A multi-step mathematical model-based predictive strategy for software release timing during testing stage

Mathematically precise modeling is important to be established to accurately examine the quantitative relationship between software testing and software reliability. Software testing process is complex since it is concerned with various factors such as test case execution, defect debugging, tester expertise, test case selection, and so forth. For this reason, it is required to be meticulous in formulating the software testing process in a manner which is mathematically concise. The software release life cycle or sequential release timeline, referring to the process related to the development, testing and distribution of a software product comprises several critical stages, and the length of this particular life cycle reveals variations depending on different factors like the type of product, the intended use of it, industry security, general standards and compliance. One consideration software engineers have is related to the release date of the software so that future commitments about the software’s release time can be formulated beforehand. In view of these aspects, a multi-step strategy for predicting software release dates is proposed in the current study along with the following stages: firstly, the proposed technique selects the utmost reliability growth model that very well fits the observed test data halfway through the testing period, and then employs it to forecast the probable date of release. This technique entails approximating the unknown parameters of suitable Software Reliability Growth Models (SRGMs). Finally, the chosen SRGM is used to forecast the release date of the software under test by fitting it to available fault data. The proposed method is straightforward and applied to test on a total of ten actual datasets collected from the literature. The results of the proposed technique reveal that in the majority of the situations, nearly exact approximation of date of release can be made halfway through the testing period. Moreover, the proposed method’s performance is also compared to that of a number of previous strategies present in the literature. The outcomes obtained by our study demonstrate that the proposed strategy may be used to forecast the release date of software in practical situations.