Testing without Scripts: An Approach to Smart GUI Exploration

Software testing through graphical user interfaces (GUIs) remains a critical challenge in quality assurance, particularly as software systems grow in complexity and evolve rapidly. Traditional script-based testing approaches, which rely on predefined test cases, are widely used in industry but often struggle with high maintenance costs, limited adaptability to GUI changes, and restricted coverage of unforeseen user behaviours. Scriptless GUI testing has emerged as a powerful alternative, dynamically exploring applications without the need for predefined test scripts. This approach introduces randomness, allowing the execution of unexpected sequences of actions and the discovery of faults that scripted tests often miss. This thesis investigates the effectiveness of scriptless testing, examining how its exploratory nature complements existing testing practices and reduces manual efforts. To establish a strong foundation, this research first analyses thirty years of GUI testing literature, tracing the evolution of the field. The findings reveal a growing transition from manual and script-based testing to scriptless approaches. With this motivation, this thesis investigates the effectiveness of scriptless GUI testing through the lens of testar, an open-source tool that serves as this study’s primary research vehicle. A generalisation study of the tool allowed the introduction of an architectural analogy for scriptless testing deployment, built upon the existing industrial case studies with testar. This thesis examines the role of state models in guiding scriptless testing by evaluating how different levels of state abstraction can influence model inference and test coverage. The results provide guidelines for balancing model complexity with exploration effectiveness. Additionally, this thesis explores the impact of reinforcement learning-driven reward mechanisms in balancing pure randomness with targeted exploration to enhance test effectiveness. The thesis further evaluates the industrial applicability of scriptless testing through empirical studies in collaboration with companies participating in the European IVVES project (Industrial-grade Verification and Validation of Evolving Systems). The research aims to bridge the gap between traditional testing ade quacy criteria and quality-oriented metrics by investigating whether code smell could serve as a complementary adequacy criterion when evaluating scriptless testing effectiveness. Findings reveal that while increasing traditional code cov erage leads to broader exploration, it does not necessarily translate into covering code with deeper structural or maintainability issues. This research extends testar’s scriptless testing into the mobile domain, adapt ing it for mobile platforms and introducing MINTestar, a specialised Android test ing tool. Developed as part of the industry collaboration within the IVVES project, these efforts explore the feasibility of scriptless testing in real-world mobile envi ronments, and the integration of mobile-specific oracles and probabilistic explo ration strategies. The results highlight the adaptability of scriptless approaches across platforms and their potential for adoption in industrial mobile testing work flows. This thesis integrates insights from literature reviews, empirical evaluations, and industrial case studies to provide both theoretical and practical contributions to the field of scriptless GUI testing. By improving state models, leveraging reward-based exploration, refining test adequacy metrics, and extending automation to mobile platforms, this thesis lays the foundation for future advancements in smart testing, domain-specific oracles, and distributed testing architectures.