Personalized and automatic model repairing using reinforcement learning

被引：18

作者：

Barriga, Angela ^{[1
]}

Rutle, Adrian ^{[1
]}

Heldal, Rogardt ^{[1
]}

机构：

[1] Western Norway Univ Appl Sci, Dept Software Engn, Bergen, Norway

来源：

2019 ACM/IEEE 22ND INTERNATIONAL CONFERENCE ON MODEL DRIVEN ENGINEERING LANGUAGES AND SYSTEMS COMPANION (MODELS-C 2019) | 2019年

关键词：

Model Repair; Reinforcement Learning; Personalization;

D O I：

10.1109/MODELS-C.2019.00030

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

When performing modeling activities, the chances of breaking a model increase together with the size of development teams and number of changes in software specifications. Model repair research mostly proposes two different solutions to this issue: fully automatic, non-interactive model repairing tools or support systems where the repairing choice is left to the developer's criteria. In this paper, we propose the use of reinforcement learning algorithms to achieve the repair of broken models allowing both automation and personalization. We validate our proposal by repairing a large set of broken models randomly generated with a mutation tool.

引用

页码：175 / 181

页数：7

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