Efficient State Identification for Finite State Machine-Based Testing

The practice of testing software systems modelled as Finite State Machines (FSMs) has garnered significant attention owing to its simplicity. In FSM-based testing, the tester derives a test suite from the FSM model representing the system’s specification. Subsequently, this test suite is executed against the implementation, and the tester uses the output to decide whether the implementation conforms to the specification. Often, a test suite generation technique requires input sequences to check whether the FSM is in the intended state. This task is referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">state identification</i> and is often carried out using a set of input sequences called a characterising set. Even though the use of characterising sets simplifies testing, they require a reliable reset or reset sequence and additional transfer sequences. Unfortunately, resetting the underlying system can be costly or may entail manual configuration. In addition, transfer sequences do not directly contribute to testing. This work introduces a class of characterising sets (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ordered Characterising Sets</i> (O-WSets)) that avoid using resets or transfers by design. We show that checking the existence of such a characterising set is NP-complete. We introduce the notion of bounded O-WSets (BO-WSets), which are types of O-WSets that limit transfer usage, and give an algorithm that constructs these. In experiments, on average, the proposed approach led to reductions in the number of resets (95% for real FSMs; 99.73% for synthetic FSMs), the number of transfer inputs (53% for real FSMs; 63.3% for synthetic FSMs) and the number of inputs in state identification sequences (50% for real FSMs; 66.6% for synthetic FSMs). Additionally, the proposed algorithm reduced the time and memory required to derive state identification sequences by 85% and 23%, respectively. Finally, the approach led to test suites with 49.3% fewer sequences and 33.3% fewer inputs on average.