Search-based software testing (SBST) typically relies on fitness functions to guide the search exploration toward software failures. There are two main techniques to define fitness functions: (a) automated fitness function computation from the specification of the system requirements, and (b) manual fitness function design. Both techniques have advantages. The former uses information from the system requirements to guide the search toward portions of the input domain more likely to contain failures. The latter uses the engineers’ domain knowledge. We propose ATheNA , a novel SBST framework that combines fitness functions automatically generated from requirements specifications and those manually defined by engineers. We design and implement ATheNA-S , an instance of ATheNA that targets Simulink ® models. We evaluate ATheNA-S by considering a large set of models from different domains. Our results show that ATheNA-S generates more failure-revealing test cases than existing baseline tools and that the difference between the runtime performance of ATheNA-S and the baseline tools is not statistically significant. We also assess whether ATheNA-S could generate failure-revealing test cases when applied to two representative case studies: one from the automotive domain and one from the medical domain. Our results show that ATheNA-S successfully revealed a requirement violation in our case studies.

Simulation-based software testing supports engineers in finding faults in Simulink®models. It typically relies on search algorithms that iteratively generate test inputs used to exercise models in simulation to detect design errors. While simulation-based software testing techniques are effective in many practical scenarios, they are typically not fully integrated within the Simulink environment and require additional manual effort. Many techniques require engineers to specify requirements using logical languages that are neither intuitive nor fully supported by Simulink, thereby limiting their adoption in industry. This work presentsHECATE, a testing approach for Simulink models using Test Sequence and Test Assessment blocks from Simulink®Test™. Unlike existing testing techniques,HECATEuses information from Simulink models to guide the search-based exploration. Specifically,HECATErelies on information provided by the Test Sequence and Test Assessment blocks to guide the search procedure. Across a benchmark of$18$Simulink models from different domains and industries, our comparison ofHECATEwith the state-of-the-art testing toolS-Taliroindicates thatHECATEis both more effective (more failure-revealing test cases) and efficient (less iterations and computational time) thanS-Talirofor$\approx$94% and$\approx$83% of benchmark models respectively. Furthermore,HECATEsuccessfully generated a failure-revealing test case for a representative case study from the automotive domain demonstrating its practical usefulness.

Automotive software development requires engineers to test their systems to detect violations of both functional and drivability requirements. Functional requirements define the functionality of the automotive software. Drivability requirements refer to the driver's perception of the interactions with the vehicle; for example, they typically require limiting the acceleration and jerk perceived by the driver within given thresholds. While functional requirements are extensively considered by the research literature, drivability requirements garner less attention. This industrial paper describes our experience assessing the usefulness of an automated search-based software testing (SBST) framework in generating failure-revealing test cases for functional and drivability requirements. We report on our experience with the VI-CarRealTime simulator, an industrial virtual modeling and simulation environment widely used in the automotive domain. We designed a Cruise Control system in Simulink for a four-wheel vehicle, in an iterative fashion, by producing 21 model versions. We used the SBST framework for each version of the model to search for failure-revealing test cases revealing requirement violations. Our results show that the SBST framework successfully identified a failure-revealing test case for 66.7% of our model versions, requiring, on average, 245.9s and 3.8 iterations. We present lessons learned, reflect on the generality of our results, and discuss how our results improve the state of practice.