Abstract
Software systems have grown larger and more complex in recent years. Generative software development strives to automate software development from a systems family by generating implementations using domain-specific languages. In current practice, specifying domain-specific languages is a manual task requiring expert analysis of multiple information sources. Furthermore, the concepts and relations represented in a language are grown through its usage. Keeping the language consistent with its usage is a time-consuming process requiring manual comparison between the language instances and its language specification. Feature model mining addresses these issues by synthesizing a representative model bottom-up from a sample set of instances called configurations.
This thesis presents a mining algorithm that reverse-engineers a probabilistic feature model from a set of individual configurations. A configuration consists of a list of features that are defined as system properties that a stakeholder is interested in. Probabilistic expressions are retrieved from the sample configurations through the use of conjunctive and disjunctive association rule mining. These expressions are used to construct a probabilistic feature model.
