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Abstract-Risk assessment is an essential part in managing software development. Performing risk assessment during the early development phases enhances resource allocation decisions. In order to improve the software development process and the quality of software products, we need to be able to build risk analysis models based on data that can be collected early in the development process. These models will help identify the high-risk components and connectors of the product architecture, so that remedial actions may be taken in order to control and optimize the development process and improve the quality of the product. In this paper, we present a risk assessment methodology which can be used in the early phases of the software life cycle. We use the Unified Modeling Language (UML) and commercial modeling environment Rational Rose Real Time (RoseRT) to obtain UML model statistics. First, for each component and connector in software architecture, a dynamic heuristic risk factor is obtained and severity is assessed based on hazard analysis. Then, a Markov model is constructed to obtain scenarios risk factors. The risk factors of use cases and the overall system risk factor are estimated using the scenarios risk factors. Within our methodology, we also identify critical components and connectors that would require careful analysis, design, implementation, and more testing effort. The risk assessment methodology is applied on a pacemaker case study.

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Many architecture-based software reliability models have been proposed in the past without any attempt to establish a relationship among them. The aim of this paper is tojill this gap. First, the unifying structural properties of the models are exhibited and the theoretical relationship is established. Then, the estimates provided by the models are compared using an empirical case study. The program chosen for the case study consists of almost 10,000 lines of C code divided into several components. The faulty version of the program was obtained by reinserting the faults discovered during integration testing and operational usage and the correct version was used as an oracle. A set of test cases was generated randomly accordingly to the known operational projile. The results show that I ) all models give reasonably accurate estimations compared to the actual reliability and 2 ) faults present in the components injuence both components reliabilities and the way components interact.