论文标题
基于会话的推荐系统用于GUI测试中的行动选择系统
Session-Based Recommender Systems for Action Selection in GUI Test Generation
论文作者
论文摘要
在图形用户界面（GUI）级别的测试生成已被证明是揭示故障的有效方法。这样做时，测试生成器必须重复确定在测试系统的当前状态（SUT）的情况下要执行什么措施。这种行动选择问题通常涉及随机选择，通常称为猴子测试。一些方法利用其他技术来提高整体效率，但只有少数方法试图创建类似人类的动作 - 甚至整个动作序列。我们已经建立了一个基于会话的新型推荐系统，可以指导测试生成。这使我们能够模仿过去的用户行为，到达需要复杂互动的状态。我们提出了一项实证研究的初步结果，我们将Github用作SUT。这些结果表明，推荐系统似乎非常适合行动选择，并且该方法可以显着有助于改善基于GUI的测试生成。
Test generation at the graphical user interface (GUI) level has proven to be an effective method to reveal faults. When doing so, a test generator has to repeatably decide what action to execute given the current state of the system under test (SUT). This problem of action selection usually involves random choice, which is often referred to as monkey testing. Some approaches leverage other techniques to improve the overall effectiveness, but only a few try to create human-like actions---or even entire action sequences. We have built a novel session-based recommender system that can guide test generation. This allows us to mimic past user behavior, reaching states that require complex interactions. We present preliminary results from an empirical study, where we use GitHub as the SUT. These results show that recommender systems appear to be well-suited for action selection, and that the approach can significantly contribute to the improvement of GUI-based test generation.
