Multi‐upgradation software reliability growth model with dependency of faults under change point and imperfect debugging. Issue 6 (22nd March 2021) - Chatterjee, Subhashis; Saha, Deepjyoti; Sharma, Akhilesh
Abstract: With the improvement of innovation, software developers consistently build up a new version of software by adding new features in the previously existing version of the software. In resent day's competitive market, the reliability and release time of multi‐upgradation software are very important issues to both: customers and developers. Many researchers have proposed various software reliability growth models (SRGMs) to improve the precision of assessing the reliability of programming. For reliability estimation, a detailed study is necessary to know the characteristics of different types of faults. It has been noticed that some faults present in software remove independently at the same time some faults remove some other faults. During upgradation, new faults generate due to many reasons. Though dependency of faults is an important issue, there is no such SRGM exist for multi‐release problem. Hence, an SRGM has been proposed for multi‐release problems here with the dependency of faults under the imperfect debugging phenomenon. The effect of change point has been incorporated in SRGM due to changes in testing strategy, changes in testing environments, and so forth, during up‐gradation. Based on datasets, it can be said the performance of the proposed model is better than some other existing models. Also, it is very important for software companies to know the optimal time of release a new version of software. As testing charges change with time, it cannot be constant throughout testing phase. Hence, a new random cost model has also been proposed for release time analysis in case of a multi‐release problem. Abstract : A nonhomogeneous Poisson process‐based software reliability growth model (SGRM) has been developed incorporating change point, imperfect debugging, and fault dependency for multi‐release software. Parameters of the proposed SRGM are estimated using nonlinear least square estimation technique. Performance analysis of proposed SRGM has been done, and it is validated using different comparison criteria. Optimal release time analysis has been performed for multi‐release software.