Abstract
During software testing phase, the software manager has to accurately allocate testing resources to arrive at acceptable software quality and reduce the cost of testing. In the last twenty years, there have been many papers discussing the problem of testing-effort prediction, and also proposed many TEFs such as Weibull TEF, Rayleigh TEF and logistic TEF, etc. In practice, the testing effort consumption rate is strongly affected by unexpected events during the testing phase. For instance, the participation of new tester or the consumer advances the deadline, etc. However, most TEFs consider testing effort consumption rate as a constant value. Due to this reason, we proposed Parr curve TEF which incorporates the concept of multiple change points in the thesis, and expected the application of change points can make the Parr curve TEF more flexible to adapt to any situation of testing resource allocation. On the other hand, the testing resource allocation deeply influences the growth trend of software reliability. Thus a powerful TEF can provide sufficient testing-effort consumption information to enhance the capability of SRGMs. In the experiment, we compared to Weibull TEF with change points and examine the performance of SRGM with 1-CP Parr curve TEF. Besides, the 2-CP Parr curve is also implemented and induced in the experiment. In order to guarantee the correctness of experiment result, both parameter estimated methods are used, LSE & MLE. Finally, we discuss the policy of optimal release time based on the proposed SRGM. Dependent on constraints, reliability and testing cost, the optimal release time was induced.