使用深度强化学习生成API测试数据

Steyn Huurman, Xiaoying Bai, Thomas Hirtz
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引用次数: 5

摘要

测试对于确保广泛使用的web api的质量至关重要。自动测试数据生成可以帮助降低成本并提高整体效率。这通常是通过使用强大的基于搜索的软件测试(SBST)概念来完成的。然而,随着web api变得越来越大,SBST技术面临着可扩展性的挑战。本文介绍了一种基于SBST的API测试数据生成方法,该方法采用深度强化学习(DRL)作为搜索算法。通过探索DRL在可扩展API测试数据生成环境中的优势,我们展示了它作为传统搜索算法替代方案的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generating API Test Data Using Deep Reinforcement Learning
Testing is critical to ensure the quality of widely-used web APIs. Automatic test data generation can help to reduce cost and improve overall effectiveness. This is commonly accomplished by using the powerful concept of search-based software testing (SBST). However, with web APIs growing larger and larger, SBST techniques face scalability challenges. This paper introduces a novel SBST based approach for generating API test data using deep reinforcement learning (DRL) as the search algorithm. By exploring the benefits of DRL in the context of scalable API test data generation, we show its potential as alternative to traditional search algorithms.
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