Reasoning about partial goal satisfaction for requirements and design engineering

Abstract

Exploring alternative options is at the heart of the requirements and design processes. Different alternatives contribute to different degrees of achievement of non-functional goals about system safety, security, performance, usability, and so forth. Such goals in general cannot be satisfied in an absolute, clear-cut sense. Various qualitative and quantitative frameworks have been proposed to support the assessment of alternatives for design decision making. In general they lead to limited conclusions due to the lack of accuracy and measurability of goal formulations and the lack of impact propagation rules along goal contribution links. The paper presents techniques for specifying partial degrees of goal satisfaction and for quantifying the impact of alternative system designs on the degree of goal satisfaction. The approach consists in enriching goal refinement models with a probabilistic layer for reasoning about partial satisfaction. Within such models, non-functional goals are specified in a precise, probabilistic way; their specification is interpreted in terms of application-specific measures; impact of alternative goal refinements is evaluated in terms of refinement equations over random variables involved in the system's functional goals. A systematic method is presented for guiding the elaboration of such models. The latter can then be used to assess the impact of alternative decisions on the degree of goal satisfaction or to derive quantitative, fine-grained requirements on the software to achieve the higher-level goals.

Applying the UFO Ontology to Design an Agent-Oriented Engineering Language

“…Sometimes, softgoals are taken to represent non-functional requirements [11]. In other times, a softgoal is considered as a fuzzy proposition, i.e., one which can be partially satisfied (or satisfied to a certain degree, or yet, satisficed) by Situations [12]. We here take a different stance, namely, that a softgoal is one "subjective to interpretation" and "context-specific".…”

UML profile to support requirements engineering with KAOS

“…Perhaps the most successful goal-oriented method is KAOS ( [5] and [8]). KAOS is goaldriven: having identified a few preliminary goals for a system-to-be, the KAOS framework facilitates the identification of further goals -and the requisites, objects, agents, and actions of the system.…”

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AbstractOne of the most important approaches to requirements engineering of the last ten years is the KAOS model as presented in [5] and [8]. We introduce a profile that allows the KAOS model to be represented in the UML.

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Automatically Discovering Properties That Specify the Latent Behavior of UML Models

“…Three broad categories of approaches have been developed to produce properties that could be used for analysis: Requirements discovery approaches (e.g., [5]) examine testing and deployment artifacts to detect missing or erroneous properties; process improvements have been proposed as part of these approaches. Refinement-based approaches (e.g., [6]) infer properties from formally specified goals or requirements. Lastly, specification generation techniques (e.g., [7][8][9][10][11][12][13][14][15]) infer properties from a representation of a system (e.g., a model or code) or a derivative of the system (e.g., execution traces).…”