Demo2Test: Transfer Testing of Agent in Competitive Environment with Failure Demonstrations

The competitive game between agents exists in many critical applications, such as military unmanned aerial vehicles. It is urgent to test these agents to reduce the significant losses caused by their failures. Existing studies mainly are to construct a testing agent that competes with the target agent to induce its failures. These approaches usually focus on a single task, requiring much more time for multi-task testing. However, if the previously tested tasks (source tasks) and the task to be tested (target task) share similar agents or task objectives, the transferable knowledge in source tasks can potentially increase the effectiveness of testing in the target task. We propose Demo2Test for conducting transfer testing of agents in the competitive environment, i.e., leveraging the demonstrations of failure scenarios from the source task to boost the testing effectiveness in the target task. It trains a testing agent with demonstrations and incorporates the action perturbation at key states to balance the number of revealed failures and their diversity. We conduct experiments in the simulated robotics competitive environments of MuJoCo. The results indicate that Demo2Test outperforms the best-performing baseline with improvements ranging from \(22.38\%\) to \(87.98\%\) , and \(12.69\%\) to \(60.98\%\) , in terms of the number and diversity of discovered failure scenarios, respectively.

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Publication Details

Journal
ACM Transactions on Software Engineering and Methodology
Published
2024-09-13
DOI
https://doi.org/10.1145/3696001
Citations
1
Primary Topic
Software System Performance and Reliability
Type
article
Field-Weighted Citation Impact
0.35
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article

Demo2Test: Transfer Testing of Agent in Competitive Environment with Failure Demonstrations

Xiaofei Xie, Qing Wang, Fanjiang Xu, Chen Jian-ming et al.
1 citations
ACM Transactions on Software Engineering and Methodology
Software System Performance and Reliability
0.35
article

Demo2Test: Transfer Testing of Agent in Competitive Environment with Failure Demonstrations

Xiaofei Xie, Qing Wang, Fanjiang Xu, Chen Jian-ming, Dandan Wang, Junjie Wang, Yawen Wang
article en
1 citations

Abstract

The competitive game between agents exists in many critical applications, such as military unmanned aerial vehicles. It is urgent to test these agents to reduce the significant losses caused by their failures. Existing studies mainly are to construct a testing agent that competes with the target agent to induce its failures. These approaches usually focus on a single task, requiring much more time for multi-task testing. However, if the previously tested tasks (source tasks) and the task to be tested (target task) share similar agents or task objectives, the transferable knowledge in source tasks can potentially increase the effectiveness of testing in the target task. We propose Demo2Test for conducting transfer testing of agents in the competitive environment, i.e., leveraging the demonstrations of failure scenarios from the source task to boost the testing effectiveness in the target task. It trains a testing agent with demonstrations and incorporates the action perturbation at key states to balance the number of revealed failures and their diversity. We conduct experiments in the simulated robotics competitive environments of MuJoCo. The results indicate that Demo2Test outperforms the best-performing baseline with improvements ranging from \(22.38\%\) to \(87.98\%\) , and \(12.69\%\) to \(60.98\%\) , in terms of the number and diversity of discovered failure scenarios, respectively.

ACM Transactions on Software Engineering and MethodologyVol. 34(2)
Chinese Academy of Sciences (CN), Institute of Software (CN), Singapore Management University (SG)
Openalex Percentile: Top 39%
Software System Performance and Reliability
0.35
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Demo2Test: Transfer Testing of Agent in Competitive Environment with Failure Demonstrations — Xiaofei Xie, Qing Wang, et al. · ACM Transactions on Software Engineering and Methodology (2024) | TGRS Research Map | TGRS