181 citations · 933 across the 61 of their papers we have counts for
15 papers · 1 filter
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…