Combining goal-oriented and use case modeling has been shown as an effective method of requirements engineering. To ensure the quality of such modeled artifacts, a conceptual foundation is needed to govern the process of determining what types of artifacts to be modeled, and how they should be specified and analyzed for 3Cs problems (completeness, consistency and correctness). However, such a foundation is missing in current goal-use case integration approaches. In this paper, we present GUIMeta, a meta-model, to address this problem. GUIMeta consists of three layers. The artifact layer defines the semantics and classification of artifacts and their relationships. The specification layer offers specification rules for each artifact class. The ontology layer allows semantics to be integrated into the entire model. Our promising evaluation shows the suitability of GUIMeta in modeling goals and use cases.

Goal and use case modeling has been recognized as a key approach for understanding and analyzing requirements. However, in practice, goals and use cases are often buried among other content in requirements specifications documents and written in unstructured styles. It is thus a time-consuming and error-prone process to identify such goals and use cases. In addition, having them embedded in natural language documents greatly limits the possibility of formally analyzing the requirements for problems. To address these issues, we have developed a novel rule-based approach to automatically extract goal and use case models from natural language requirements documents. Our approach is able to automatically categorize goals and ensure they are properly specified. We also provide automated semantic parameterization of artifact textual specifications to promote further analysis on the extracted goal-use case models. Our approach achieves 85% precision and 82% recall rates on average for model extraction and 88% accuracy for the automated parameterization.

Combining goal-oriented and use case modeling has been proven to be an effective method in requirements elicitation and elaboration. However, current requirements engineering approaches generally lack reliable support for automated analysis of such modeled artifacts. To address this problem, we have developed GUITAR, a tool which delivers automated detection of incorrectness, incompleteness and inconsistency between artifacts. GUITAR is based on our goal-use case integration meta-model and ontologies of domain knowledge and semantics. GUITAR also provides comprehensive explanations for detected problems and can suggest resolution alternatives.

Requirements engineering (RE) is a coordinated effort to allow clients, users, and software engineers to jointly formulate assumptions, constraints, and goals about a software solution. However, one of the most challenging aspects of RE is the detection of inconsistencies between requirements. To address this issue, we have developed REInDetector, a knowledge-based requirements engineering tool, supporting automatic detection of a range of inconsistencies. It provides facilities to elicit, structure, and manage requirements with distinguished capabilities for capturing the domain knowledge and the semantics of requirements. This permits an automatic analysis of both consistency and realizability of requirements. REInDetector finds implicit consequences of explicit requirements and offers all stakeholders an additional means to identify problems in a more timely fashion than existing RE tools. In this paper, we describe the Description Logic used to capture requirements, the REInDetector tool, its support for inconsistency detection, and its efficacy as applied to several RE examples. An important feature of REInDetector is also its ability to generate comprehensive explanations to provide more insights into the detected inconsistencies.