Information Quality Requirements Engineering: a Goal-based Modeling and Reasoning Approach

Information Quality (IQ) has been always a growing concern for most organizations, since they depend on information for managing their daily tasks, delivering their services to their costumers, making important decisions, etc., and relying on low-quality information may negatively influence their overall performance, or even disasters in the case of critical systems (e.g., air traffic management systems, healthcare systems, etc.). Although there exist several techniques for dealing with IQ related problems in the literature (e.g., checksum, integrity constraints, etc.), but most of them propose solutions that are able to address the technical aspects of IQ, and seem to be limited in addressing social and organizational aspects. In other words, these techniques do not satisfy the needs of current complex systems, such as socio-technical systems, where humans and their interactions are considered as an integral part of the system along with the technical elements (e.g., healthcare systems, smart cities, etc.). This introduces the need of analyzing the social and organizational context where the system will eventually operates, since IQ related problems might manifest themselves in the actors' interactions and dependencies. Moreover, considering IQ requirements since the early phase of the system development (the requirements phase) can prevent revising the system to accommodate such needs after the system deployment, which might be too costly. Despite this, most of the Requirements Engineering (RE) frameworks and approaches either loosely define, or simply ignore IQ requirements. To this end, we propose a goal-oriented framework for modeling and reasoning about IQ requirements since the early phases of the system development. The proposed framework consists of (i) a modeling language that provides concepts and constructs for modeling IQ requirements; (ii) a set of analysis techniques that support system designers while performing the required analysis to verify the correctness and consistency of the IQ requirements model; (iii) an engineering methodology to assist designers in using the framework for capturing IQ requirements; and (iv) an automated tool-support, namely ST-IQ Tool. In addition, we empirically evaluated the framework to demonstrate its applicability, usefulness, and the scalability of its reasoning techniques by successfully applying it to a case study concerning a stock market system.