Showing papers by "Betty H. C. Cheng published in 2012"

A taxonomy of uncertainty for dynamically adaptive systems

Abstract: Self-reconfiguration enables a dynamically adaptive system (DAS) to satisfy requirements even as detrimental system and environmental conditions arise. A DAS, especially one intertwined with physical elements, must increasingly reason about and cope with unpredictable events in its execution environment. Unfortunately, it is often infeasible for a human to exhaustively explore, anticipate, or resolve all possible system and environmental conditions that a DAS will encounter as it executes. While uncertainty can be difficult to define, its effects can hinder the adaptation capabilities of a DAS. The concept of uncertainty has been extensively explored by other scientific disciplines, such as economics, physics, and psychology. As such, the software engineering DAS community can benefit from leveraging, reusing, and refining such knowledge for developing a DAS. By synthesizing uncertainty concepts from other disciplines, this paper revisits the concept of uncertainty from the perspective of a DAS, proposes a taxonomy of potential sources of uncertainty at the requirements, design, and execution phases, and identifies existing techniques for mitigating specific types of uncertainty. This paper also introduces a template for describing different types of uncertainty, including fields such as source, occurrence, impact, and mitigating strategies. We use this template to describe each type of uncertainty and illustrate the uncertainty source in terms of an example DAS application from the intelligent vehicle systems (IVS) domain.

Using Security Patterns to Model and Analyze Security Requirements

Abstract: Recently, there has been growing interest in identifying patterns for the domain of system security, termed security patterns Currently, those patterns lack comprehensive structure that conveys essential information inherent to security engineering This paper describes research into investigating an appropriate template for security patterns that is tailored to meet the needs of secure systems development In order to maximize comprehensibility, we make use of well-known notations such as the Unified Modeling Language (UML) to represent structural and behavioral information Furthermore, we investigate how verification of requirements properties can be enabled by adding formal constraints to the patterns

Relaxing claims: coping with uncertainty while evaluating assumptions at run time

Abstract: Self-adaptation enables software systems to respond to changing environmental contexts that may not be fully understood at design time. Designing a dynamically adaptive system (DAS) to cope with this uncertainty is challenging, as it is impractical during requirements analysis and design time to anticipate every environmental condition that the DAS may encounter. Previously, the RELAX language was proposed to make requirements more tolerant to environmental uncertainty, and Claims were applied as markers of uncertainty that document how design assumptions affect goals. This paper integrates these two techniques in order to assess the validity of Claims at run time while tolerating minor and unanticipated environmental conditions that can trigger adaptations. We apply the proposed approach to the dynamic reconfiguration of a remote data mirroring network that must diffuse data while minimizing costs and exposure to data loss. Results show RELAXing Claims enables a DAS to reduce adaptation costs.

Repository for model driven development (ReMoDD)

Abstract: The Repository for Model-Driven Development (ReMoDD) contains artifacts that support Model-Driven Development (MDD) research and education. ReMoDD is collecting (1) documented MDD case studies, (2) examples of models reflecting good and bad modeling practices, (3) reference models (including metamodels) that can be used as the basis for comparing and evaluating MDD techniques, (4) generic models and transformations reflecting reusable modeling experience, (5) descriptions of modeling techniques, practices and experiences, and (6) modeling exercises and problems that can be used to develop classroom assignments and projects. ReMoDD provides a single point of access to shared artifacts reflecting high-quality MDD experience and knowledge from industry and academia. This access facilitates sharing of relevant knowledge and experience that improve MDD activities in research, education and industry.

Models@run.time (Dagstuhl Seminar 11481)

Abstract: This report documents the program and the outcomes of Dagstuhl Seminar 11481 ``Models@run.time''. Research on models@run.time seeks to extend the applicability of models and abstractions to the runtime environment, with the goal of providing effective technologies for managing the complexity of evolving software behaviour while it is executing. The Dagstuhl Seminar ``Models@run.time'' brought together a diverse set of researchers and practitioners with a broad range of expertise, including MDE, software architectures, reflection, self-adaptive systems, validation and verification, middleware, robotics and requirements engineering.

Automatically RELAXing a goal model to cope with uncertainty

Abstract: Dynamically adaptive systems (DAS) must cope with changing system and environmental conditions that may not have been fully understood or anticipated during development time. RELAX is a fuzzy logic-based specification language for making DAS requirements more tolerable to unanticipated environmental conditions. This paper presents AutoRELAX, an approach that generates RELAXed goal models that address environmental uncertainty by identifying which goals to RELAX, which RELAX operators to apply, and the shape of the fuzzy logic function that defines the goal satisfaction criteria. AutoRELAX searches for RELAXed goal models that enable a DAS to satisfy its functional requirements while balancing tradeoffs between minimizing the number of RELAXed goals and minimizing the number of adaptations triggered by minor and adverse environmental conditions. We apply AutoRELAX to an industry-provided network application that self-reconfigures in response to adverse environmental conditions, such as link failures.

Applying evolutionary computation to mitigate uncertainty in dynamically-adaptive, high-assurance middleware

Abstract: In this paper, we explore the integration of evolutionary computation into the development and run-time support of dynamically-adaptable, high-assurance middleware. The open-ended nature of the evolutionary process has been shown to discover novel solutions to complex engineering problems. In the case of high-assurance adaptive software, however, this search capability must be coupled with rigorous development tools and run-time support to ensure that the resulting systems behave in accordance with requirements. Early investigations are reviewed, and several challenging problems and possible research directions are discussed.

An ecology-based evolutionary algorithm to evolve solutions to complex problems

Abstract: Evolutionary algorithms have shown great promise in evolving novel solutions to real-world problems, but the complexity of those solutions is limited, unlike the apparently open-ended evolution that occurs in the natural world. In part, nature surmounts these complexity barriers with ecological dynamics that generate a diverse array of raw materials for evolution to build upon. The authors previously introduced Eco-EA, an evolutionary algorithm that integrates these natural ecological dynamics to promote and maintain diversity in the evolving population. Here, we apply the Eco-EA to the real-world software engineering problem of evolving behavioral models for deployed nodes in a remote sensor network for flood monitoring. We show that the Eco-EA evolves good behavioral models faster than a traditional EA, generates a more diverse suite of models than a traditional EA, and creates models that are themselves more evolvable than those created by a traditional EA.

Exploring the evolution of internal control structure using digital enzymes

Abstract: The Digital Enzyme model of control is based on the bottom-up, reactive process of signal transduction found in cells. An earlier study applied a specific instance of the this model to the foraging problem. Here, we extend the system and use it to explore a fundamental question in both biology and evolutionary computation, namely, whether environmental complexity is a driving factor for an organism''s internal control structure. To address this question, we extended the original system to allow the open-ended evolution of the unique programs, instructions, and threads within each controller. With the extended model, we were able to evolve successful foraging strategies that nearly doubled the performance of strategies found in the earlier work. In response to increasing environmental complexity, we discovered a high degree of variation for the number of programs, threads, and instructions that produced successful strategies. These results imply that environmental complexity does not require evolutionary search methods to explore regions of the search space characterized by parallel and distributed control. However, strategies found within these regions were as successful as strategies governed by a single program and thread, highlighting the importance of evolutionary search techniques that enable the open-ended evolution of key internal control components.