ACTGAN: Automatic Configuration Tuning for Software Systems with Generative Adversarial Networks Complex software systems often provide a large number of parameters so that users can configure them for their specific application scenarios. However, configuration tuning requires a deep understanding of the software system, far beyond the abilities of typical system users. To address this issue, many existing approaches focus on exploring and learning good performance estimation models. The accuracy of such models often suffers when the number of available samples is small, a thorny challenge under a given tuningtime constraint. By contrast, we hypothesize that good configurations often share certain hidden structures. Therefore, instead of trying to improve the performance estimation of a given configuration, we focus on capturing the hidden structures of good configurations and utilizing such learned structure to generate potentially better configurations. We propose ACTGAN to achieve this goal. We have implemented and evaluated ACTGAN using 17 workloads with eight different software systems. Experimental results show that ACTGAN outperforms default configurations by 76.22% on average, and six stateoftheart configuration tuning algorithms by 6.58%64.56%. Furthermore, the ACTGANgenerated configurations are often better than those used in training and show certain features consisting with domain knowledge, both of which supports our hypothesis.

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.