Stochastic debugging based reliability growth models for Open Source Software project

Open Source Software (OSS) is one of the most trusted technologies for implementing industry 4.0 solutions. The study aims to assist a community of OSS developers in quantifying the product’s reliability. This research proposes reliability growth models for OSS by incorporating dynamicity in the debugging process. For this, stochastic differential equation-based analytical models are developed to represent the instantaneous rate of error generation. The fault introduction rate is modeled using exponential and Erlang distribution functions. The empirical applications of the proposed methodology are verified using the real-life failure data of the Open Source Software projects, GNU Network Object Model Environment, and Eclipse. A soft computing technique, Genetic Algorithm, is applied to estimate model parameters. Cross-validation is also performed to examine the forecasting efficacy of the model. The predictive power of the developed models is compared with various benchmark studies. The data analysis is conducted using the R statistical computing software. The results demonstrate the proposed models’ efficacy in parameter estimation and predictive performance. In addition, the optimal release time policy based on the proposed mathematical models is presented by formulating the optimization model that intends to minimize the total cost of software development under reliability constraints. The numerical illustration and sensitivity analysis exhibit the proposed problem's practical significance. The findings of the numerical analysis exemplify the proposed study's capability of decision-making under uncertainty.