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A generalized multiple environmental factors software reliability model with stochastic fault detection process

  • S.I. : Reliability Modeling with Applications Based on Big Data
  • Published:
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Abstract

Software systems have been widely applied in numerous safety–critical domains; however, large-scale software development is still considered as a complicated and expensive activity. As the latest trends in software industry accelerate the complexity and dependency of software development, such complicated and human-centered process needs to be addressed well. Meanwhile, recent survey investigations (Zhu et al. in J Syst Softw 109:150–160, 2015; Zhu and Pham in J Syst Softw 132:72–84, 2017) revealed that environmental factors, defined from software development, have significant impacts on software reliability. Considering such significant impacts, we first propose a generalized multiple-environmental-factors software reliability growth model with multiple environmental factors and the associated randomness under the martingale framework. The randomness is reflected on the process of detecting software fault. Indeed, this is a stochastic fault detection process. As an illustration, a specific multiple-environmental-factors software reliability growth model incorporating two specific environmental factors, percentage of reused modules and frequency of program specification change, is further developed. Lastly, we employ two real-world data sets to demonstrate the prediction performance of the proposed generalized multiple-environmental-factors software reliability growth model.

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Authors and Affiliations

  1. Department of Textile Engineering, Chemistry and Science, North Carolina State University, Raleigh, NC, 27606, USA

    Mengmeng Zhu

  2. Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ, 08854, USA

    Hoang Pham

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  1. Mengmeng Zhu
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  2. Hoang Pham
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Correspondence to Mengmeng Zhu.

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Zhu, M., Pham, H. A generalized multiple environmental factors software reliability model with stochastic fault detection process. Ann Oper Res 311, 525–546 (2022). https://doi.org/10.1007/s10479-020-03732-3

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