SLIMECRAFT: State Learning for Client-Server Regression Analysis and Fault Testing

In software engineering, behavioral state machine models play a crucial role in validating system behavior and maintaining correctness. This paper proposes an extension of an existing architecture for automatically learning state machine models of client-server systems that automates processes such as regression detection and test case generation, and guides the development of new features. The learned models help identify potential implementation issues of clients, servers, their interactions, as well as the protocols themselves. The architecture also enhances the debugging process and ensures comprehensive system coverage. By employing the LTSDiff algorithm, the method efficiently detects behavioral changes due to software updates, preventing unintended consequences on system performance. Consequently, the automatically generated state machine models can be used as evidence in security, safety, and reliability assurance, providing a valuable tool for development, testing, and maintenance of complex software systems. The learned state machines and detected changes correctly model the behavior of a client-server system to a specified depth at the level of an active outside adversary with the capability to read, replay, replace, or block any message.