A Game of Attribute Decomposition for Software Architecture Design

Attribute-driven software architecture design aims to provide decision support by taking into account the quality attributes of softwares. A central question in this process is: What architecture design best fulfills the desirable software requirements? To answer this question, a system designer needs to make tradeoffs among several potentially conflicting quality attributes. Such decisions are normally ad-hoc and rely heavily on experiences. We propose a mathematical approach to tackle this problem. Game theory naturally provides the basic language: Players represent requirements, and strategies involve setting up coalitions among the players. In this way we propose a novel model, called decomposition game, for attribute-driven design. We present its solution concept based on the notion of cohesion and expansion-freedom and prove that a solution always exists. We then investigate the computational complexity of obtaining a solution. The game model and the algorithms may serve as a general framework for providing useful guidance for software architecture design. We present our results through running examples and a case study on a real-life software project.Comment: 23 pages, 5 figures, a shorter version to appear at 12th International Colloquium on Theoretical Aspects of Computing (ICTAC 2015

Cardinal Scales for Public Health Evaluation

Policy studies often evaluate health for a population by summing the individuals’ health as measured by a scale that is ordinal or that depends on risk attitudes. We develop a method using a different type of preferences, called preference intensity or cardinal preferences, to construct scales that measure changes in health. The method is based on a social welfare model that relates preferences between changes in an individual’s health to preferences between changes in health for a population.public health evaluation; social welfare; preference intensity; health state

Preferences Between Continuous Streams of Events

Cost-benefit and health policy studies often model a consequence occurring over time as a continuous stream of events. Such a consequence is measured by the rates at which events occur or by the states that occur, and the value of the consequence is measured by an integral. This paper presents a foundation for such models. It defines conditions on preferences between consequences that are equivalent to an integral value function having a discounting function and an intertemporal equity function.discounting; equity; continuous time; value function; evaluation

Credibility-Based Binary Feedback Model for Grid Resource Planning

In commercial grids, Grid Service Providers (GSPs) can improve their profitability by maintaining the lowest possible amount of resources to meet client demand. Their goal is to maximize profits by optimizing resource planning. In order to achieve this goal, they require an estimate of the demand for their service, but collecting demand data is costly and difficult. In this paper we develop an approach to building a proxy for demand, which we call a value profile. To construct a value profile, we use binary feedback from a collection of heterogeneous clients. We show that this can be used as a proxy for a demand function that represents a client’s willingness-to-pay for grid resources. As with all binary feedback systems, clients may require incentives to provide feedback and deterrents to selfish behavior, such as misrepresenting their true preferences to obtain superior services at lower costs. We use credibility mechanisms to detect untruthful feedback and penalize insincere or biased clients. Finally, we use game theory to study how cooperation can emerge in this community of clients and GSPs

Does structure influence performance in downstream supply chain?

In 1989, John Sterman has explained in the famous beer game the bullwhip effect in downstream supply chain and how structure influences behavior of supply chain members. In this article, we try to find answers to the following questions: Does structure influence performance in downstream supply chain? Can be identified a network configuration that performs better than other configurations? In finding answers to the research questions, we considered the axiom ‘what it is not measured, it cannot be managed’. In the study, we took SCOR metrics at first level as performance metrics and best practices to express each SCOR dimension. 30 top executives from World Class Manufacturers were surveyed to rate the importance of the metrics and best practices in measuring performance in downstream supply chain. The second step was to develop a multiple attribute utility model (MAUT) to select from the seven configurations identified the one that has the highest performance.downstream supply chain network; multiple attribute utility model; SCOR; performance metrics; best practices.

Towards Specifying And Evaluating The Trustworthiness Of An AI-Enabled System

Applied AI has shown promise in the data processing of key industries and government agencies to extract actionable information used to make important strategical decisions. One of the core features of AI-enabled systems is the trustworthiness of these systems which has an important implication for the robustness and full acceptance of these systems. In this paper, we explain what trustworthiness in AI-enabled systems means, and the key technical challenges of specifying, and verifying trustworthiness. Toward solving these technical challenges, we propose a method to specify and evaluate the trustworthiness of AI-based systems using quality-attribute scenarios and design tactics. Using our trustworthiness scenarios and design tactics, we can analyze the architectural design of AI-enabled systems to ensure that trustworthiness has been properly expressed and achieved.The contributions of the thesis include (i) the identification of the trustworthiness sub-attributes that affect the trustworthiness of AI systems (ii) the proposal of trustworthiness scenarios to specify trustworthiness in an AI system (iii) a design checklist to support the analysis of the trustworthiness of AI systems and (iv) the identification of design tactics that can be used to achieve trustworthiness in an AI system