We discuss SIL (safety integrity level) assessment for software of E/E/PE (Electrical/Electronic/Programmable Electronic) safety-related systems. The SIL is known as a graded measure representing the safety integrity of E/E/PE safety-related systems, and has been defined in IEC 61508, which is an international standard issued totally in 2000 (the second version has been issued in 2010) for E/E/PE safety-related systems. We try to develop mathematical models for estimating the approximations of the PFD (time-averaged probability of failure on demand) and PFH (time-averaged frequency of dangerous failure per hour), which are the target failure measures of SIL, for the E/E/PE safety-related software by applying the notion of software reliability modeling and assessment methods.

The iterative and incremental development is becoming a major development process model in industry, and allows us for a good deal of parallelism between development and testing. In this paper we develop a quantitative software reliability assessment method in incremental development processes, based on the familiar non-homogeneous Poisson processes. More specifically, we utilize the software metrics observed in each incremental development and testing, and estimate the associated software reliability measures. In a numerical example with a real incremental developmental project data, it is shown that the estimate of software reliability with a specific model can take a realistic value, and that the reliability growth phenomenon can be observed even in the incremental development scheme.

In the functional safety standard (IEC 61508), development methods and quantitative analytical methods are defined for establishment of safety-related systems. However, only development methods are recommended to establish the software of safety-related systems. That is, the safety integrity level for software is determined only by the number of the development methods applied to practical safety-related system development. This is not reasonable to evaluate the safety integrity level, because various risk factors should be taken up. In this paper, we propose how to calculate the safety integrity level for software. Especially, we propose the calculation method based on software reliability growth models that have been utilized for many years in the large-scale system development.

Software system developed for a specific user under contract undergoes a period of testing by the user before acceptance. This is known as user acceptance testing and is useful to debug the software in the user's operational circumstance. In this paper we first present a simple non-homogeneous Poisson process (NHPP)-based software reliability model to assess the quantitative software reliability under the user acceptance test, where the idea of an accelerated life testing model is introduced to represent the user's operational phase and to investigate the impact of user's acceptance test. This idea is applied to the reliability assessment of web applications in a different testing environment, where two stress tests with normal and higher workload conditions are executed in parallel. Through numerical examples with real software fault data observed in actual user acceptance and stress tests, we show the applicability of the software accelerated life testing model to two different software testing schemes.

Many non-homogeneous Poisson process software reliability growth models are characterized by their mean value functions. Mean value functions of coverage-based models are usually obtained as composite functions of the coverage growth function and the function relating the number of detected faults to the coverage. This paper performs empirical evaluation of the relationships between the number of detected faults and the coverage embedded in the coverage- based software reliability growth models. It is also illustrated that integration of well-performing coverage growth functions and relationships between the number of detected faults and the coverage produces well-performing mean value functions.