Neutrosophic inference system (NIS) has been used here to compute the values of dependability attributes accurately, reducing incompleteness, indeterminacy, and impreciseness from metric values by incorporating expert knowledge. An Elman Recurrent Neural Network (ERNN)-based algorithm has been proposed here based on predicted dependability attribute values to classify dependable and non-dependable software modules. Backpropagation algorithm and Genetic Algorithm are used during training of ERNN. Mahalanobis distance (MD) is used to rank software modules based on dependability attributes at early phase of development. This entire process of dependability analysis will help to optimize resource utilization, development cost, and meet the target release time. Different comparison criteria are used to compare the effectiveness of the proposed model with some existing models based on four datasets. Performance analysis demonstrates effectiveness and usefulness for identifying and ranking the non-dependable software modules during early phase of development.

Suggested Citation

  • Subhashis Chatterjee & Deepjyoti Saha, 2024. "Software dependability analysis under neutrosophic environment using optimized Elman recurrent neural network-based classification algorithm and Mahalanobis distance-based ranking algorithm," Annals of Operations Research, Springer, vol. 340(1), pages 83-115, September.
    • Handle: RePEc:spr:annopr:v:340:y:2024:i:1:d:10.1007_s10479-024-05888-8
    DOI: 10.1007/s10479-024-05888-8
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    References listed on IDEAS

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    1. Subhashis Chatterjee & Ankur Shukla, 2017. "An Ideal Software Release Policy for an Improved Software Reliability Growth Model Incorporating Imperfect Debugging with Fault Removal Efficiency and Change Point," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(03), pages 1-21, June.
    2. Subhashis Chatterjee & Bappa Maji, 2020. "A Fuzzy Logic-Based Model for Classifying Software Modules in Order to Achieve Dependable Software," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 11(4), pages 45-57, October.
    3. Shivam Gupta & Sachin Modgil & Samadrita Bhattacharyya & Indranil Bose, 2022. "Artificial intelligence for decision support systems in the field of operations research: review and future scope of research," Annals of Operations Research, Springer, vol. 308(1), pages 215-274, January.
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