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The four papers listed above are self-contained. Therefore, some information is repeated. There are also some differences in the terminologies used in the papers. My contributions For all papers, I was responsible for the idea, implementation, experiments design, analysis, and writing. My supervisors contributed in all phases of the work. During my PhD study, I also contributed another paper that is not included in this thesis.

Many real-world product lines are only represented as nonhierarchical collections of distinct products, described by their configuration values. As the manual preparation of feature models is a tedious and labour-intensive activity, some techniques have been proposed to automatically generate boolean feature models from product descriptions. However, none of these techniques is capable of synthesizing feature attributes and relations among attributes, despite the huge relevance of attributes for documenting software product lines. In this paper, we introduce for the first time an algorithmic and parametrizable approach for computing a legal and appropriate hierarchy of features, including feature groups, typed feature attributes, domain values and relations among these attributes. We have performed an empirical evaluation by using both randomized configuration matrices and real-world examples. The initial results of our evaluation show that our approach can scale up to matrices containing 2,000 attributed features, and 200,000 distinct configurations in a couple of minutes. CCS Concepts •Software and its engineering → Software product lines; •Social and professional topics → Software reverse engineering;

Configuration in the domain of Integrated Control Systems (ICS) is largely manual, laborious, and error prone. In this article, we propose a model-based configuration approach that provides automation support for reducing configuration effort and the likelihood of configuration errors in the ICS domain. We ground our approach on component-based specifications of ICS families. We then develop a configuration algorithm using constraint satisfaction techniques over finite domains to generate products that are consistent with respect to their ICS family specifications. We reason about the termination and consistency of our configuration algorithm analytically. We evaluate the effectiveness of our configuration approach by applying it to a real subsea oil production system. Specifically, we have rebuilt a number of existing verified product configurations of our industry partner. Our experience shows that our approach can automatically infer up to 50% of the configuration decisions, and reduces the complexity of making configuration decisions.

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Past research has proposed association rule mining as a means to uncover the evolutionary coupling from a system's change history. These couplings have various applications, such as improving system decomposition and recommending related changes during development. The strength of the coupling can be characterized using a variety of interestingness measures. Existing recommendation engines typically use only the rule with the highest interestingness value in situations where more than one rule applies. In contrast, we argue that multiple applicable rules indicate increased evidence, and hypothesize that the aggregation of such rules can be exploited to provide more accurate recommendations.To investigate this hypothesis we conduct an empirical study on the change histories of two large industrial systems and four large open source systems. As aggregators we adopt three cumulative gain functions from information retrieval. The experiments evaluate the three using 39 different rule interestingness measures. The results show that aggregation provides a significant impact on most measure's value and, furthermore, leads to a significant improvement in the resulting recommendation.