Reasoning about Confidence in Goal Satisfaction

Abstract

Goal models are commonly used requirements engineering artefacts that capture stakeholder requirements and their inter-relationships in a way that supports reasoning about their satisfaction, trade-off analysis, and decision making. However, when there is uncertainty in the data used as evidence to evaluate goal models, it is crucial to understand the confidence or trust level in such evaluations, as uncertainty may increase the risk of making premature or incorrect decisions. Different approaches have been proposed to tackle goal model uncertainty issues and risks. However, none of them considers simple quality measures of collected data as a starting point. In this paper, we propose a Data Quality Tagging and Propagation Mechanism to compute the confidence level of a goal’s satisfaction level based on the quality of input data sources. The paper uses the Goal-oriented Requirement Language (GRL), part of the User Requirements Notation (URN) standard, in examples, with an implementation of the proposed mechanism and a case study conducted in order to demonstrate and assess the approach. The availability of computed confidence levels as an additional piece of information enables decision makers to (i) modulate the satisfaction information returned by goal models and (ii) make better-informed decisions, including looking for higher-quality data when needed.

An Improved User Requirements Notation (URN) Models’ Construction Approach

“…Those elements are linked to the developed softgoal or goal by means of GRL link elements. Each such link clearly specifies the nature and type of impact the intentional element will have on the satisfaction of goal being developed [31]. As in the case of softgoals, each goal is decomposed and operationalized until some concrete design decisions are obtained.…”

“…Thus, the necessity for this phase to explicitly proceed with the requirements elicitation during which proper inputs to GRL are identified, analyzed and categorized into, for instance, non-functional and functional requirements. Thereafter, the analysis of non-functional and functional requirements can be performed followed by softgoals and goals refinement and operationalization.The last step of the process is the GRL model evaluation, whereby, based on the chosen strategy (e.g., [31]), alternative solutions to softgoals and goals satisfaction are generated. Algorithm 1 summarizes the process and therefore constitutes the first step toward its formalization.…”