Quantitative Software Reliability Assessment Research Articles

Quantitative software reliability assessment methodology based on Bayesian belief networks and statistical testing for safety-critical software

This study proposes an overall methodology that provides in-depth evidence on software reliability. It is used to quantitatively assess the reliability of nuclear power plant (NPP) safety-critical software for the incorporation of digital instrumentation and control systems into NPP probabilistic risk assessment (PRA). The methodology consists of three parts: (1) the relationships among the software development life cycle (SDLC) phases, the number of remaining faults in the software, and the probability of failure on demand (PFD) are modeled by a Bayesian belief network, which can provide a prior distribution of the software PFD; (2) a reliability model for the PFD is used to calculate the number of no-failure tests needed to meet the expected reliability target according to the prior distribution; (3) the software statistical testing (SST) based on PRA is used as a reliability validation test method to assess reliability, when the required no-failure tests are completed, it is considered that the software meets the expected reliability target. The main contribution of this methodology is that it fully considers the factors that affect software reliability, i.e. the quality of development activities and verification & validation (V&V) activities of the SDLC processes, software operational profile and software operational environment when assessing software reliability. This is done such that the methodology overcomes the subjectivity of separate quality assessments of the SDLC processes. It also solves the problem that occurs because an individual SST using an uninformative prior distribution is conservative.

SOFTWARE RELIABILITY ASSESSMENT USING EXPONENTIAL-TYPE CHANGE-POINT HAZARD RATE MODELS

We discuss software hazard rate modeling with a change of testing-environment and a software reliability assessment method based on the proposed software hazard rate models. A software hazard rate model is known as one of the important and useful mathematical models for describing the software failure-occurrence phenomenon and conducting quantitative software reliability assessment. Taking into consideration of the effect of the change in software reliability growth modeling is expected to conduct more accurate software reliability assessment because it is said that such approach enables us to conduct more plausible software reliability assessment reflecting the actual testing-environment. Especially in this paper, we develop exponential-type software hazard rate models with effect of change-point and a software reliability assessment method based on our models. Finally, we show numerical examples for our models and results of model comparisons with existing software hazard rate models by using actual data.

OPTIMAL SOFTWARE SHIPPING TIME ESTIMATION BASED ON A CHANGE-POINT HAZARD RATE MODEL

Software failure-occurrence or the fault-detection phenomenon is changed notably in an actual testing phase or an operational phase due to the changes of factors influencing the software reliability growth process. Taking into consideration of the effect of the change in software reliability growth modeling is expected to conduct more accurate software reliability assessment because it is said that such approach enables us to conduct more plausible software reliability assessment reflecting an actual testing-environment. We develop a framework for developing software hazard rate models with effect of change-point, and discuss change-point detection methods for applying our model to quantitative software reliability assessment. Additionally, we discuss an optimal software release problem for estimating optimal shipping time with the effect of the change as one of the application problems of our model in software project management. Finally, we show numerical examples for software reliability assessment and our optimal software release policy based on our change-point model by using actual data.

ON THE DEVELOPMENT OF UNIFIED SCHEME FOR DISCRETE SOFTWARE RELIABILITY GROWTH MODELING

The importance of Software Reliability Growth Models to control the testing process and for quantitative assessment of software reliability is a well established fact. However, difficulties created by their underlying assumptions, their relevance and validity to real testing environment have made the selection of appropriate model an uphill task. Recently, new dimensions have been added to software reliability engineering with the development of unified modeling schemes. These schemes have proved seminal in the development of the general theory, partially because of their simplicity and mathematical tractability. In this paper, we propose a unified scheme for discrete software reliability growth modeling using the fault detection/correction rate function. Standard probability distributions have been used to model the fault correction and detection times. Initially, we have formulated the unified scheme when the fault correction is immediate to the failure observation and later we extend it to the cases where removal is a two stage process namely failure observation followed by fault detection/correction. The use of fault detection/correction rate or else known as hazard rate to represent stage wise removal of fault during testing highlights the importance of the proposed framework. Convolution of probability distribution functions has been used to represent Stage-wise removal of fault i.e. failure observation, fault detection/fault correction. Few Discrete models have been derived using the proposed methodology. Parameter estimation on two real software failure datasets has been worked out. The results obtained are fairly accurate and quite encouraging.

Quantitative Reliability Assessment for Safety Critical System Software

At recent times, an essential issue in the replacement of the old analogue I&C to computer-based digital systems in nuclear power plants becomes the quantitative software reliability assessment. Software reliability models have been successfully applied to many industrial applications, but have the unfortunate drawback of requiring data from which one can formulate a model. Software that is developed for safety critical applications is frequently unable to produce such data for at least two reasons. First, the software is frequently one-of-a-kind, and second, it rarely fails. Safety critical software is normally expected to pass every unit test producing precious little failure data. The basic premise of the rare events approach is that well-tested software does not fail under normal routine and input signals, which means that failures must be triggered by unusual input data and computer states. The failure data found under the reasonable testing cases and testing time for these conditions should be considered for the quantitative reliability assessment. We presented the quantitative reliability assessment methodology of safety critical software for rare failure cases in this paper.