References of "Cordy, Maxime 50027892"
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See detailA Formal Framework of Software Product Line Analyses
Castro, Thiago; Teixeira, Leopoldo; Alves, Vander et al

in ACM Transactions on Software Engineering and Methodology (in press)

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See detailEvasion Attack STeganography: Turning Vulnerability Of Machine Learning ToAdversarial Attacks Into A Real-world Application
Ghamizi, Salah UL; Cordy, Maxime UL; Papadakis, Mike UL et al

in Proceedings of International Conference on Computer Vision 2021 (2021)

Evasion Attacks have been commonly seen as a weakness of Deep Neural Networks. In this paper, we flip the paradigm and envision this vulnerability as a useful application. We propose EAST, a new ... [more ▼]

Evasion Attacks have been commonly seen as a weakness of Deep Neural Networks. In this paper, we flip the paradigm and envision this vulnerability as a useful application. We propose EAST, a new steganography and watermarking technique based on multi-label targeted evasion attacks. Our results confirm that our embedding is elusive; it not only passes unnoticed by humans, steganalysis methods, and machine-learning detectors. In addition, our embedding is resilient to soft and aggressive image tampering (87% recovery rate under jpeg compression). EAST outperforms existing deep-learning-based steganography approaches with images that are 70% denser and 73% more robust and supports multiple datasets and architectures. [less ▲]

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See detailRequirements And Threat Models of Adversarial Attacks and Robustness of Chest X-ray classification
Ghamizi, Salah UL; Cordy, Maxime UL; Papadakis, Mike UL et al

E-print/Working paper (2021)

Vulnerability to adversarial attacks is a well-known weakness of Deep Neural Networks. While most of the studies focus on natural images with standardized benchmarks like ImageNet and CIFAR, little ... [more ▼]

Vulnerability to adversarial attacks is a well-known weakness of Deep Neural Networks. While most of the studies focus on natural images with standardized benchmarks like ImageNet and CIFAR, little research has considered real world applications, in particular in the medical domain. Our research shows that, contrary to previous claims, robustness of chest x-ray classification is much harder to evaluate and leads to very different assessments based on the dataset, the architecture and robustness metric. We argue that previous studies did not take into account the peculiarity of medical diagnosis, like the co-occurrence of diseases, the disagreement of labellers (domain experts), the threat model of the attacks and the risk implications for each successful attack. In this paper, we discuss the methodological foundations, review the pitfalls and best practices, and suggest new methodological considerations for evaluating the robustness of chest xray classification models. Our evaluation on 3 datasets, 7 models, and 18 diseases is the largest evaluation of robustness of chest x-ray classification models. We believe our findings will provide reliable guidelines for realistic evaluation and improvement of the robustness of machine learning models for medical diagnosis. [less ▲]

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See detailA Replication Study on the Usability of Code Vocabulary in Predicting Flaky Tests
Haben, Guillaume UL; Habchi, Sarra UL; Papadakis, Mike UL et al

in 18th International Conference on Mining Software Repositories (2021, May)

Abstract—Industrial reports indicate that flaky tests are one of the primary concerns of software testing mainly due to the false signals they provide. To deal with this issue, researchers have developed ... [more ▼]

Abstract—Industrial reports indicate that flaky tests are one of the primary concerns of software testing mainly due to the false signals they provide. To deal with this issue, researchers have developed tools and techniques aiming at (automatically) identifying flaky tests with encouraging results. However, to reach industrial adoption and practice, these techniques need to be replicated and evaluated extensively on multiple datasets, occasions and settings. In view of this, we perform a replication study of a recently proposed method that predicts flaky tests based on their vocabulary. We thus replicate the original study on three different dimensions. First we replicate the approach on the same subjects as in the original study but using a different evaluation methodology, i.e., we adopt a time-sensitive selection of training and test sets to better reflect the envisioned use case. Second, we consolidate the findings of the initial study by building a new dataset of 837 flaky tests from 9 projects in a different programming language, i.e., Python while the original study was in Java, which comforts the generalisability of the results. Third, we propose an extension to the original approach by experimenting with different features extracted from the Code Under Test. Our results demonstrate that a more robust validation has a consistent negative impact on the reported results of the original study, but, fortunately, these do not invalidate the key conclusions of the study. We also find re-assuring results that the vocabulary-based models can also be used to predict test flakiness in Python and that the information lying in the Code Under Test has a limited impact in the performance of the vocabulary-based models [less ▲]

