NaviQAte: Functionality-Guided Web Application Navigation

With over 781 billion website visits globally each month [51], their popularity highlights the growing need for developers to maintain high standards of quality and functionality. Traditional manual web testing approaches, however, can be time-consuming and challenging [8], leading to the increased adoption of automated testing methodologies to streamline the quality assurance process [5, 12, 13, 19, 24, 27, 30, 44, 48, 53, 56, 64]. Despite these advances, conventional testing tools may exhibit challenges and shortcomings regarding testing coverage, potentially overlooking critical bugs and usability issues [18, 19]. The discrepancy between tests generated by conventional methods and real user interactions further compounds these challenges [63], resulting in suboptimal testing outcomes. Web applications typically encompass a spectrum of actions, including form submissions, button clicks, and navigation through various pages. Automated testing tools for web applications encounter challenges stemming from the intricate and dynamic nature of modern web interfaces, which can feature diverse layouts, interactions, and non-deterministic states [3]. To address these challenges and mitigate the limitations of the traditional test generation methods, there has been a growing interest in leveraging deep learning (DL) [12, 13] and reinforcement learning (RL) [22, 23, 26, 27, 30, 31, 48, 64] techniques for automated testing in web applications. By assimilating insights from human testers' behavior, such automated testing approaches aim to emulate human-like interactions with web interfaces, thereby improving the comprehensiveness and effectiveness of testing. However, these DL and RL-based methodologies are not without their constraints.