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The assessment of software’s reliability via developed mathematical models is one of those areas which is studied a lot by management. With new innovations in science and industry, the applicability of artificial neural networks, i.e., ANN has been employed to assess software’s reliability paradigm. ANN is a Machine Learning algorithm which processes the information/data, as it moves through a series of interconnected neurons, which constitute a neural structure. It was observed that, with a perfect combination of assigned weights and activation function, ANN can predict fault-occurrence pattern of a software as an output at its output node. Keeping this in mind, the authors have made some alterations in its network architecture, and have developed a model which resembles the fault occurrence phenomenon of a software system having different severity of faults. Further, this concept has been extended to assess the reliability indices of multi-release of software system. The performance analysis of developed structure is showcased with the help of comparison criterions, on which it was performing better than the traditionally-acknowledged procedures of parameter estimation.

The today's fast-paced, competitive environment in the field of Science and Technology, demands highly reliable hardware and software in order to achieve new breakthroughs in quality and productivity. In this scenario, first release of software products includes enough features and functionality to make it useful for the customers. Later, software companies have to come up with upgradation or add-ons in their software to survive in the market through a series of releases. Each succeeding upgradation offers some innovative performance or new functionality, distinguishing itself from the past releases. In one-dimensional Software Reliability Growth Models (SRGM) researcher used one factor such as Testing-Time, Testing-Effort or Coverage, etc. but within a two-dimensional SRGM environment, the process depends on two-types of reliability growth factors like Testing-time and Testing-effort. In addition, we also consider the combined effect of bugs encountered during testing of present release and user reported bugs from the operational phase. The model developed in the paper takes into consideration the testing and the operational phase where fault removal phenomenon follows, logistic and Weibull model, respectively. The paper also comprises of formulating an optimal release problem based on Multi-Attribute Utility Theory (MAUT). Lastly, the model validation is done on real dataset of software already released in the market with successive generations.

With the growing competition and the demand of the customers, a software organization needs to regularly provide up-gradations and add features to its existing version of software. For the organization, creating these software upgrades means an increase in the complexity of the software which in turn leads to the increase in the number of faults. Also, the faults left undetected in the previous version need to be addressed in this phase. Many software reliability growth models have been proposed to model the phenomenon of multi-release problems using two stage failure observation and correction processes. The model proposed in this paper partitions the fault removal process into a two-stage process which includes fault detection process and fault removal process considering the joint effect of premeditated release pressure and resource restrictions using a well-known Cobb–Douglas production function for the multi release problem of a software. The faults detected in the operational phase of the previous release or left incomplete are also incorporated in the next release. A generalized framework for the multi-release problem in which fault detection follows an exponential distribution function and fault correction follows Gamma distribution function is proposed and verified on a real data set of four releases of software. The estimated parameters and comparison criteria are also given.

Across the globe almost every organization is dependent on information technology for increasing their business efficiency. This has led to huge demand for reliable and good quality software. Innovation is most important to attain success in software industry. Software companies need to keep bringing upgradations or add-ons in the software to compete in the market. In the present framework we propose a generalized mathematical modeling for multiple software releases. In this framework we examine the collective effect of testing time and resources using Cobb–Douglas production function for defining the failure phenomenon using a software reliability growth model (SRGM). In this paper, we consider the practical scenario where there is a possibility of change in the fault detection rate. Fault detection rate can be affected by various factors like testing environment, testing strategy and allocation of resources. Change in these factors during testing phase can lead to increase or decrease in failure intensity function. The time point at which abrupt fluctuations in fault detection rate take place is known as change point. In this paper, a generalized framework for developing a two-dimensional SRGM with change point for multiple software releases has been discussed. We have derived various existing change point models using the proposed framework. The developed models have been validated on real data set.

The efficiency and performance of a software application largely depend on the testing strategy adopted by the firm. Apart from the tools, techniques, and skills used for testing, the duration also plays an important influence in establishing software reliability. This defines the operational performance of the software. The testing duration decision is dependent on the failure behavior depicted by the software during the process of testing and the cost spent at various phases of development. In this paper, we study the multi-release model whose fault removal process is affected by random irregular fluctuations (white noise) and error generation phenomenon and determine the optimal time of testing for a multi-release-based software. The fault removal process is governed by the phenomenon of testing coverage which is affected by random fluctuations. The model shows encouraging results as it handles the stochastic property of the fault detection process. The optimal testing time is determined with the goal to minimize the expected development cost related to software while achieving the desired reliability levels of software for that release using a Genetic Algorithm. A real-life four-release fault dataset of tandem computers has been used to numerically demonstrate the methodology. It is observed through sensitivity analysis that the presence of white noise directly affects the cost and optimal testing duration. The potential to improve sensitivity, flexibility, early detection, discovering unsuspected patterns, and boost fault diagnosis is enhanced by collecting irregular fluctuations in the fault detection rate. This technology, by going beyond existing methodologies, has distinct advantages for detecting faults and can contribute to more dependable and efficient systems in a variety of domains.