2019年IEEE第27届国际需求工程会议 (RE 2019)

The 27th IEEE International Requirements Engineering Conference (RE'19) is the premier international forum for researchers, practitioners, educators, and students to present and discuss the most recent innovations, experiences, and concerns in the discipline of requirements engineering.

The RE'19 theme isRE and Collective Intelligence in the Days of AI.RE research has a unique opportunity to lead a paradigm shift that places human and society at the forefront of the design of AI systems. Research in RE has never been more needed in AI. For example, in helping address ethical considerations in the design, use, and misuse of intelligent systems; or, in enabling the multi-disciplinary, collaborative efforts that are key to exploring and understanding the problem on a greater scale than individual decision making.

Application of RE in the days of AI can be twofold. First, RE techniques are important to explore the complex problem space so that we design an intelligent machine that understands the nature of real-world problems and behaves accordingly. Secondly, developers need to fully understand and comprehend what people (or in a large scale ‘society’) expect from AI and articulate these expectations/values to the machine. A thorough understanding of the requirements can help negotiate the values of various stakeholders affected by AI system. Our attention shifts to asking “how do we collaborate among various groups of people with different perspectives and expectations so that we end up designing the right system with the right set of behaviors” as opposed to “how do we make a system intelligent with the latest technical achievements”. By incorporating an end-to-end collaboration of RE and Collective Intelligence in AI technologies, engineers can design a system that understands the complexity of human and environments.

Requirements elicitation, prioritization, and negotiation

Design thinking and open innovation

Innovation through creativity

Crowdsourcing and social media

Social, cultural, and cognitive factors

User feedback and usage monitoring

Capturing and understanding users’ needs

Natural language approaches

Model-driven approaches

Formal approaches

Traceability

Evolution and release planning

Reuse

Tools and standards

Good-enough Requirements Engineering

Agile and lean approaches

Requirements engineering in Open Source

Complex systems

Product lines and value chains

Software ecosystems, artificial intelligence, big data, and cloud technologies

Requirements Engineering for Smart Cities, Cyber-Physical Systems, and Systems of Systems