Abstract
Software reliability growth models (SRGMs) are very useful tool to calculate the probability of software failure. A lot of mathematical models have been formulated to predict software reliability growth behavior. In the literature, most of the SRGMs is developed under consideration that the reliability growth of the software depends on testing time, and when a failure is occured, fault is immediately removed. In this article, an approach has been used which considers the testing effort and delay in removing the faults. Where testing time and testing effort are taken together. The combined effect of testing time and testing effort is considered using Cobb Douglas production function. Proposed model works in imperfect debugging environment where new faults may introduce in the fault detection and correction process. Time used by the testing team to remove any fault is also considered with some delay. The parameters are estimated using nonlinear regression. The developed model is validated on the real data sets. The performance of proposed study is compared based on mean square error (MSE) with existing models in the literature.
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Pachauri, B., Kumar, A., Raja, S. (2019). Imperfect Software Reliability Growth Model Using Delay in Fault Correction. In: Deep, K., Jain, M., Salhi, S. (eds) Performance Prediction and Analytics of Fuzzy, Reliability and Queuing Models . Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-13-0857-4_8
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