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Article type: Research Article
Authors: Anand, Sameera | Jaiswal, Ajayb | Verma, Vibhac; * | Singh, Atuld
Affiliations: [a] Department of Operational Research, University of Delhi, Delhi, India | [b] Campus of Open Learning, University of Delhi, Delhi, India | [c] Jaipuria School of Business, Indirapuram, Ghaziabad, India | [d] Department of Statistics, Ramjas College, University of Delhi, Delhi, India
Correspondence: [*] Corresponding author: Vibha Verma, Jaipuria School of Business, Indirapuram, Ghaziabad 201014, India. E-mail: vibhaverma.jsb@jaipuria.edu.in.
Abstract: The operational environment of software differs from the debugging environment. Therefore, the study explores the impact of irregular consumption by diverse users on the development cost of software, reliability of software, and release decisions. To accomplish this, a release model has been formulated considering logistic testing coverage-based reliability growth model built as a stochastic process including the error generation. The stochastic nature has been captured by considering the noise factor due to irregular fluctuations occurring during testing while usage uncertainty has been captured by introducing a constant parameter in the cost function of the operational phase. It is assumed that the testing phase cost is affected by the noise and the operation phase cost is affected by the severity of noise which is the result of uncertain usage by users. The model was evaluated against a real failure dataset. The release model creates a trade-off between software development cost, release timing, and reliability aspirations. This study contributes to software reliability literature and provides insights to practitioners to make software release decisions. The sensitivity analysis results give information about various aspects during the operational phase that affect the overall development cost.
Keywords: Testing coverage, noise-based SRGM, error generation, operational phase, release planning, environmental factor, sensitivity analysis
DOI: 10.3233/IDT-240307
Journal: Intelligent Decision Technologies, vol. 18, no. 3, pp. 2339-2352, 2024
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