Li Qiuying, Pham Hoang
School of Reliability & Systems Engineering, Beihang University, Beijing, China.
Science & Technology on Reliability & Environmental Engineering Laboratory, Beijing, China.
PLoS One. 2017 Jul 27;12(7):e0181524. doi: 10.1371/journal.pone.0181524. eCollection 2017.
In this paper, we propose a software reliability model that considers not only error generation but also fault removal efficiency combined with testing coverage information based on a nonhomogeneous Poisson process (NHPP). During the past four decades, many software reliability growth models (SRGMs) based on NHPP have been proposed to estimate the software reliability measures, most of which have the same following agreements: 1) it is a common phenomenon that during the testing phase, the fault detection rate always changes; 2) as a result of imperfect debugging, fault removal has been related to a fault re-introduction rate. But there are few SRGMs in the literature that differentiate between fault detection and fault removal, i.e. they seldom consider the imperfect fault removal efficiency. But in practical software developing process, fault removal efficiency cannot always be perfect, i.e. the failures detected might not be removed completely and the original faults might still exist and new faults might be introduced meanwhile, which is referred to as imperfect debugging phenomenon. In this study, a model aiming to incorporate fault introduction rate, fault removal efficiency and testing coverage into software reliability evaluation is developed, using testing coverage to express the fault detection rate and using fault removal efficiency to consider the fault repair. We compare the performance of the proposed model with several existing NHPP SRGMs using three sets of real failure data based on five criteria. The results exhibit that the model can give a better fitting and predictive performance.
在本文中，我们提出了一种软件可靠性模型，该模型基于非齐次泊松过程（NHPP），不仅考虑了错误生成，还结合测试覆盖信息考虑了故障排除效率。在过去的四十年里，已经提出了许多基于NHPP的软件可靠性增长模型（SRGM）来估计软件可靠性度量，其中大多数都有以下相同的共识：1）在测试阶段，故障检测率总是变化，这是一种常见现象；2）由于调试不完善，故障排除与故障重新引入率有关。但是文献中很少有SRGM能够区分故障检测和故障排除，即它们很少考虑不完善的故障排除效率。但在实际软件开发过程中，故障排除效率不可能总是完美的，即检测到的故障可能无法完全排除，原始故障可能仍然存在，同时可能会引入新的故障，这被称为不完善调试现象。在本研究中，开发了一个旨在将故障引入率、故障排除效率和测试覆盖纳入软件可靠性评估的模型，使用测试覆盖来表示故障检测率，并使用故障排除效率来考虑故障修复。我们基于五个标准，使用三组实际故障数据，将所提出模型的性能与几个现有的基于NHPP的SRGM进行比较。结果表明，该模型能够给出更好的拟合和预测性能。
