Daniel Kraus

Daniel Kraus, Jeremias Rößler, Martin Sulzmann

Ensuring the correct visual appearance of graphical user interfaces (GUIs) is important because visual bugs can cause substantial losses for businesses. An application might behave functionally correct in an automated test, but visual bugs can make the GUI effectively unusable for the user. Most of today's approaches for visual testing are pixel-based and tend to have flaws that are characteristic for image differencing. For instance, minor and unimportant visual changes often cause false positives, which confuse the user with unnecessary error reports. Our idea is to introduce an abstract GUI state (AGS), where we define structural relations to identify relevant GUI changes and ignore those that are unimportant from the user's point of view. In addition, we explore several strategies to address the GUI element identification problem in terms of AGS. This allows us to provide rich diagnostic information that help the user to better interpret changes. Based on the principles of golden master testing, we can support a fully-automated approach to visual testing by using the AGS. We have implemented our approach to visually test web pages and our experiments show that we are able to reliably detect GUI changes.

Varun Nayak, Daniel Kraus

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.

Daniel Kraus

ReTest is a novel testing tool for Java applications with a graphical user interface (GUI), combining monkey testing and difference testing. Since this combination sidesteps the oracle problem, it enables the generation of GUI-based regression tests. ReTest makes use of evolutionary computing (EC), particularly a genetic algorithm (GA), to optimize these tests towards code coverage. While this is indeed a desirable goal in terms of software testing and potentially finds many bugs, it lacks one major ingredient: human behavior. Consequently, human testers often find the results less reasonable and difficult to interpret. This thesis proposes a new approach to improve the initial population of the GA with the aid of machine learning (ML), forming an ML-technique enhanced-EC (MLEC) algorithm. In order to do so, existing tests are exploited to extract information on how human testers use the given GUI. The obtained data is then utilized to train an artificial neural network (ANN), which ranks the available GUI actions respectively their underlying GUI components at runtime---reducing the gap between manually created and automatically generated regression tests. Although the approach is implemented on top of ReTest, it can be easily used to guide any form of monkey testing. The results show that with only little training data, the ANN is able to reach an accuracy of 82% and the resulting tests represent an improvement without reducing the overall code coverage and performance significantly.