6 papers
Engineering Resource-constrained Software Systems with DNN Components: a Concept-based Pruning Approach
Federico Formica, Andrea Rota, Aurora Francesca Zanenga +4
Deep Neural Networks (DNNs) are widely used by engineers to solve difficult problems that require predictive modeling from data. However, these models are often massive, with milli…
Ensembles-based Feature Guided Analysis
Federico Formica, Stefano Gregis, Andrea Rota +3
Recent Deep Neural Networks (DNN) applications ask for techniques that can explain their behavior. Existing solutions, such as Feature Guided Analysis (FGA), extract rules on their…
Search-based Software Testing Driven by Domain Knowledge: Reflections and New Perspectives
Federico Formica, Mark Lawford, Claudio Menghi
Search-based Software Testing (SBST) can automatically generate test cases to search for requirements violations. Unlike manual test case development, it can generate a substantial…
Feature-Guided Analysis of Neural Networks: A Replication Study
Federico Formica, Stefano Gregis, Aurora Francesca Zanenga +3
Understanding why neural networks make certain decisions is pivotal for their use in safety-critical applications. Feature-Guided Analysis (FGA) extracts slices of neural networks…
Engineering Automotive Digital Twins on Standardized Architectures: A Case Study
Stefan Ramdhan, Winnie Trandinh, Istvan David +2
Digital twin (DT) technology has become of interest in the automotive industry. There is a growing need for smarter services that utilize the unique capabilities of DTs, ranging fr…
Search-based Testing of Simulink Models with Requirements Tables
Federico Formica, Chris George, Shayda Rahmatyan +4
Search-based software testing (SBST) of Simulink models helps find scenarios that demonstrate that the system can reach a state that violates one of its requirements. However, many…