Software reliability estimation is one of most active area of research in software testing. Since recording of time between failures has often been too difficult to collect, software testing data now have commonly been recorded as test-case-wise in a discrete set up. Although there have many models that were developed to estimate software reliability, they were too restrictive in nature. We have developed a model using the size-biased concept which not only estimates the software reliability, but also estimates the total number of bugs present in the software. The model is highly flexible as it could provide the reliability estimates at the end of software testing and also at any future phase of software testing that could have been conducted in order to improve the software reliability. This flexibility is instrumental to find the stopping phase such that the software reliability achieves a desired optimum level (e.g., 95\%). In addition, we also provide a model extension which could be applied on grouped bugs in different regions of a software. We assessed the performance of our model via simulation study and found that each of the key parameters could be estimated with satisfactory level of accuracy. We also applied our model to two different software testing data sets. In the first model application we found that the conducted software testing was inefficient and a considerable amount of further testing is required to achieve a optimum reliability level. On the other hand, the application to second empirical data set has shown that the respective software was highly reliable with software reliability estimate 99.8\%. We anticipate that our novel modelling approach to estimate software reliability could be very useful for the users and can potentially be a key tool in the field of software reliability estimation.
翻译:软件可靠性估算是软件测试中最活跃的研究领域之一。由于记录故障之间的时间间隔往往太难收集,因此,软件测试数据现在通常在一个离散的装置中作为测试个案记录。虽然有许多模型是用来估计软件可靠性的,但性质限制过强。我们开发了一个模型,使用尺寸偏差的概念不仅估计软件可靠性,而且还估计软件中存在的错误的总数。模型非常灵活,因为它可以在软件测试结束时和今后任何软件测试阶段提供可靠性估算,以便提高软件可靠性。这种灵活性有助于找到使软件可靠性达到理想最佳水平的停止阶段(例如,95 ⁇ )。此外,我们还开发了一个模型扩展,不仅可以适用于软件不同区域的分组错误。我们通过模拟研究评估了我们的模型的性能,发现每个关键参数都可以以令人满意的准确程度来估算。我们还将模型应用于两个不同的软件测试用户的可靠性,提高软件可靠性。8 在第一个阶段,软件可靠性达到理想水平的阶段,我们进行了一个测试,另一个测试是高水平的实地,我们进行了一个测试,这个测试的实地试验,一个是高水平的实地测试。我们发现,一个测试了相当低的软件的实地测试,一个测试,一个是高水平的实地测试。