The rise of mobile, social, cloud, and crowd-sourcing applications requires Requirements Engineering (RE) to adapt. The traditional methods of RE are very inefficient in situations involving thousands to millions of current and potential users of a (software) product. The crowd is an interesting source for RE because it produces user feedback in texts and usage data. Being able to respond quickly, effectively, and iteratively to the requirements, problems, wishes, and needs identified in user feedback can increase a product’s success. Crowd-Based RE (CrowdRE) seeks to provide RE with suitable means for this crowd paradigm.
The Eighth Workshop on Crowd-Based Requirements Engineering (CrowdRE'24) focuses on Exploiting Large Language Models and Generative AI in CrowdRE, and on Broadening the Reach and Perception of CrowdRE.
Title: Requirements Engineering and Machine Learning: Intentionality and the Crowd
Abstract: Crowd-based requirements engineering has a history of gathering requirements from current or future users of software, typically some level of automated analysis. In parallel, the rise of machine learning usage and capabilities has forced us to rethink how we can conduct requirements engineering for systems with machine learning, and how advanced machine learning can be used to help with requirements tasks. This keynote will explore the intersection of requirements engineering and machine learning with crowd-based engineering. Does big data emulate the crowd? Must the crowd be "intentional", in that they know they are providing feedback for a particular application? We aim to consider both crowd-based requirements engineering for systems with machine learning and using machine learning as "the crowd" for possible elicitation of requirements.
Bio: Jennifer Horkoff is an Associate Professor at the Interaction Design and Software Engineering division in the Computer Science and Engineering Department shared by Chalmers University of Technology and the University of Gothenburg, Sweden. Dr. Horkoff is currently involved in projects investigating requirements engineering for machine learning, supported by the Swedish Research Council and Vinnova. She has been an author or co-author of more than 100 papers in peer-reviewed journals, conferences, or workshops. Jennifer has been a co-program chair of RE, REFSQ, ER and PoEM, and has served on program committees and organizing committees of several international conferences (e.g., ICSE, RE, ER, MODELS, CAiSE), and has been a (co-) organizer of several international workshops.
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