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, 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...
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| Vydáno v: | 2019 34th IEEE/ACM International Conference on Automated Software Engineering Workshop (ASEW) s. 86 - 89 |
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IEEE
01.11.2019
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Kehrer, Timo Basmer, Maike |
| Author_xml | – sequence: 1 givenname: Maike surname: Basmer fullname: Basmer, Maike organization: Humboldt-Universität zu Berlin – sequence: 2 givenname: Timo surname: Kehrer fullname: Kehrer, Timo organization: Humboldt-Universität zu Berlin |
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| Snippet | Nowadays software is often highly configurable, and the required adaptation is a complex and tedious task when performed manually. Moreover, hand-crafted... |
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| SubjectTerms | adaptability Adaptation models automated algorithm configuration Conferences Context modeling Encoding Machine learning algorithms Optimization SiDiff SMAC Software Software algorithms Software engineering Software engineering tools Tuning |
| Title | Encoding Adaptability of Software Engineering Tools as Algorithm Configuration Problem: A Case Study |
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