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Allocation of efforts to a software development project during the testing phase is a multifaceted task for software managers. The challenges become stiffer when the nature of the development process is considered in the dynamic environment. Many software reliability growth models have been proposed in last decade to minimise the total testing-effort expenditures, but mostly under static assumption. The main purpose of this article is to investigate an optimal resource allocation plan to minimise the cost of software during the testing and operational phase under dynamic condition. An elaborate optimisation policy based on the optimal control theory is proposed and numerical examples are illustrated. This article also studies the optimal resource allocation problems for various conditions by examining the behaviour of the model parameters and also suggests policy for the optimal release time of the software. The experimental results greatly help us to identify the contribution of each selected parameter and its weight. [ABSTRACT FROM PUBLISHER]

The life of software is very short in the environment of perfect competition market. Therefore the software developers have to come up with multiple releases in order to survive in the market. But, enhancing the product and coming up with multiple releases puts a constant pressure on even the best organized engineering organizations. The reason being, up-grading a software application is a complex process. Up-grades of software introduce the risk that the new release will contain a bug, causing the program to fail. Therefore, to capture the effect of faults generated in the software with multiple release, we have developed a multi release software reliability model in this paper. The model uniquely take into account the faults of the current release and the remaining faults of just previous release. The multi release software reliability growth model treats the faults removal rate as a function of testing resources consumed. In addition, most of the debugging process in real life is not perfect. Due to complexity and incomplete understanding of the software, the testing team may not be able to remove/correct the fault perfectly on observation/ detection of a failure and the original fault may remain resulting in the phenomenon known as imperfect debugging, or get replaced by another fault causing error generation. The effects of both type of imperfect debugging during testing phase are incorporated in our proposed multi-release model. The model developed is validated on real data set with software which has been released in the market with new features four times [ABSTRACT FROM AUTHOR]

It is widely recognized that the debugging processes are usually imperfect. Software faults are not completely removed because of the difficulty in locating them or because new faults might be introduced. Hence, it is of great importance to investigate the effect of the imperfect debugging on software development cost, which, in turn, might affect the optimal software release time or operational budget. In this paper, a commonly used cost model is extended to the case of imperfect debugging. Based on this, the effect of imperfect debugging is studied. As the probability of perfect debugging, termed testing level here, is expensive to be increased, but manageable to a certain extent with additional resources, a model incorporating this situation is presented. Moreover, the problem of determining the optimal testing level is considered. This is useful when the decisions regarding the test team composition, testing strategy, etc., are to be made for more effective testing. [ABSTRACT FROM AUTHOR]