Encoding Adaptability of Software Engineering Tools as Algorithm Configuration Problem: A Case Study
Encoding Adaptability of Software Engineering Tools as Algorithm Configuration Problem: A Case Study
复制标题
软件工程工具的编码适应性作为算法配置问题：案例研究
DOI：
10.1109/asew.2019.00035
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Timo Kehrer
中科院分区：
文献类型：
--
作者：
Maike Basmer;Timo Kehrer
Nowadays software is often highly configurable, and the required adaptation is a complex and tedious task when performed manually. Moreover, hand-crafted 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 domain-specific modeling languages used in model-driven 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 hyper-parameter 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 model-based 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.
