Test Agents: Adaptive, Autonomous and Intelligent Test Cases
Eduard Enoiu, Mirgita Frasheri

TL;DR
This paper proposes the concept of intelligent test agents that are adaptive, autonomous, and capable of reasoning to improve regression testing efficiency within continuous integration frameworks.
Contribution
It introduces the novel idea of using intelligent software agents as test cases to enable adaptive, autonomous, and decentralized testing processes.
Findings
Test agents can autonomously decide when to execute tests.
Test agents can adapt their testing objectives based on context.
The approach aims to enhance regression testing efficiency.
Abstract
Growth of software size, lack of resources to perform regression testing, and failure to detect bugs faster have seen increased reliance on continuous integration and test automation. Even with greater hardware and software resources dedicated to test automation, software testing is faced with enormous challenges, resulting in increased dependence on complex mechanisms for automated test case selection and prioritization as part of a continuous integration framework. These mechanisms are currently using simple entities called test cases that are concretely realized as executable scripts. Our key idea is to provide test cases with more reasoning, adaptive behavior and learning capabilities by using the concepts of intelligent software agents. We refer to such test cases as test agents. The model that underlie a test agent is capable of flexible and autonomous actions in order to meet…
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Taxonomy
TopicsSoftware Testing and Debugging Techniques · AI-based Problem Solving and Planning · Reinforcement Learning in Robotics
