Reusing Models For Requirements Engineering
Tim Menzies, Ying Hu · 2001
A problem with model-based requirements engineering is that new projects may lack the data required to customize old models. Such data droughts are a common problem in software engineering and are particularly acute in early life cycle activities such as requirements engineering. When specific data relevant to a new project is missing, one technique is to simulate a model across the range of possibilities that might be relevant to a project. This generates voluminous output which can be summarized via a new machine learning technique called treatment learning.