Session-Based Recommender Systems for Action Selection in GUI Test Generation
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.
Sat 24 OctDisplayed time zone: Lisbon change
16:00 - 17:30 | Session: User-Interface Testing + ClosingNEXTA 2020 at D. Luis Chair(s): Kristian Wiklund Ericsson AB | ||
16:00 30mFull-paper | On the Industrial Applicability of Augmented Testing: An Empirical Study NEXTA 2020 Michel Nass Blekinge Institute of Technology, Emil Alégroth Blekinge Institute of Technology, Robert Feldt Chalmers University of Technology Link to publication DOI | ||
16:30 20mFull-paper | Session-Based Recommender Systems for Action Selection in GUI Test Generation NEXTA 2020 Link to publication DOI | ||
16:50 40mAwards | Award Ceremony -- Best Question Award; Best Presentation Award; Most influential tweet award NEXTA 2020 |