Lalit Kumar Singh

Software Reliability Early Prediction in Architectural Design Phase: Overview and Limitations

In recent times, computer based systems are frequently used for protection and control in the various industries viz Nuclear, Electrical, Mechanical, Civil, Electronics, Medical, etc. From the operating experience of those computer based systems, it has been found that the failure of which can lead to the severe damage to equipments or environmental harm. The culprit of this accident is nobody other than our software, whose reliability has not been ensured in those conditions. Also for real time system, throughput of the system and average response time are very important constructs/ metrics of reliability. Moreover neither of the software reliability model is available which can be fitted generically for all kinds of software. So, we can ensure reliability at the early stage i.e. during design phase by architecturing the software in a better way. The objective of this paper is to provide an overview of the state-of-the-art research in the area of architecture-based software reliability analysis. We then describe the shortcomings and the limiting assumptions underlying the prevalent research. We also propose various approaches which have the potential to address the existing limitations

Early Prediction of Software Reliability: A Case Study with a Nuclear Power Plant System

Existing methods to predict software reliability using the Markov chain are based on assumed state-transition probabilities. A new prediction approach applied to a nuclear plants feed-water system yielded results that were 96.9 percent accurate relative to the systems actual reliability. Across 38 operational datasets, the average accuracy was 99.67 percent.

Design Verification of Instrumentation and Control Systems of Nuclear Power Plants

Instrumentation and Control systems are the nervous system of a nuclear power plant. They monitor all facets of the plants health and help respond with care and adjustments needed, thus ensuring goals of efficient power production and safety. Due to safety significance of I&C, it becomes increasingly important to have a design verification methodology which ensures I&C systems fully functional. The strategy discussed the system modeling for design verification using Petri Net, converting it into Markov Chain and solving the linear system mathematically. It also exploits the best attribute of the created Markov model. The approach has been validated on seven sets of operation profile data of reactor control system of seven Nuclear Power Plants. The singular & plural of an acronym are always spelled the same.

Computing Transition Probability in Markov Chain for Early Prediction of Software Reliability

Early prediction of software reliability provides basis for evaluating potential reliability during early stages of a project. It also assists in evaluating the feasibility of proposed reliability requirements and provides a rational basis for design and allocation decisions. Many researchers have proposed different approaches to predict the software reliability based on a Markov model. The transition probabilities in between the states of the Markov model are input parameters to predict the software reliability. In the existing approaches, these probabilities are either assumed on some knowledge or computed using analytical method, and hence, it does not give accurate predicted reliability figure. Some authors compute them using operational profile data, but that is possible only after the deployment of the software, and this is not early prediction. The work in this paper is devoted to demonstrate the computation of transition probability in the Markov reliability model taking a case study. The proposed approach has been validated on 47 sets of real data. Copyright

Approach for parameter estimation in Markov model of software reliability for early prediction: a case study

Early prediction of software reliability may be used to evaluate design feasibility, compare design alternatives, identify potential failure areas, trade-off system design factors, track reliability improvements, identify the cost overrun at an early stage and to provide optimal development strategies. Many researchers have proposed different approaches to predict the software reliability based on Markov model but the uncertainty associated with these approaches is to find the transition probabilities in between the two states of the Markov chain. The authors propose an approach to address this problem by modelling the software system through Petri Net, converting it into Markov chain and solving the linear system mathematically. The validation of the proposed approach has also been shown by comparing the predicted reliability, based on predicted transition probability, with computed reliability, based on operational profile of safety critical software of Nuclear Power Plant.

Reliability prediction through system modeling

Quantifying software reliability, such as performance and dependability, through stochastic behavior models (or labeled transition systems) is already a common practice in the software analysis community. However, those models are usually too fine grained to represent an accurate view of the software system by its stakeholders. Scenarios, on the other hand, are capable not only to describe the system traces as behavior models do but also depict very clearly the system components designed to provide the intended system behavior as well as to outline a high level architecture view of the system being described. In this paper, we introduce a case study of a safety critical computer based system that is running in an Indian Nuclear Power Plant. We define clear component interfaces, from which we analyze its software reliability.

Modeling and Prediction of Performability of Safety Critical Computer Based Systems Using Petri Nets

Computer based safety critical systems are progressively replacing analog systems in safety-critical applications like nuclear power plants. Hence these systems require modeling techniques to estimate performability in the early stages of the system development life cycle. This paper addresses the dynamic modeling of Test Facility of a safety critical system used in Nuclear Power plant. System function and its architecture have been explained. No details of the system have been shown that can jeopardize the confidentiality & hence exact details have been concealed. Special attention has been paid to the modeling part of a communication module of this system in between the two computer based subsystems that are running on different platform, VxWorks and Linux, using a mathematical formalism, Petri Net. Also, formalism of Petri nets with particular emphasis on the application of the methodology in the area of the performance and reliability modeling and analysis of the computer based systems, taking Test Facility system as a case study, is illustrated in this paper. This paper also describes the use of TimeNET (Timed Net Evaluation Tool), a software package for the modeling and evaluation of stochastic Petri nets with non-exponentially distributed firing times to evaluate performability.

Dependability Analysis of Safety Critical Real-Time Systems by Using Petri Nets

The failure of such systems leads to the catastrophic effects, including injury or death to humans, and harm to the environment. Petri nets (PNs) have been widely used for verification and validation of real-time systems. However, the existing approaches do not consider the critical aspects of reliability and safety that include nonliveness, deadlock, stability, and throughput. In this paper, we introduce these as metrics of reliability and safety for safety critical real-time systems. This paper also proposes an innovative methodology for analysis of nonliveness, deadlock, stability, and throughput metrics by linear programming using PN modeling. The application of the proposed techniques has been validated by applying it on four different safety critical systems, running in six nuclear power plants and shown for reactor protection system.

Impact of change in component reliabilities on system reliability estimation

The computation of a set of performance indicators of a computer-based system can be achieved through dependability analysis. Researchers have proposed several methods and tools that have an ability to give a prognosis for the failure of a computer-based system. These tools and methods are classified into three main approaches: model-based, data-driven, and experience-based prognostics. Wherever sufficient real data is available, the data-driven approach is appropriate, which can be transformed into behavior models using Hidden Markov Models, which fall in a subclass of Bayesian networks. In a Bayesian framework, the estimates of reliabilities of components of a computer-based system are updated using operational profile data as new information of reliability of one or more node becomes available for the identification of robustness of a system. In this paper, we show, using Bayesian Networks, how to update the reliability of individual components and the reliability of a whole computer-based system when the reliability of any component in the system changes. We use a running safety-critical computer-based system from a nuclear power plant as a case study.

A Probabilistic Hazard Assessment Framework for Safety-Critical and Control Systems: A Case Study for a Nuclear Power Plant

Any risk in safety-critical or control applications may lead to catastrophic disaster; hence, safety is a primary concern for such applications. The impact of risk varies from minor inconvenience and cost to personal injury, significant economic loss, and death. Therefore, a safety assessment process should be an inherent part of the system development process to make a system safe or to ensure that the effects from failures are minimized. This paper deals with a new probabilistic approach to quantify the safety of safety-critical systems (SCSs) and control systems based on probabilistic safety assessment to deal with the shortcomings of the existing techniques. The methodology has been tested on 29 operational data sets to validate its effectiveness. This paper demonstrates the methodology on the digital feedwater controller system of a nuclear power plant. The results indicate that the method can identify possible hazards and quantify such hazards of a SCS.