Modelling Open-Source Software Reliability Incorporating Swarm Intelligence-Based Techniques

In the software industry, two software engineering development best practices coexist: open-source and closed-source software. The former has a shared code that anyone can contribute, whereas the latter has a proprietary code that only the owner can access. Software reliability is crucial in the industry when a new product or update is released. Applying meta-heuristic optimization algorithms for closed-source software reliability prediction has produced significant and accurate results. Now, open-source software dominates the landscape of cloud-based systems. Therefore, providing results on open-source software reliability - as a quality indicator - would greatly help solve the open-source software reliability growth-modelling problem. The reliability is predicted by estimating the parameters of the software reliability models. As software reliability models are inherently nonlinear, traditional approaches make estimating the appropriate parameters difficult and ineffective. Consequently, software reliability models necessitate a high-quality parameter estimation technique. These objectives dictate the exploration of potential applications of meta-heuristic swarm intelligence optimization algorithms for optimizing the parameter estimation of nonhomogeneous Poisson process-based open-source software reliability modelling. The optimization algorithms are firefly, social spider, artificial bee colony, grey wolf, particle swarm, moth flame, and whale. The applicability and performance evaluation of the optimization modelling approach is demonstrated through two real open-source software reliability datasets. The results are promising.Comment: 14 pages, 11 figures, 7 table

Improving software reliability growth model selection ranking using particle swarm optimization

Reliability of software always related to software failures and a number of software reliability growth models (SRGMs) have been proposed past few decades to predict software reliability. Different characteristics of SRGM leading to the study and practices of SRGM selection for different domains. Appropriate model must be chosen for suitable domain in order to predict the occurrence of the software failures accurately then help to estimate the overall cost of the project and delivery time. In this paper, particle swarm optimization (PSO) method is used to optimize a parameter estimation and distance based approach (DBA) is used to produce SRGM model selection ranking. The study concluded that the use of PSO for optimizing the SRGM’s parameter has provided more accurate reliability prediction and improved model selection rankings. The model selection ranking methodology can facilitate a software developer to concentrate and analyze in making a decision to select suitable SRGM during testing phases. � 2005 - 2017 JATIT & LLS

Optimal Allocation of Resources in Reliability Growth

Reliability growth testing seeks to identify and remove failure modes in order to improve system reliability. This dissertation centers around the resource allocation across the components of a multi-component system to maximize system reliability. We summarize this dissertation’s contributions to optimal resource allocation in reliability growth. Chapter 2 seeks to deploy limited testing resources across the components of a series-parallel system in effort to maximize system reliability under the assumption that each component’s reliability exhibits growth according to an AMSAA model with known parameters. An optimization model for this problem is developed and then extended to consider the allocation of testing resources in a series-parallel system with consideration for the possibility of testing at different levels (system, subsystem, and component). We contribute a class of exact algorithms that decomposes the problem based upon the series-parallel structure. We prove the algorithm is finite, compare it with heuristic approaches on a set of test instances, and provide detailed analyses of numerical examples. In Chapter 3, we extend model in Chapter 2 to solve a robust optimization version of this problem in which AMSAA parameters are uncertain but assumed to lie within a budget-restricted uncertainty set. We model the problem of robust allocation of testing resources to maximize system reliability for both series and series-parallel systems, and we develop and analyze exact solution approaches for this problem based on a cutting plane algorithm. Computational results demonstrate the value of the robust optimization approach as compared to deterministic alternatives. In the last chapter, we develop a new model that merges testing components and installing redundancies within an integrated optimization model that maximizes system reliability. Specifically, our model considers a series-parallel system in which the system reliability can be improved by both testing components and installing redundant components. We contribute an exact algorithm that decomposes the problem into smaller integer linear programs. We prove that this algorithm is finite and apply it to a set of instances. Experiments demonstrate that the integrated approach generates greater reliabilities than applying test planning and redundancy allocation models iteratively, and moreover, it yields significant savings in computational time

An interactive metaheuristic search framework for software serviceidentification from business process models

In recent years, the Service-Oriented Architecture (SOA) model of computing has become widely used and has provided efficient and agile business solutions in response to inevitable and rapid changes in business requirements. Software service identification is a crucial component in the production of a service-oriented architecture and subsequent successful software development, yet current service identification methods have limitations. For example, service identification methods are either not sufficiently comprehensive to handle the totality of service identification activities, or they lack computational support, or they pay insufficient attention to quality checks of resulting services. To address these limitations, comprehensive computationally intelligent support for software engineers when deriving software services from an organisation’s business process models shows great potential, especially when the impact of human preference on the quality of the resulting solutions can be incorporated. Accordingly, this research attempts to apply interactive metaheuristic search to effectively bridge the gap between business and SOA technology and so increase business agility.A novel, comprehensive framework is introduced that is driven by domain independent role-based business process models, and uses an interactive metaheuristic search-based service identification approach based on a genetic algorithm, while adhering to SOA principles. Termed BPMiSearch, the framework is composed of three main layers. The first layer is concerned with processing inputs from business process models into search space elements by modelling input data and presenting them at an appropriate level of granularity. The second layer focuses on identifying software services from the specified search space. The third layer refines the resulting services to map the business elements in the resulting candidate services to the corresponding service components. The proposed BPMiSearch framework has been evaluated by applying it to a healthcare domain case study, specifically, Cancer Care and Registration (CCR) business processes at the King Hussein Cancer Centre, Amman, Jordan.Experiments show that the impact of software engineer interaction on the quality of the outcomes in terms of search effectiveness, efficiency, and level of user satisfaction, is assessed. Results show that BPMiSearch has rapid search performance to positively support software engineers in the identification of services from role-based business process models while adhering to SOA principles. High-quality services are identified that might not have been arrived at manually by software engineers. Furthermore, it is found that BPMiSearch is sensitive and responsive to software engineer interaction resulting in a positive level of user trust, acceptance, and satisfaction with the candidate services