35 citations · 129 across the 28 of their papers we have counts for
29 papers · 1 filter
Can LLMs Solve Science or Just Write Code? Evaluating Quantum Solver Generation
Luciano Baresi, Domenico Bianculli, Maryse Ernzer +3
Large Language Models (LLMs) show strong capabilities in code generation, motivating their use in automated quantum solver development. However, in quantum computing, successful ex…
Randomized and Diverse Input State Generation for Quantum Program Testing
Maryse Ernzer, Seung Yeob Shin, Fabrizio Pastore +1
With the accelerating development of quantum technologies and their growing computational potential, quantum systems are being adapted for simulations and other critical tasks acro…
Beyond Rules: LLM-Powered Linting for Quantum Programs
Pietro Cassieri, Giuseppe Scanniello, Seung Yeob Shin +2
As quantum computing transitions from theoretical experimentation to its practical application, the reliability of quantum software has become a critical bottleneck. Traditional st…
Quantum Program Linting with LLMs: Emerging Results from a Comparative Study
Seung Yeob Shin, Fabrizio Pastore, Domenico Bianculli
Ensuring the quality of quantum programs is increasingly important; however, traditional static analysis techniques are insufficient due to the unique characteristics of quantum co…
GAN-enhanced Simulation-driven DNN Testing in Absence of Ground Truth
Mohammed Attaoui, Fabrizio Pastore
The generation of synthetic inputs via simulators driven by search algorithms is essential for cost-effective testing of Deep Neural Network (DNN) components for safety-critical sy…
Fuzzing-based Mutation Testing of C/C++ Software in Cyber-Physical Systems
Jaekwon Lee, Fabrizio Pastore, Lionel Briand
Mutation testing can help minimize the delivery of faulty software. Therefore, it is a recommended practice for developing embedded software in safety-critical cyber-physical syste…
Field-based Security Testing of SDN configuration Updates
Jahanzaib Malik, Fabrizio Pastore
Software-defined systems revolutionized the management of hardware devices but introduced quality assurance challenges that remain to be tackled. For example, software defined netw…
MOTIF: A tool for Mutation Testing with Fuzzing
Jaekwon Lee, Enrico Viganò, Fabrizio Pastore +1
Mutation testing consists of generating test cases that detect faults injected into software (generating mutants) which its original test suite could not. By running such an augmen…
Search-based DNN Testing and Retraining with GAN-enhanced Simulations
Mohammed Oualid Attaoui, Fabrizio Pastore, Lionel Briand
In safety-critical systems (e.g., autonomous vehicles and robots), Deep Neural Networks (DNNs) are becoming a key component for computer vision tasks, particularly semantic segment…
Towards Generating Executable Metamorphic Relations Using Large Language Models
Seung Yeob Shin, Fabrizio Pastore, Domenico Bianculli +1
Metamorphic testing (MT) has proven to be a successful solution to automating testing and addressing the oracle problem. However, it entails manually deriving metamorphic relations…
Fuzzing for CPS Mutation Testing
Jaekwon Lee, Enrico Viganò, Oscar Cornejo +2
Mutation testing can help reduce the risks of releasing faulty software. For such reason, it is a desired practice for the development of embedded software running in safety-critic…
Testing Updated Apps by Adapting Learned Models
Chanh-Duc Ngo, Fabrizio Pastore, Lionel Briand
Although App updates are frequent and software engineers would like to verify updated features only, automated testing techniques verify entire Apps and are thus wasting resources.…
Supporting Safety Analysis of Image-processing DNNs through Clustering-based Approaches
Mohammed Oualid Attaoui, Hazem Fahmy, Fabrizio Pastore +1
The adoption of deep neural networks (DNNs) in safety-critical contexts is often prevented by the lack of effective means to explain their results, especially when they are erroneo…
HUDD: A tool to debug DNNs for safety analysis
Hazem Fahmy, Fabrizio Pastore, Lionel Briand
We present HUDD, a tool that supports safety analysis practices for systems enabled by Deep Neural Networks (DNNs) by automatically identifying the root causes for DNN errors and r…
Metamorphic Testing for Web System Security
Nazanin Bayati Chaleshtari, Fabrizio Pastore, Arda Goknil +1
Security testing aims at verifying that the software meets its security properties. In modern Web systems, however, this often entails the verification of the outputs generated whe…
Simulator-based explanation and debugging of hazard-triggering events in DNN-based safety-critical systems
Hazem Fahmy, Fabrizio Pastore, Lionel Briand +1
When Deep Neural Networks (DNNs) are used in safety-critical systems, engineers should determine the safety risks associated with failures (i.e., erroneous outputs) observed during…
Data-driven Mutation Analysis for Cyber-Physical Systems
Enrico Viganò, Oscar Cornejo, Fabrizio Pastore +1
Cyber-physical systems (CPSs) typically consist of a wide set of integrated, heterogeneous components; consequently, most of their critical failures relate to the interoperability…
Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering
Mohammed Oualid Attaoui, Hazem Fahmy, Fabrizio Pastore +1
Deep neural networks (DNNs) have demonstrated superior performance over classical machine learning to support many features in safety-critical systems. Although DNNs are now widely…
Mutation Analysis for Cyber-Physical Systems: Scalable Solutions and Results in the Space Domain
Oscar Cornejo, Fabrizio Pastore, Lionel Briand
On-board embedded software developed for spaceflight systems (space software) must adhere to stringent software quality assurance procedures. For example, verification and validati…
Automated, Cost-effective, and Update-driven App Testing
Chanh Duc Ngo, Fabrizio Pastore, Lionel Briand
Apps' pervasive role in our society led to the definition of test automation approaches to ensure their dependability. However, state-of-the-art approaches tend to generate large n…
Supporting DNN Safety Analysis and Retraining through Heatmap-based Unsupervised Learning
Hazem Fahmy, Fabrizio Pastore, Mojtaba Bagherzadeh +1
Deep neural networks (DNNs) are increasingly important in safety-critical systems, for example in their perception layer to analyze images. Unfortunately, there is a lack of method…
Metamorphic Security Testing for Web Systems
Phu X. Mai, Fabrizio Pastore, Arda Goknil +1
Security testing verifies that the data and the resources of software systems are protected from attackers. Unfortunately, it suffers from the oracle problem, which refers to the c…
Automatic Generation of Acceptance Test Cases from Use Case Specifications: an NLP-based Approach
Chunhui Wang, Fabrizio Pastore, Arda Goknil +1
Acceptance testing is a validation activity performed to ensure the conformance of software systems with respect to their functional requirements. In safety critical systems, it pl…
Automating System Test Case Classification and Prioritization for Use Case-Driven Testing in Product Lines
Ines Hajri, Arda Goknil, Fabrizio Pastore +1
Product Line Engineering (PLE) is a crucial practice in many software development environments where software systems are complex and developed for multiple customers with varying…