Sensemaking Practices in the Everyday Work of AI/ML Software Engineering

This paper considers sensemaking as it relates to everyday software engineering (SE) work practices and draws on a multi-year ethnographic study of SE projects at a large, global technology company building digital services infused with artificial intelligence (AI) and machine learning (ML) capabilities. Our findings highlight the breadth of sensemaking practices in AI/ML projects, noting developers' efforts to make sense of AI/ML environments (e.g., algorithms/methods and libraries), of AI/ML model ecosystems (e.g., pre-trained models and "upstream"models), and of business-AI relations (e.g., how the AI/ML service relates to the domain context and business problem at hand). This paper builds on recent scholarship drawing attention to the integral role of sensemaking in everyday SE practices by empirically investigating how and in what ways AI/ML projects present software teams with emergent sensemaking requirements and opportunities

On Solving the Business Requirements Engineering Problems of Information Systems Development Projects – Lessons from Three Projects

Information systems development (ISD) often fails. Requirements engineering (RE) problems rank high in ISD project failure statistics. RE is often regarded as the link between business (processes) (BP) and IS. Thus, in RE, the BP and IS requirements need to be synchronized. We conducted three case studies to investigate RE problems and the reasons for them, especially to contemplate how to synchronize business process and IS development requirements in plan-driven (waterfall) and change-driven (agile) projects. Investigated cases indicate that the ontological and epistemological matching of IS and BP requirements engineering methods improves requirements quality