C
Cu D. Nguyen
Researcher at University of Luxembourg
Publications - 37
Citations - 955
Cu D. Nguyen is an academic researcher from University of Luxembourg. The author has contributed to research in topics: Test case & Web service. The author has an hindex of 19, co-authored 36 publications receiving 828 citations. Previous affiliations of Cu D. Nguyen include Center for Information Technology & fondazione bruno kessler.
Papers
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Proceedings ArticleDOI
Automated testing for SQL injection vulnerabilities: an input mutation approach
TL;DR: An automated testing approach, namely μ4SQLi, and its underpinning set of mutation operators, are presented and it is demonstrated that the approach is effective to detect SQL injection vulnerabilities and to produce inputs that bypass application firewalls, which is a common configuration in real world.
Proceedings ArticleDOI
Combining model-based and combinatorial testing for effective test case generation
TL;DR: This work proposes a novel approach that combines model-based and combinatorial testing in order to generate executable and effective test cases from a model, and introduces a post-optimization algorithm that can guarantee the combinatorsial criterion of choice on the whole set of test paths extracted from the model.
Journal ArticleDOI
Evolutionary testing of autonomous software agents
TL;DR: This paper proposes a methodology to derive objective (fitness) functions that drive evolutionary algorithms, and evaluates the overall approach with two simulated autonomous agents, showing that the approach is effective in finding good test cases automatically.
Proceedings ArticleDOI
Test Case Prioritization for Audit Testing of Evolving Web Services Using Information Retrieval Techniques
TL;DR: This paper presents a novel approach to the prioritization of audit test cases using information retrieval that matches a service change description with the code portions exercised by the relevant test cases.
Journal ArticleDOI
A Machine-Learning-Driven Evolutionary Approach for Testing Web Application Firewalls
TL;DR: ML-Driven, an approach based on machine learning and an evolutionary algorithm to automatically detect holes in WAFs that let SQL injection attacks bypass them, is presented.