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Knowledge Representation and Reasoning for Requirements Engineering

by Alexander Borgida and John Mylopoulos

Requirements Engineering (RE), like all engineering disciplines, is founded on models and reasoning techniques with respect to such models. Artificial Intelligence (AI) offers rich theories of models and reasoning techniques through its core research area known as “Knowledge Representation and Reasoning” (aka KRR). We review some instances where concepts from KRR were adopted for RE models and modelling languages, going back to RML (1982) one of the first formal requirements modelling languages. Our talk covers more than 30 years of RE research on models and reasoning with such models.

The theme of this third edition of AIRE introduces the objective of discussing how AI techniques can improve Quality in RE — one of the grand challenges we identified in AIRE'15. Papers must explicitly address the following points at least in the discussion/conclusion section:

• the authors’ understanding of RE quality, as well as the quality criteria they wish to improve,
• the automation technique they apply and how this improves the aforementioned criteria, and
• how the approach is applied/can be applied in practice, or, if the paper discusses no applicable approach, how industry can benefit from the created knowledge.

The purpose of this workshop is to explore synergies between Artificial Intelligence and Requirements Engineering in order to identify complex RE problems that could benefit from the application of AI techniques. It aims to strengthen the links between the community, foster the communication between industry and academia, as well as between researchers working in this field. It also aims to build foundations for a common terminology and understanding of the interplay between AI and RE. We welcome submissions in the intersection between RE and AI, as long as they address the three points mentioned above. The topics of interest include but are not limited to:

• RE quality models and their automation
• Natural language processing and comprehension
• Machine learning techniques including supervised, unsuper- vised, and machine-human interactions
• Reasoning about uncertainties and ambiguities
• Reasoning techniques
• Knowledge acquisition and representation
• Agent based solutions
• Problem solving and decision making support mechanisms • Optimization techniques
• Automated approaches for prioritization

AIRE'16 is co-located with the 24th IEEE International Requirements Engineering Conference (RE 2016) in Beijing, China.