Exploiting a Goal-Decomposition Technique to Prioritize Non-functional Requirements

Business stakeholders need to have clear and realistic goals if they want to meet commitments in application development. As a consequence, at early stages they prioritize requirements. However, requirements do change. The effect of change forces the stakeholders to balance alternatives and reprioritize requirements accordingly. In this paper we discuss the problem of priorities to non-functional requirements subjected to change. We, then, propose an approach to help smooth the impact of such changes. Our approach favors the translation of nonoperational specifications into operational definitions that can be evaluated once the system is developed. It uses the goal-question-metric method as the major support to decompose non-operational specifications into operational ones. We claim that the effort invested in operationalizing NFRs helps dealing with changing requirements during system development. Based on\ud this transformation and in our experience, we provide guidelines to prioritize volatile non-functional requirements

Planning Rural Water Services in Nicaragua: A Systems-Based Analysis of Impact Factors Using Graphical Modeling

The success or failure of rural water services in the developing world is a result of numerous factors that interact in a complex set of connections that are difficult to separate and identify. This research effort presented a novel means to empirically reveal the systemic interactions of factors that influence rural water service sustainability in the municipalities of Darío and Terrabona, Nicaragua. To accomplish this, the study employed graphical modeling to build and analyze factor networks. Influential factors were first identified by qualitatively and quantitatively analyzing transcribed interviews from community water committee members. Factor influences were then inferred by graphical modeling to create factor network diagrams that revealed the direct and indirect interaction of factors. Finally, network analysis measures were used to identify “impact factors” based on their relative influence within each factor network. Findings from this study elucidated the systematic nature of such factor interactions in both Darío and Terrabona, and highlighted key areas for programmatic impact on water service sustainability for both municipalities. Specifically, in Darío, the impact areas related to the current importance of water service management by community water committees, while in Terrabona, the impact areas related to the current importance of finances, viable water sources, and community capacity building by external support. Overall, this study presents a rigorous and useful means to identify impact factors as a way to facilitate the thoughtful planning and evaluation of sustainable rural water services in Nicaragua and beyond

Ignorable Information in Multi-Agent Scenarios

In some multi-agent scenarios, identifying observations that an agent can safely ignore reduces exponentially the size of the agent's strategy space and hence the time required to find a Nash equilibrium. We consider games represented using the multi-agent influence diagram (MAID) framework of Koller and Milch [2001], and analyze the extent to which information edges can be eliminated. We define a notion of a safe edge removal transformation, where all equilibria in the reduced model are also equilibria in the original model. We show that existing edge removal algorithms for influence diagrams are safe, but limited, in that they do not detect certain cases where edges can be removed safely. We describe an algorithm that produces the "minimal" safe reduction, which removes as many edges as possible while still preserving safety. Finally, we note that both the existing edge removal algorithms and our new one can eliminate equilibria where agents coordinate their actions by conditioning on irrelevant information. Surprisingly, in some games these "lost" equilibria can be preferred by all agents in the game

Graphical Markov models, unifying results and their interpretation

Graphical Markov models combine conditional independence constraints with graphical representations of stepwise data generating processes.The models started to be formulated about 40 years ago and vigorous development is ongoing. Longitudinal observational studies as well as intervention studies are best modeled via a subclass called regression graph models and, especially traceable regressions. Regression graphs include two types of undirected graph and directed acyclic graphs in ordered sequences of joint responses. Response components may correspond to discrete or continuous random variables and may depend exclusively on variables which have been generated earlier. These aspects are essential when causal hypothesis are the motivation for the planning of empirical studies. To turn the graphs into useful tools for tracing developmental pathways and for predicting structure in alternative models, the generated distributions have to mimic some properties of joint Gaussian distributions. Here, relevant results concerning these aspects are spelled out and illustrated by examples. With regression graph models, it becomes feasible, for the first time, to derive structural effects of (1) ignoring some of the variables, of (2) selecting subpopulations via fixed levels of some other variables or of (3) changing the order in which the variables might get generated. Thus, the most important future applications of these models will aim at the best possible integration of knowledge from related studies.Comment: 34 Pages, 11 figures, 1 tabl

Transparency in Complex Computational Systems

Scientists depend on complex computational systems that are often ineliminably opaque, to the detriment of our ability to give scientific explanations and detect artifacts. Some philosophers have s..