Leslie Cheung

Early prediction of software component reliability

The ability to predict the reliability of a software system early in its development, e.g., during architectural design, can help to improve the systems quality in a cost-effective manner. Existing architecture-level reliability prediction approaches focus on system-level reliability and assume that the reliabilities of individual components are known. In general, this assumption is unreasonable, making component reliability prediction an important missing ingredient in the current literature. Early prediction of component reliability is a challenging problem because of many uncertainties associated with components under development. In this paper we address these challenges in developing a software component reliability prediction framework. We do this by exploiting architectural models and associated analysis techniques, stochastic modeling approaches, and information sources available early in the development lifecycle. We extensively evaluate our framework to illustrate its utility as an early reliability prediction approach.

A Study of Web Services Performance Prediction: A Client's Perspective

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 third party 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 ¡V 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).

A Comprehensive Exploration of Challenges in Architecture-Based Reliability Estimation

A plasma processing apparatus such as a plasma etching apparatus, which is not subject to arcing to the gas distributor plate which is caused by secondary potentials generated by polymers adhering to a gas distribution plate. The gas distribution plate is electrically isolated from the ground electric potential, and does not have any polarity. The gas distribution plate may be formed of an insulating material. Furthermore, a support plate may be adapted to fix the gas distribution plate to a chamber of the apparatus in such a manner that the gas distribution plate is detachably coupled with the support plate. Thereby, it is easier to separate the gas distribution plate from the apparatus to remove the accumulated polymers during the plasma process.

Identifying and Addressing Uncertainty in Architecture-Level Software Reliability Modeling

Assessing reliability at early stages of software development, such as at the level of software architecture, is desirable and can provide a cost-effective way of improving a software systems quality. However, predicting a components reliability at the architectural level is challenging because of uncertainties associated with the system and its individual components due to the lack of information. This paper discusses representative uncertainties which we have identified at the level of a systems components, and illustrates how to represent them in our reliability modeling framework. Our preliminary evaluation indicates promising results in our frameworks ability to handle such uncertainties.

Architecture-level reliability prediction of concurrent systems

Stringent requirements on modern software systems dictate evaluation of dependability qualities, such as reliability, as early as possible in a systems 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.

SHARP: a scalable approach to architecture-level reliability prediction of concurrent systems

Early prediction of reliability is important in building dependable software. Existing approaches are unable to model concurrent systems in a scalable way. To address the scalability challenge, we propose a framework that is applicable at the architecture level. Our framework achieves scalability by approaching the system from the perspective of usage scenarios and by employing a hierarchical solution. Specifically, we solve lower granularity scenario-based submodels and a higher granularity system model; we then combine their results to obtain a system reliability estimate. Our evaluation indicates that (a) the proposed hierarchical framework is accurate, and (b) that it is more scalable than existing techniques.

A fault tolerance protocol for uploads: design and evaluation

This paper investigates fault tolerance issues in Bistro, a wide area upload architecture. In Bistro, clients first upload their data to intermediaries, known as bistros. A destination server then pulls data from bistros as needed. However, during the server pull process, bistros can be unavailable due to failures, or they can be malicious, i.e., they might intentionally corrupt data. This degrades system performance since the destination server may need to ask for retransmissions. As a result, a fault tolerance protocol is needed within the Bistro architecture. Thus, in this paper, we develop such a protocol which employs erasure codes in order to improve the reliability of the data uploading process. We develop analytical models to study reliability and performance characteristics of this protocol, and we derive a cost function to study the tradeoff between reliability and performance in this context. We also present numerical results to illustrate this tradeoff.