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20192026
most citedTest Case Selection and Prioritization Using Machine Learning: A Systematic Literature Review

181 citations · 933 across the 61 of their papers we have counts for

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Showing cs.SEShow all

55 papers · 1 filter

cs.SE2025

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…

cs.SE2025

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…

cs.SE2024

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…

cs.SE2023

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…

cs.SE2023

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…

cs.SE2023

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.…

cs.SE2023★ 4 cited

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…

cs.SE2023★ 15 cited

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…

cs.SE2023★ 43 cited

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…

cs.SE2023★ 2 cited

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…

cs.SE2023★ 12 cited

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…

cs.SE2023★ 17 cited

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…

cs.SE2023★ 8 cited

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…

cs.SE2023★ 1 cited

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…

cs.SE2023

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…

cs.SE2023

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…

cs.SE2022★ 3 cited

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…

cs.SE2022★ 1 cited

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…

cs.SE2022★ 1 cited

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…

cs.SE2022★ 7 cited

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…

cs.SE2022★ 2 cited

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…

cs.SE2022

COREQQA -- A COmpliance REQuirements Understanding using Question Answering Tool

Sallam Abualhaija, Chetan Arora, Lionel Briand

We introduce COREQQA, a tool for assisting requirements engineers in acquiring a better understanding of compliance requirements by means of automated Question Answering. Extractin…

cs.SE2022★ 55 cited

A Search-Based Testing Approach for Deep Reinforcement Learning Agents

Amirhossein Zolfagharian, Manel Abdellatif, Lionel Briand +2

Deep Reinforcement Learning (DRL) algorithms have been increasingly employed during the last decade to solve various decision-making problems such as autonomous driving and robotic…

cs.SE2022★ 9 cited

Trace Diagnostics for Signal-based Temporal Properties

Chaima Boufaied, Claudio Menghi, Domenico Bianculli +1

Most of the trace-checking tools only yield a Boolean verdict. However, when a property is violated by a trace, engineers usually inspect the trace to understand the cause of the v…