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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 2022Show all

13 papers · 1 filter

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.LG2022

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…

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…

cs.SE2022★ 15 cited

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…

cs.SE2022★ 16 cited

A Machine Learning Approach for Automated Filling of Categorical Fields in Data Entry Forms

Hichem Belgacem, Xiaochen Li, Domenico Bianculli +1

Users frequently interact with software systems through data entry forms. However, form filling is time-consuming and error-prone. Although several techniques have been proposed to…

cs.SE2022★ 1 cited

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…

cs.SE2022★ 25 cited

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…