In the past several decades, significant attention has been devoted to the quality assessment of safety-critical (SC) and control systems from many perspectives such as its reliability, safety, and performance. Researchers are continuing to put their efforts to ensure these dependability attributes. This study summarises the state of the art in the field of the reliability of such systems. A detailed literature survey is conducted to investigate the various techniques/models to ensure the reliability of the computer-based systems. The limitations of these models are also analysed with respect to their applicability in SC systems, for which a case study of nuclear power plant system has been taken. The direction for future research is suggested, based on the case study, to extend the further scope of research.

The size of a software project is a key measure of predicting software effort at the requirements and analysis phase. Use case points (UCP) is among software size metrics that achieved good reputation because of the increasing popularity of use case driven development methodologies in software industry. Nevertheless, there is no consistent method that can effectively translate the UCP into its corresponding effort. Previous estimation models were built using a very limited number of projects, and they were not well examined. The soft computing techniques were rarely applied for such problem and their performances have not been well investigated using a systematic procedure. This study looks into the accuracy and stability of some soft computing methods for the problem of effort estimation based on UCP. Four neural network methods, adaptive neuro fuzzy inference system and support vector regression have been used in this comparative study. The results suggest that most used soft computing techniques can work well with good accuracy for such problem. Among them, the general regression neural network is the superior one with stable ranking across different accuracy measures. Also, it has been found that using adjustment variables with basic UCP variables, solely or together, have positive impact on the accuracy and stability.

Regression testing ensures that the functionality of previous code is not affected by the updates in the modified code. The focus of regression test case generation is to generate test cases for changed functionality. The authors’ research advocates the use of mobile agent-based technology for regression test case generation using syntax and semantics analysis based on model and formal specifications. In this study, the authors presented a tool for adopting multi-agent systems for regression test case generation on distributed environment using standard unified modelling language (UML) models and formal specifications. Different agents are designed to perform model comparison, behaviour comparison, specifications comparison, impact analysis, and regression test case generation. Agents designed in JADE framework perform these tasks by using XML files of UML class diagram, sequence diagram and formal specifications based on Object-Z and OCL. To the best of the authors’ knowledge, no research has reported regression test case generation using mobile agent-based technology along with model and formal specifications. It is found that the use of mobile agents will significantly reduce time and effort for regression test case generation in distributed systems.

Software ageing problems are mainly caused by resource consumption exhaustion, so many researchers focused on predicting software resource consumption. However, the loss analysis using variance has not been done. In this study, the authors propose a framework to analyse variance change in the resource consumption prediction problems. This framework is made up of three steps. First, an original variance decomposition is proposed in view of data sampling and partitioning process. Second, in order to study the influence of data sampling and partitioning process to the variance, the enhanced Friedman test plus Nemenyi post-hoc test is introduced. Lastly, they propose a corrected t-test to analyse the performances of two regression algorithms: auto-regressive integrated moving average and artificial neuron network. In the experiments, they analyse the variance in two levels: operating system level and application level. They find the result that k is equal to ten for k-fold cross-validation is proper for resource consumption prediction, although the contribution to variance is almost same for the sensitivity of forecasted estimation loss in consideration of data partitioning process and the sensitivity of forecasted estimation loss in consideration of data sampling procedure.