Software reliability

International Journal Of Engineering And Computer Science, 2016

In this paper analysis of a semi-markov model is done with reference to famous Jelinski-Moranda model which is probably the first model in software reliability. Fault removal resulting from the execution of program depends on the occurrence of the associated failure. Occurrence of failure depends both on the length of time for which the software has been executing and on the execution environment or operating condition. When different functions are executed, different faults are encountered and failures that are exhibited tend to be different.

Proceedings of the World Congress on …, 2009

This paper presents a new model sequential Bayesian technique for software reliability characterization using a growth curve formulation that allows model parameters to vary as a function of covariate information. The approaches include probabilistic models that aim at predicting reliability and other elements of software quality on the basis of program properties such as size and complexity, and statistical models that base reliability prediction on an analysis of failure data. We describe a Sequential Bayesian Technique and model evaluation which allows for integration of historical information and expert opinion in the form of prior distributions on the parameters.

1996

A number of software reliability models have been proposed for assessing the reliability of a software system. In this paper, we discuss the time-domain and data-domain approaches to software reliability modeling, and classify the previously reported models into these two classes based on their underlying assumptions. The data-domain models are further classi ed into fault-seeding and input domain models, while the time-domain models are further classi ed into homogeneous Markov, non-homogeneous Markov and semi-Markov models. We present some representative models belonging to each of the classes, and then discuss the relative merits and limitations of the time and data-domain approaches.

2008

This paper reviews recent developments in Bayesian software reliability modeling. In so doing, emphasis is given to two models which can incorporate the case of reliability deterioration due to potential introduction of new bugs to the software during the development phase. Since the introduction of bugs is an unobservable process, latent variables are introduced to incorporate this characteristic into the models. The two models are based, respectively, on a hidden Markov model and a self-exciting point process with latent variables.

Software plays an important role in every field of human activity today varying from medical diagnosis to remote controlling spacecraft. Hence it is important for the software to provide failure-free performance whenever needed. The Information technology industry has witnessed rapid growth in the recent past. The competition among the firms also increased. The software organization in the developing countries like India can no longer survive on cost advantage alone. The software companies need to deliver reliable and quality software on time. A lot of research has been carried out on software quality management and reliability estimation. The objective of this paper is to provide a brief review of the major research contribution in the field of software reliability and identify the future research areas in software reliability estimation and prediction Keywords: software reliability growth models, nonhomogeneous Poisson process models, s-shaped models, imperfect debugging I. INTRODUCTION Many organizations utilize information technology (IT) to improve productivity, enhance operational efficiency, responsiveness, etc [1] As a result, the IT industry has witnessed tremendous growth in the past few decades. As the number of information technology companies increased, the competition among them also increased. The software organization in the developing countries like India can no longer survive or grow based on cost advantage alone. But delivering reliable and quality software on time within budgeted cost is a challenge for many organizations [2], [3]. Many times the companies would compromise on software testing and release the software with residual defects. This would make the software unreliable. The software reliability is defined as the probability of failure-free operation of a software system for a specified time in a specified environment [4]. The failure of the software during operations can lead to customer dissatisfaction, loss of market share, etc. The failure of a software used in the medical device or that used in air traffic control system can have a disastrous effect on the individual as well as society. Hence it is imperative for the software firms to ensure their product is sufficiently reliable before releasing the software for usage. This paper is a brief review of the important developments happened in the field of software reliability and identifies the future research areas. The remaining part of this article is arranged as follows: the session II describes the literature review methodology, the literature review analysis is given in session III and the conclusion are discussed in session IV. II. LITERATURE REVIEW METHODOLOGY A lot of articles have been presented at conferences, published in journals and books have been written in the last few decades on software reliability estimation and prediction. The aim of this paper is to provide a brief review of the important researches carried on developing software reliability models. The process started with searching for relevant published articles. The scope of the review is limited to the published books and papers published in journals and important conference proceedings. The databases searched are IEEE explore, Science direct, Google scholar and research gate. Two hundred and nine papers are identified for review. After reading the abstract, ninety-seven papers are shortlisted for review. Another twenty-nine papers are later dropped as the content is not directly related to the focus area of the review. Finally, sixty-eight papers are included in the review. The details are given in fig 1.

Sadhana-academy Proceedings in Engineering Sciences, 2009

This paper proposes a sequential Bayesian approach similar to Kalman filter for estimating reliability growth or decay of software. The main advantage of proposed method is that it shows the variation of the parameter over a time, as new failure data become available. The usefulness of the method is demonstrated with some real life data.

This paper explores yet another software reliability modeling framework based on non-homogeneous Markov processes (NHMPs). For two subclasses of NHMPs; generalized binomial processes (GBPs) and generalized Polya processes (GPPs), we formulate 22 novel NHMP-based software reliability models (SRMs) with 11 kinds of baseline intensity functions, which are different from the existing NHMP-based SRMs by Li et al. (2023). Our evaluation of NHMP-based SRMs focuses on assessing their goodness-of-fit and predictive performances using 8 data sets of software fault detection time-domain data and 8 data sets of time-interval data (group data). The results are compared with the well-known NHPP-based SRMs. Through comprehensive numerical experiments, we show that our new modeling framework could provide the strengths in the goodness-of-fit performance in many cases but outperform on the predictive performance in a limited number of cases.

2006

There are many probabilistic and statistical approaches to modelling software reliability. Software reliability estimates are used for various purposes: during development, to make the release decision; and after the software has been taken into use, as part of system reliability estimation, as a basis of maintenance recommendations, and further improvement, or a basis of the recommendation to discontinue the use of the software. This report reviews proposed software reliability models, ways to evaluate them, and the role of software reliability estimation. Both frequentist and Bayesian approaches have been proposed. The advantage of Bayesian models is that various important but nonmeasurable factors, such as software complexity, architecture, quality of verification and validation activities, and test coverage are easily incorporated in the model. Despite their shortcomings – excessive data requirements for even modest reliability claims, difficulty of taking relevant nonmeasurable...

Proceedings of IEEE International Computer Performance and Dependability Symposium

This paper reviews existing Non-Homogeneous Poisson Process (NHPP) models and their limitations, and proposes a more powerful non-homogeneous Markov model of the fault detection/removal problem. In addition, this non-homogeneous Markov model allows for the possibility of a nite time to repair a fault and for imperfections in the repair process. The proposed scheme provides the basis for decision making both during the testing and the operational phase of the software product. Software behavior in the operational phase and the development test phase are related and the release time formulae are derived. Illustrations of the proposed model are provided.

Handbook of Reliability Engineering

... Siddhartha R. Dalal ... t)/[1 − F(t)]. These models are Markovian but not strongly Markovian, except when F is exponential; minor variations of this case were studied by Jelinski and Moranda [15], Shooman [16], Schneidewind [17], Musa [18], Moranda [19], and Goel and Okomoto ...