Performance is a nonfunctional software attribute that plays a crucial role in wide application domains spreading from safety-critical systems to e-commerce applications. Software risk can be quantified as a combination of the probability that a software system may fail and the severity of the damages caused by the failure. In this paper, we devise a methodology for estimation of performance-based risk factor, which originates from violations, of performance requirements, (namely, performance failures). The methodology elaborates annotated UML diagrams to estimate the performance failure probability and combines it with the failure severity estimate which is obtained using the functional failure analysis. We are thus able to determine risky scenarios as well as risky software components, and the analysis feedback can be used to improve the software design. We illustrate the methodology on an e-commerce case study using step-by step approach, and then provide a brief description of a case study based on large real system.

Recent evidences indicate that most faults in software systems are found in only a few of a system's components [1]. The early identification of these components allows an organization to focus on defect detection activities on high risk components, for example by optimally allocating testing resources [2], or redesigning components that are likely to cause field failures. This paper presents a prototype tool called Architecture-level Risk Assessment Tool (ARAT) based on the risk assessment methodology presented in [3]. The ARAT provides risk assessment based on measures obtained from Unified Modeling Language (UML) artifacts [4]. This tool can be used in the design phase of the software development process. It estimates dynamic metrics [5] and automatically analyzes the quality of the architecture to produce architectural-level software risk assessment [3].

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.