What should your runtime configuration framework do to help developers? The users or deployment engineers of a software system can adapt such a system to a wide range of deployment and usage scenarios by changing the value of configuration options, for example by disabling unnecessary features, tweaking performancerelated parameters or specifying GUI preferences. However, the literature agrees that the flexibility of such options comes at a price: misconfigured options can lead a software system to crash in the production environment, while even in the absence of such configuration errors, a large number of configuration options makes a software system more complicated to deploy and use. In earlier work, we also found that developers who intend to make their application configurable face 22 challenges that impact their configuration engineering activities, ranging from technical to managementrelated or even inherent to the domain of configuration engineering. In this paper, we use a prototyping approach to derive and empirically evaluate requirements for tool support able to deal with 13 (primarily technical) configuration engineering challenges. In particular, via a set of interviews with domain experts, we identify four requirements by soliciting feedback on an incrementally evolving prototype. The resulting xe2x80x9cConfig2Codexe2x80x9d prototype, which implements the four requirements, is then empirically evaluated via a user study involving 55 participants that comprises 10 typical configuration engineering tasks, ranging from the creation, comprehension, refactoring, and reviewing of configuration options to the quality assurance of options and debugging of configuration failures. A configuration framework satisfying the four requirements enables developers to perform more accurately and more swiftly in 70% and 60% (respectively) of the configuration engineering tasks than a stateofthepractice framework not satisfying the requirements. Furthermore, such a framework allows to reduce the time taken for these tasks by up to 94.62%, being slower for only one task.

Encoding Adaptability of Software Engineering Tools as Algorithm Configuration Problem: A Case Study Nowadays software is often highly configurable, and the required adaptation is a complex and tedious task when performed manually. Moreover, handcrafted configurations are often far from optimal. In this paper, we study the software configuration problem in the context of the model comparison tool SiDiff, which needs to be carefully adapted to domainspecific modeling languages used in modeldriven engineering. To tackle the configuration challenge, we propose to draw from the field of automated algorithm configuration, a research area which has studied the optimization of parameterizable algorithms for many years and which has gained particular momentum through its applications to hyperparameter tuning in machine learning. Specifically, we report on ongoing work encoding the adaptability of SiDiff as an algorithm configuration problem which is amenable to a sequential modelbased optimization tool known as SMAC. While empirical evaluation results are left for future work, the main goal of this paper is to foster active discussions at the workshop and to collect early feedback on our ongoing research.