ABSTRACT Objective Long‐running software systems suffer from aging, characterized by rising performance degradation, resource depletion, and elevated failure rates. Conventional rejuvenation techniques, which often depend on predetermined restart intervals, model the system as simply healthy, deteriorated, or failed. However, practical evidence from a variety of fields indicates that wear and defect buildup frequently accelerate nonlinearly, necessitating more adaptable mathematical explanations. Method Compared to simpler two‐parameter or linear models, an extended three‐parameter power‐law model which consists of a scaling factor, exponent, and offset is employed here to more accurately depict this behavior. The proposed framework enables adaptive rejuvenation policies triggered by observed conditions rather than rigid schedules by continuously monitoring a degradation metric tuned to this model. Renewal theory with rewards can be used to enhance rejuvenation timing and analytically evaluate steady‐state unavailability. Results By accurately simulating real‐world dynamics, numerical results show that these degradation‐aware, threshold‐based policies outperform fixed interval approaches by accurately modelling real‐world dynamics, particularly in scenarios with increasing aging. Conclusions Together, the extended degradation model and the proposed rejuvenation policies provide a unified analytical framework that improves system availability and reduces long‐run operational costs. This study shows that alert‐based strategies consistently outperform risk‐based policies because they allow for early, data‐driven maintenance across various software aging conditions.

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.

Reliability, a measure of software, deals in total number of faults count up to a certain period of time. The present study aims at estimating the total number of software faults during the early phase of software life cycle. Such estimation helps in producing more reliable software as there may be a scope to take necessary corrective actions for improving the reliability within optimum time and cost by the software developers. The proposed interval type-2 fuzzy logic-based model considers reliability-relevant software metric and earlier project data as model inputs. Type-2 fuzzy sets have been used to reduce uncertainties in the vague linguistic values of the software metrics. A rule formation algorithm has been developed to overcome inconsistency in the consequent parts of large number of rules. Twenty-six software project data help to validate the model, and a comparison has been provided to analyse the proposed model’s performance.

Abstract This paper presents a unified approach to model the reliability growth of software with imperfect debugging and coverage factor. Existing testing coverage‐based software reliability growth models considered that faults present at a particular fault location are detected with certainty during the testing process. Practically, it is very difficult to detect all software faults. To overcome this limitation, a revised software reliability growth model has been developed with the assumption that detection of the faults at a particular fault location is not definite. Furthermore, a new method to model the imperfect debugging phenomenon has been incorporated in the proposed study. A revised model ranking method has been developed to improve the accuracy of model ranking, which is mainly extension of existing normalized criteria distance method. Change point analysis has been done with the effect of different environmental factors on the models' parameters. Numerical examples are given to demonstrate the effectiveness of the proposed model.