International audience

Numerous industrial sectors employ Programmable Logic Controllers (PLC) software to control safety-critical systems. These systems necessitate extensive testing and stringent coverage measurements, which can be facilitated by automated test-generation techniques. Existing such techniques have not been applied to PLC programs, and therefore do not directly support the latter regarding automated test-case generation. To address this deficit, in this work, we introduce PyLC, a tool designed to automate the conversion of PLC programs to Python code, assisted by an existing test generator called Pynguin. Our framework is capable of handling PLC programs written in the Function Block Diagram language. To demonstrate its capabilities, we employ PyLC to transform safety-critical programs from industry and illustrate how our approach can facilitate the manual and automatic creation of tests. Our study highlights the efficacy of leveraging Python as an intermediary language to bridge the gap between PLC development tools, Python-based unit testing, and automated test generation.

We elaborate on a metamorphic approach for testing industrial control systems. The proposed approach consists of two phases: an exploration phase in which we learn about fault patterns of the system under test and an exploitation phase where the observed fault patterns are used for targeted testing. Our method extracts metamorphic relations and input space of the system from its requirements. The seed input used for testing is extracted from the execution logs of the system and used to generate source tests and follow-up tests automatically. The morphed input is constructed based on the seed input and refined using a set of constraints. The approach is exemplified on a position control system and the results show that it is effective in discovering faults with an increased level of automation.