Detecting user-visible failures in AJAX web applications by analyzing users' interaction behaviors

Web applications can suffer from poor reliability, and AJAX technology makes Web sites even more error-prone. Failures of a Web application, particularly user-visible failures, impact users' satisfaction and may drive users away from using the Web site. Conventional testing techniques are inadequate for improving AJAX applications' reliability, and application providers commonly rely on fast failure detection, which is challenging. In this paper, we present a novel technique for automatically detecting user-visible failures in AJAX applications. Our technique trains a Bayesian model to analyze users' interaction behaviors to infer whether such user responses are related to user-visible failures. We implemented our technique in a tool called SIRANA. We performed a case study using a commercial AJAX application with seeded bugs, and collected users' interaction data during 14 one-hour sessions. We evaluated our technique using SIRANA applied to the collected data. The results demonstrate the effectiveness of our technique: It not only detected all seeded bugs, but also detected four real, previously-unknown bugs.