arXiv subjects

Federico Formica

Publications and source records attributed to Federico Formica.

12 recordsLinked to original sources

arXiv2026

Automated Repair of Requirements for Cyber-Physical Systems in Simulink Requirements Tables

The development of complex software systems, e.g., cyber-physical systems (CPSs), involves continuous evolution of both system implementations and their requirements. These two artifacts often proceed independently, creating a risk of misalignment. For example, a system may be updated due to implementation-level concerns, yielding a new version that no longer satisfies its original requirements. Traditional compliance recovery techniques, e.g., automated program repair, address this problem by modifying the system while assuming that requirements are correct. However, faulty, outdated or inadequate requirements are a well-documented challenge in practice, motivating the complementary task of requirement repair. In this paper, we propose a framework that leverages system execution data to repair misaligned CPS requirements, thereby restoring requirement-to-system compliance. Our approach evaluates the correctness of declarative requirements over time-based, real-valued signals expressed using the MATLAB Simulink Requirements Tables language. We evaluate seven variants of our framework on six real-world case studies covering 12 requirements. Results confirm the effectiveness of the proposed framework in producing correct and useful repaired requirements.

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arXiv2026

Engineering Resource-constrained Software Systems with DNN Components: a Concept-based Pruning Approach

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 millions or billions of parameters, and require substantial computational power, RAM, and storage. This becomes a limitation in practical scenarios where strict size and resource constraints must be respected. In this paper, we present a novel concept-based pruning technique for DNNs that guides pruning decisions using human-interpretable concepts, such as features, colors, and classes. This is particularly important in a software engineering context, as DNNs are integrated into systems and must be pruned according to specific system requirements. Our concept-based pruning solution analyzes neuron activations to identify important neurons from a system requirements viewpoint and uses this information to guide the DNN pruning. We assess our solution using the VGG-19 network and a dataset of 26'384 RGB images, focusing on its ability to produce small, effective pruned DNNs and on the computational complexity and performance of these pruned DNNs. We also analyzed the pruning efficiency of our solution and compared alternative configurations. Our results show that concept-based pruning efficiently generates much smaller, effective pruned DNNs. Pruning greatly improves the computational efficiency and performance of DNNs, properties that are particularly useful for practical applications with stringent memory and computational time constraints. Finally, alternative configuration options enable engineers to identify trade-offs adapted to different practical situations.

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arXiv2026

Ensembles-based Feature Guided Analysis

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 internal behaviors, e.g., by providing explanations related to neurons activation. Results from the literature show that these rules have considerable precision (i.e., they correctly predict certain classes of features), but the recall (i.e., the number of situations these rule apply) is more limited. To mitigate this problem, this paper presents Ensembles-based Feature Guided Analysis (EFGA). EFGA combines rules extracted by FGA into ensembles. Ensembles aggregate different rules to increase their applicability depending on an aggregation criterion, a policy that dictates how to combine rules into ensembles. Although our solution is extensible, and different aggregation criteria can be developed by users, in this work, we considered three different aggregation criteria. We evaluated how the choice of the criterion influences the effectiveness of EFGA on two benchmarks (i.e., the MNIST and LSC datasets), and found that different aggregation criteria offer alternative trade-offs between precision and recall. We then compare EFGA with FGA. For this experiment, we selected an aggregation criterion that provides a reasonable trade-off between precision and recall. Our results show that EFGA has higher train recall (+28.51% on MNIST, +33.15% on LSC), and test recall (+25.76% on MNIST, +30.81% on LSC) than FGA, with a negligible reduction on the test precision (-0.89% on MNIST, -0.69% on LSC).

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arXiv2025

Search-based Software Testing Driven by Domain Knowledge: Reflections and New Perspectives

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 number of test cases in a limited time. However, SBST does not possess the domain knowledge of engineers. Several techniques have been proposed to integrate engineers' domain knowledge within existing SBST frameworks. This paper will reflect on recent experimental results by highlighting bold and unexpected results. It will help re-examine SBST techniques driven by domain knowledge from a new perspective, suggesting new directions for future research.

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arXiv2025

Feature-Guided Analysis of Neural Networks: A Replication Study

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 relevant to their tasks. Existing feature-guided approaches typically monitor the activation of the neural network neurons to extract the relevant rules. Preliminary results are encouraging and demonstrate the feasibility of this solution by assessing the precision and recall of Feature-Guided Analysis on two pilot case studies. However, the applicability in industrial contexts needs additional empirical evidence. To mitigate this need, this paper assesses the applicability of FGA on a benchmark made by the MNIST and LSC datasets. We assessed the effectiveness of FGA in computing rules that explain the behavior of the neural network. Our results show that FGA has a higher precision on our benchmark than the results from the literature. We also evaluated how the selection of the neural network architecture, training, and feature selection affect the effectiveness of FGA. Our results show that the selection significantly affects the recall of FGA, while it has a negligible impact on its precision.

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arXiv2025

Proceedings of the First International Workshop on Autonomous Systems Quality Assurance and Prediction with Digital Twins

This volume contains the proceedings of the First International Workshop on Autonomous Systems Quality Assurance and Prediction with Digital Twins (ASQAP 2025), which was held in Hamilton, Canada, on May 4th, 2025, as a satellite event of ETAPS 2025. The aim of ASQAP 2025 is to gather experts from academia and industry to explore the potential of digital twin technology in supporting quality assurance in autonomous systems, including concepts such as specification, verification, validation, testing, analysis, and many others.

