No abstract

Stringent requirements on modern software systems dictate evaluation of dependability qualities, such as reliability, as early as possible in a system's life cycle. A primary shortcoming of the existing design-time reliability prediction approaches is their lack of support for modeling and analyzing concurrency in a scalable way. To address the scalability challenge, we propose SHARP, an architecture-level reliability prediction framework that analyzes a hierarchical scenario-based specification of system behavior. It achieves scalability by utilizing the scenario relations embodied in this hierarchy. SHARP first constructs and solves models of basic scenarios, and combines the obtained results based on the defined scenario dependencies; the dependencies we handle are sequential and parallel execution of multiple scenarios. This process iteratively continues through the scenario hierarchy until finally obtaining the system reliability estimate. Our evaluations performed on real-world specifications indicate that SHARP is (a) almost as accurate as a traditional non-hierarchical method, and (b) more scalable than other existing techniques.

Abstract. Architecture-based reliability estimation is challenging: modern software is complex with numerous factors affecting a system's reliability. In this article, we address three core challenges for architecture-based estimation of a system's reliability: (1) defining an appropriate failure model based on characteristics of the system being analyzed, (2) dealing with uncertainties of the reliability-related parameters, due to the lack of system implementation, and (3) overcoming the barriers of complexity and scale inherent in modern software. For each challenge, we identify the essential elements of the problem space, outline promising solutions, and illustrate the problems and solutions using a robotics case study. First, we show how a failure model can be derived from the system requirements and architecture. Second, we suggest how information sources available during architectural design can be combined to mitigate model parameter uncertainties. Third, we foresee hierarchical techniques as a promising way of improving the computational tractability of reliability models.

Software reliability techniques are aimed at reducing or eliminating failures in software systems. Reliability in software systems is typically measured during or after system implementation. However, software engineering methodology lays stress on doing the "correct things" early on in the software development lifecycle in order to curb development and maintenance costs. In this paper, we propose a framework for reliability estimation of software components at the level of software architecture.

The Web service (WS) paradigm is an emerging approach to building Web applications, in which software designers typically build new WSs by leveraging existing, third-party WSs. Understanding performance characteristics of third party WSs is critical to the overall system performance. Although such performance evaluation can be done through testing of thirdparty WSs, it is quite an expensive process. This is especially the case when testing at high workloads, because performance degradations are likely to occur, which may render the WS under testing unusable during the tests' duration. Avoiding testing at high workloads by applying standard extrapolation approaches from data collected at low workloads (e.g., using regression analysis) results in a lack of accuracy. To address this challenge, in this paper, we propose a framework that utilizes the benefits of queueing models to guide the extrapolation process, while achieving accuracy in both regimes -low and high workloads. Our extensive experiments show that our approach gives accurate results as compared to standard techniques (i.e., use of regression analysis alone).