María Belén Rodríguez

arXiv2026

PICKLES: a Natural Language Framework for Requirement Specification and Model-Based Testing

This paper combines methods from the fields of Model-Based Testing (MBT) and Behaviour-Driven Development (BDD) to define a testing approach with human-readable specifications and test cases, as in BDD, while using the modelling techniques and automatic test generation algorithms from MBT. We introduce PICKLES, a Precise Input and Control-flow Keyword-based Language for tEst Scenarios; an extension of Gherkin-style BDD scenarios, specified in structured natural language. We provide a bi-directional translation from Pickles scenarios to formal models, where both specifications and tests are human-readable, and a method to obtain a so-called master model combining all translated scenarios. Standard MBT algorithms can then be applied to automatically derive test cases from it. We implement a prototype of the translation and composition steps, which we use on an industrial case study: a software component from a traffic management system. With it, we illustrate the pipeline and show how Pickles can achieve significantly higher coverage with respect to BDD from the same set of scenarios.

cs.SE↗

arXiv2025

Systematic Evaluation of Black-Box Checking for Fast Bug Detection

Combinations of active automata learning, model-based testing and model checking have been successfully used in numerous applications, e.g., for spotting bugs in implementations of major network protocols and to support refactoring of embedded controllers. However, in the large majority of these applications, model checking is only used at the very end, when no counterexample can be found anymore for the latest hypothesis model. This contrasts with the original proposal of black-box checking (BBC) by Peled, Vardi & Yannakakis, which applies model checking for all hypotheses, also the intermediate ones. In this article, we present the first systematic evaluation of the ability of BBC to find bugs quickly, based on 77 benchmarks models from real protocol implementations and controllers for which specifications of safety properties are available. Our main finding are: (a) In cases where the full model can be learned, BBC detects violations of the specifications with just 3.4% of the queries needed by an approach in which model checking is only used for the full model. (b) Even when the full model cannot be learned, BBC is still able to detect many violations of the specification. In particular, BBC manages to detect 94% of the safety properties violations in the challenging RERS 2019 industrial LTL benchmarks. (c) Our results also confirm that BBC is way more effective than existing MBT algorithms in finding deep bugs in implementations.

cs.SE↗