The widespread integration of big data technologies into daily life has heightened the demand for reliable software systems, yet research on open source software reliability lags behind that of closed source software, whose theoretical framework is already mature. Conventional software reliability growth models fail to address the unique attributes of open source big data systems, creating an urgent need for a specialized reliability assessment tool. To fill this gap, this paper proposes an imperfect debugging reliability model that considers big data technology characteristics and captures the complex dynamics of fault removal efficiency using a Weibull distribution. Rigorous experimental validation demonstrates the model’s exceptional fitting accuracy and predictive power. By enabling effective and precise reliability evaluation of open source big data system software, this work not only advances the theoretical frontier of software reliability but also provides actionable insights for optimizing software development and testing processes.
Wang et al. (Mon,) studied this question.
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