Generation of test sequences, that is, (user) inputs -expected (system) outputs, is an important task of testing of graphical user interfaces (GUI). This work proposes an approach to randomly generate test sequences that might be used for comparison with existing GUI testing techniques to evaluate their efficiency. The proposed approach first models GUI under test by a finite state machine (FSM) and then converts it to a regular expression (RE). A tool based on a special technique we developed analyzes the RE to fulfill missing context information such as the position of a symbol in the RE. The result is a context table representing the RE. The proposed approach traverses the context table to generate the test sequences. To do this, the approach repeatedly selects a symbol in the table, starting from the initial symbol, in a random manner until reaching a special, finalizing symbol for constructing a test sequence. Thus, the approach uses a symbol coverage criterion to assess the adequacy of the test generation. To evaluate the approach, mutation testing is used. The proposed technique is to a great extent implemented and is available as a tool called PQ-Ran Test (PQ-analysis based Random Test Generation). A case study demonstrates the proposed approach and analyzes its effectiveness by mutation testing.

Model-based GUI testing has achieved widespread recognition in academy thanks to its advantages compared to code-based testing due to its potentials to automate testing and the ability to cover bigger parts more efficiently. In this study design paper, we address the scalability part of the model-based GUI testing by using community detection algorithms. A case study is presented as an example of possible improvements to make a model-based testing approach more efficient. We demonstrate layered ESG models as an example of our approach to consider the scalability problem. We present rough calculations with expected results, which show 9 times smaller time and space units for 100 events in the ESG model when a community detection algorithm is applied.

Traditionally, software testing is aimed at showing the presence of faults. This paper proposes a novel approach to testing graphical user interfaces (GUI) for showing both the presence and absence of faults in the sense of ideal testing. The approach uses a positive testing concept to show that the GUI under consideration (GUC) does what the user expects; to the contrary, the negative testing concept shows that the GUC does not do anything that the user does not expect, building a holistic view. The first step of the approach models the GUC by a finite state machine (FSM) that enables the model-based generation of test cases. This is always possible as the GUIs are considered as strictly sequential processes. The next step converts the FSM to an equivalent regular expression (RE) that will be analyzed first to construct test selection criteria for excluding redundant test cases and construct test coverage criteria for terminating the positive test process. Both criteria enable us to assess the adequacy and efficiency of the positive tests performed. The negative tests will be realized by systematically mutating the FSM to model faults, the absence of which are to be shown. Those mutant FSMs will be handled and assessed in the same way as in positive testing. Two case studies illustrate and validate the approach; the experiments' results will be analyzed to discuss the pros and cons of the techniques introduced.

Model-based GUI testing is an important concept in Software GUI testing. Manual testing is a time-consuming labor and heavily error-prone. It has several well-accepted models that Software Testing community has been working and contributing to them for many years. This paper reviews different models used in model-based GUI testing and presents a case study with a proposed approach for how to convert several well-accepted models to ESG (Event Sequence Graphs) to generate test cases and execute them with an aim to consolidate past and future works in a single model.