181 citations · 933 across the 61 of their papers we have counts for
64 papers
Unknown Attack Detection in IoT Networks using Large Language Models: A Robust, Data-efficient Approach
Shan Ali, Feifei Niu, Paria Shirani +1
The rapid evolution of cyberattacks continues to drive the emergence of unknown (zero-day) threats, posing significant challenges for network intrusion detection systems in Interne…
Requirements Coverage-Guided Minimization for Natural Language Test Cases
Rongqi Pan, Feifei Niu, Lionel C. Briand +1
As software systems evolve, test suites tend to grow in size and often contain redundant test cases. Such redundancy increases testing effort, time, and cost. Test suite minimizati…
TVR: Automotive System Requirement Traceability Validation and Recovery Through Retrieval-Augmented Generation
Feifei Niu, Rongqi Pan, Lionel C. Briand +1
In automotive software development, as well as other domains, traceability between stakeholder requirements and system requirements is crucial to ensure consistency, correctness, a…
Learning-Guided Fuzzing for Testing Stateful SDN Controllers
Raphaël Ollando, Seung Yeob Shin, Lionel C. Briand
Controllers for software-defined networks (SDNs) are centralised software components that enable advanced network functionalities, such as dynamic traffic engineering and network v…
Learning-Based Relaxation of Completeness Requirements for Data Entry Forms
Hichem Belgacem, Xiaochen Li, Domenico Bianculli +1
Data entry forms use completeness requirements to specify the fields that are required or optional to fill for collecting necessary information from different types of users. Howev…
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.…
SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents
Amirhossein Zolfagharian, Manel Abdellatif, Lionel C. Briand +1
Deep Reinforcement Learning (DRL) has made significant advancements in various fields, such as autonomous driving, healthcare, and robotics, by enabling agents to learn optimal pol…
TEASMA: A Practical Methodology for Test Adequacy Assessment of Deep Neural Networks
Amin Abbasishahkoo, Mahboubeh Dadkhah, Lionel Briand +1
Successful deployment of Deep Neural Networks (DNNs) requires their validation with an adequate test set to ensure a sufficient degree of confidence in test outcomes. Although well…
A Comprehensive Study of Machine Learning Techniques for Log-Based Anomaly Detection
Shan Ali, Chaima Boufaied, Domenico Bianculli +2
Growth in system complexity increases the need for automated log analysis techniques, such as Log-based Anomaly Detection (LAD). While deep learning (DL) methods have been widely u…
Impact of Log Parsing on Deep Learning-Based Anomaly Detection
Zanis Ali Khan, Donghwan Shin, Domenico Bianculli +1
Software systems log massive amounts of data, recording important runtime information. Such logs are used, for example, for log-based anomaly detection, which aims to automatically…
Automated Smell Detection and Recommendation in Natural Language Requirements
Alvaro Veizaga, Seung Yeob Shin, Lionel C. Briand
Requirement specifications are typically written in natural language (NL) due to its usability across multiple domains and understandability by all stakeholders. However, unstructu…
LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models
Rongqi Pan, Taher A. Ghaleb, Lionel Briand
Test suites tend to grow when software evolves, making it often infeasible to execute all test cases with the allocated testing budgets, especially for large software systems. Test…
Systematic Evaluation of Deep Learning Models for Log-based Failure Prediction
Fatemeh Hadadi, Joshua H. Dawes, Donghwan Shin +2
With the increasing complexity and scope of software systems, their dependability is crucial. The analysis of log data recorded during system execution can enable engineers to auto…
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks
Zohreh Aghababaeyan, Manel Abdellatif, Mahboubeh Dadkhah +1
Deep neural networks (DNNs) are widely used in various application domains such as image processing, speech recognition, and natural language processing. However, testing DNN model…
Stress Testing Control Loops in Cyber-Physical Systems
Claudio Mandrioli, Seung Yeob Shin, Martina Maggio +2
Cyber-Physical Systems (CPSs) are often safety-critical and deployed in uncertain environments. Identifying scenarios where CPSs do not comply with requirements is fundamental but…
Probabilistic Safe WCET Estimation for Weakly Hard Real-Time Systems at Design Stages
Jaekwon Lee, Seung Yeob Shin, Lionel Briand +1
Weakly hard real-time systems can, to some degree, tolerate deadline misses, but their schedulability still needs to be analyzed to ensure their quality of service. Such analysis u…
Identifying the Hazard Boundary of ML-enabled Autonomous Systems Using Cooperative Co-Evolutionary Search
Sepehr Sharifi, Donghwan Shin, Lionel C. Briand +1
In Machine Learning (ML)-enabled autonomous systems (MLASs), it is essential to identify the hazard boundary of ML Components (MLCs) in the MLAS under analysis. Given that such bou…
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…
ATM: Black-box Test Case Minimization based on Test Code Similarity and Evolutionary Search
Rongqi Pan, Taher A. Ghaleb, Lionel Briand
Executing large test suites is time and resource consuming, sometimes impossible, and such test suites typically contain many redundant test cases. Hence, test case minimization is…
Learning Failure-Inducing Models for Testing Software-Defined Networks
Raphaël Ollando, Seung Yeob Shin, Lionel C. Briand
Software-defined networks (SDN) enable flexible and effective communication systems that are managed by centralized software controllers. However, such a controller can undermine t…
Many-Objective Reinforcement Learning for Online Testing of DNN-Enabled Systems
Fitash Ul Haq, Donghwan Shin, Lionel Briand
Deep Neural Networks (DNNs) have been widely used to perform real-world tasks in cyber-physical systems such as Autonomous Driving Systems (ADS). Ensuring the correct behavior of s…
NLP-based Automated Compliance Checking of Data Processing Agreements against GDPR
Orlando Amaral, Muhammad Ilyas Azeem, Sallam Abualhaija +1
Processing personal data is regulated in Europe by the General Data Protection Regulation (GDPR) through data processing agreements (DPAs). Checking the compliance of DPAs contribu…