SYSMODIS: a systematic model discovery approach

Official URL: http://dx.doi.org/10.1109/ICSTW52544.2021.00023

In this paper, we present an automated model discovery approach, called SYSMODIS, which uses covering arrays to systematically sample the input spaces. SYSMODIS discovers finite state machine-based models, where states represent distinct screens and the edges between the states represent the transitions between the screens. SYSMODIS also discovers the likely guard conditions for the transitions, i.e., the conditions that must be satisfied before the transitions can be taken. For the first time a previously unseen screen is visited, a covering array-based test suite for the input fields present on the screen as well as the actions that can be taken on the screen, is created. SYSMODIS keeps on crawling until all the test suites for all the screens have been exhaustively tested. Once the crawling is over, the results of the test suites are fed to a machine learning algorithm on a per screen basis to determine the likely guard conditions. In the experiments we carried out to evaluate the proposed approach, we observed that SYSMODIS profoundly improved the state/screen coverage, transition coverage, and/or the accuracy of the predicted guard conditions, compared to the existing approaches studied in the paper.