Automated UI Testing for Large-Scale Web Applications

Description :

In complex modern software solutions, UI correctness testing had long been recognized to pose significant challenges. Even nowadays, UI tests still rely on manual efforts while automated tests are far from achieving human-like performance. UI testing is expected to deal with dynamic content update and deep bugs hiding under complicated user interactions and specific input values, which can only be triggered by certain action sequences in the huge search space. This leads to UI tests that suffer from flakiness and false negatives due to random failures, incurring a significant waste of developer time and reducing some experiences of the art of programming [1] to fastidious manual labor. Traditionally, research has focused on Model-Based UI testing to provide solutions for such problems [2]. In recent years, the field of Artificial Intelligence (AI) has been revolutionized by a wide variety of novel methods and applications [3]. This has recently been reflected by contributions to UI testing based on reinforcement learning [4]. In this thesis, we aim to provide solutions to the challenges of UI testing by exploring both Model-Based and AI-Based automated testing methods. In this regard, an interesting approach consists of generating a graph-based model from a given UI and using AI-based traversal methods to synthesize tests from this model. The applications of such novel techniques to the UI of large industrial applications such as Murex’s MX also poses practical challenges that are expected to be tackled by this doctoral thesis. 

 The application of AI-based methods to solve the Web testing automation problem is a significant novelty compared to previous work. The fact that this research work is expected to be put into production on a large-scale Web Application such as Murex’s Mx is definitely expected to stand out from current state-of-the-art (SOTA) techniques References: [1] Knuth, D., 2011. The Art of Programming. ITNow, 53(4). [2] Biagiola, M., Stocco, A., Ricca, F. and Tonella, P., 2019, August. Diversity-based web test generation. In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (pp. 142-153). [3] Russell, S. J. & Norvig, P. (2020), Artificial Intelligence: a modern approach, 4 edn, Pearson. [4] Zheng, Y., Liu, Y., Xie, X., Liu, Y., Ma, L., Hao, J. and Liu, Y., 2021, May. Automatic web testing using curiosity-driven reinforcement learning. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) (pp. 423-435). IEEE.