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arXiv2025

Search-based Testing of Simulink Models with Requirements Tables

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 SBST techniques for Simulink models rely on requirements being expressed in logical languages, limiting their adoption in industry. To help with the adoption, SBST methods and tools for Simulink models need to be integrated with tools used by engineers to specify requirements. This work presents the first black-box testing approach for Simulink models that supports Requirements Table (RT), a tool from Simulink Requirements Toolbox used by practitioners to express software requirements. We evaluated our solution by considering 60 model-RT combinations each made by a model and an RT. Our SBST framework returned a failure-revealing test case for 70% of the model-RT combinations. Remarkably, it identified a failure-revealing test case for three model-RT combinations for a cruise controller of an industrial simulator that other previously used tools were not able to find. The efficiency of our SBST solution is acceptable for practical applications and comparable with existing SBST tools that are not based on RT.

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arXiv2024

Search-based Trace Diagnostic

Cyber-physical systems (CPS) development requires verifying whether system behaviors violate their requirements. This analysis often considers system behaviors expressed by execution traces and requirements expressed by signal-based temporal properties. When an execution trace violates a requirement, engineers need to solve the trace diagnostic problem: They need to understand the cause of the breach. Automated trace diagnostic techniques aim to support engineers in the trace diagnostic activity. This paper proposes search-based trace-diagnostic (SBTD), a novel trace-diagnostic technique for CPS requirements. Unlike existing techniques, SBTD relies on evolutionary search. SBTD starts from a set of candidate diagnoses, applies an evolutionary algorithm iteratively to generate new candidate diagnoses (via mutation, recombination, and selection), and uses a fitness function to determine the qualities of these solutions. Then, a diagnostic generator step is performed to explain the cause of the trace violation. We implemented Diagnosis, an SBTD tool for signal-based temporal logic requirements expressed using the Hybrid Logic of Signals (HLS). We evaluated Diagnosis by performing 34 experiments for 17 trace-requirements combinations leading to a property violation and by assessing the effectiveness of SBTD in producing informative diagnoses and its efficiency in generating them on a time basis. Our results confirm that Diagnosis can produce informative diagnoses in practical time for most of our experiments (33 out of 34).

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arXiv2023

Test Case Generation for Drivability Requirements of an Automotive Cruise Controller: An Experience with an Industrial Simulator

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. Our experience concerns 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.

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arXiv2023

Reflections on Surrogate-Assisted Search-Based Testing: A Taxonomy and Two Replication Studies based on Industrial ADAS and Simulink Models

Surrogate-assisted search-based testing (SA-SBT) aims to reduce the computational time for testing compute-intensive systems. Surrogates enhance testing techniques by improving test case generation focusing the testing budget on the most critical portions of the input domain. In addition, they can serve as approximations of the system under test (SUT) to predict tests' results instead of executing the tests on compute-intensive SUTs. This article reflects on the existing SA-SBT techniques, particularly those applied to system-level testing and often facilitated using simulators or complex test beds. Our objective is to synthesize different heuristic algorithms and evaluation methods employed in existing SA-SBT techniques and present a comprehensive view of SA-SBT solutions. In addition, by critically reviewing our previous work on SA-SBT, we aim to identify the limitations in our proposed algorithms and evaluation methods and to propose potential improvements. We present a taxonomy that categorizes and contrasts existing SA-SBT solutions and highlights key research gaps. To identify the evaluation challenges, we conduct two replication studies of our past SA-SBT solutions: One study uses industrial advanced driver assistance system (ADAS) and the other relies on a Simulink model benchmark. We compare our results with those of the original studies and identify the difficulties in evaluating SA-SBT techniques, including the impact of different contextual factors on results generalization and the validity of our evaluation metrics. Based on our taxonomy and replication studies, we propose future research directions, including re-considerations in the current evaluation metrics used for SA-SBT solutions, utilizing surrogates for fault localization and repair in addition to testing, and creating frameworks for large-scale experiments by applying SA-SBT to multiple SUTs and simulators.

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arXiv2022

Simulation-based Testing of Simulink Models with Test Sequence and Test Assessment Blocks

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 presents HECATE, a testing approach for Simulink models using Test Sequence and Test Assessment blocks from Simulink Test. Unlike existing testing techniques, HECATE uses information from Simulink models to guide the search-based exploration. Specifically, HECATE relies on information provided by the Test Sequence and Test Assessment blocks to guide the search procedure. Across a benchmark of 16 Simulink models from different domains and industries, our comparison of HECATE with the state-of-the-art testing tool S-TALIRO indicates that HECATE is both more effective (more failure-revealing test cases) and efficient (less iterations and computational time) than S-TALIRO for ~94% and ~81% of benchmark models respectively. Furthermore, HECATE successfully generated a failure-revealing test case for a representative case study from the automotive domain demonstrating its practical usefulness.

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arXiv2022

Search-based Software Testing Driven by Automatically Generated and Manually Defined Fitness Functions

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.

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