In this chapter, we consider one shot systems, long-term repairable storage systems, which are in storage and can be used at an unknown time point once, whose failure only can be detected by inspection. Due to the system failure can incur a loss of life and economic damage, inspections and maintenance should be carried out to maintain a high level of storage reliability. However, since these inspections are usually costly, inspection times should be optimized to achieve a balance between undetected failure costs and inspection costs. Therefore, it is necessary to suggest appropriate optimization criteria and inspection policies according to the system structure and function characteristics of one shot system. In recent years, performance evaluation and inspection optimization problems have attracted many researchers’ attention. This study summarizes the existing literatures related to the reliability and inspection optimization models of one shot systems. Firstly, this paper reviews the recent advances in storage reliability modeling for evaluating the performance of one shot systems. On this basis, the inspection optimization models of one shot systems with various structures are established and the key ideas of optimization methods in each optimization inspection problem are summarized. In summary, this contribution provides a survey on optimization methods for the inspection policy of one shot systems, with emphasis on the optimization methods under the different scales of systems, such as single-unit and multi-unit, as the target system. In addition, a qualitative comparison is performed to provide some general guidelines for the range of applicability of the approaches discussed in this contribution.

Abstract Testing has been an inevitable activity in the software development life cycle. In the current scenario, software development has become evolutionary in nature where software is released in cycles, each cycle fulfilling the requirements of the customer on a priority basis. This evolutionary development of software also demands high maintenance in the form of retesting. This re-testing is called regression testing and the literature reveals that it is a proven N-P hard problem that attracts the application of approximation algorithms such as meta-heuristics. In this paper, Mayfly Optimization Algorithm has been adopted to solve the regression test case selection problem to minimize the maintenance cost. The aim is to optimize the number of test cases to re-execute to reduce the execution time and cost. The performance of the adopted approach is further compared with state-of-the-art approaches with the help of statistical tests. The shows that the adopted approach performs well in comparison to state of art approaches.KeywordsSoftware testingMayfly optimization algorithmSIRMetaHeuristicsRegression test case selection