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See detailTowards Exploring the Limitations of Active Learning: An Empirical Study
Hu, Qiang UL; Guo, Yuejun UL; Cordy, Maxime UL et al

in The 36th IEEE/ACM International Conference on Automated Software Engineering. (2021)

Deep neural networks (DNNs) are being increasingly deployed as integral parts of software systems. However, due to the complex interconnections among hidden layers and massive hyperparameters, DNNs ... [more ▼]

Deep neural networks (DNNs) are being increasingly deployed as integral parts of software systems. However, due to the complex interconnections among hidden layers and massive hyperparameters, DNNs require being trained using a large number of labeled inputs, which calls for extensive human effort for collecting and labeling data. Spontaneously, to alleviate this growing demand, a surge of state-of-the-art studies comes up with different metrics to select a small yet informative dataset for the model training. These research works have demonstrated that DNN models can achieve competitive performance using a carefully selected small set of data. However, the literature lacks proper investigation of the limitations of data selection metrics, which is crucial to apply them in practice. In this paper, we fill this gap and conduct an extensive empirical study to explore the limits of selection metrics. Our study involves 15 selection metrics evaluated over 5 datasets (2 image classification tasks and 3 text classification tasks), 10 DNN architectures, and 20 labeling budgets (ratio of training data being labeled). Our findings reveal that, while selection metrics are usually effective in producing accurate models, they may induce a loss of model robustness (against adversarial examples) and resilience to compression. Overall, we demonstrate the existence of a trade-off between labeling effort and different model qualities. This paves the way for future research in devising selection metrics considering multiple quality criteria. [less ▲]

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See detailAdversarial Robustness in Multi-Task Learning: Promises and Illusions
Ghamizi, Salah UL; Cordy, Maxime UL; Papadakis, Mike UL et al

E-print/Working paper (2021)

Vulnerability to adversarial attacks is a well-known weakness of Deep Neural networks. While most of the studies focus on single-task neural networks with computer vision datasets, very little research ... [more ▼]

Vulnerability to adversarial attacks is a well-known weakness of Deep Neural networks. While most of the studies focus on single-task neural networks with computer vision datasets, very little research has considered complex multi-task models that are common in real applications. In this paper, we evaluate the design choices that impact the robustness of multi-task deep learning networks. We provide evidence that blindly adding auxiliary tasks, or weighing the tasks provides a false sense of robustness. Thereby, we tone down the claim made by previous research and study the different factors which may affect robustness. In particular, we show that the choice of the task to incorporate in the loss function are important factors that can be leveraged to yield more robust models. [less ▲]

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See detailTest Selection for Deep Learning Systems
Ma, Wei UL; Papadakis, Mike UL; Tsakmalis, Anestis et al

in ACM Transactions on Software Engineering and Methodology (2021), 30(2), 131--1322

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See detailKilling Stubborn Mutants with Symbolic Execution
Titcheu Chekam, Thierry UL; Papadakis, Mike UL; Cordy, Maxime UL et al

in ACM Transactions on Software Engineering and Methodology (2021), 30(2), 191--1923

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See detailLoad approximation for uncertain topologies in the low-voltage grid
Mouline, Ludovic UL; Cordy, Maxime UL; Le Traon, Yves UL

in INTERNATIONAL CONFERENCE ON SMART GRID COMMUNICATIONS, 11-13 November 2020 (2020, November)

Smart grids allow operators to monitor the grid continuously, detect occurring incidents, and trigger corrective actions. To perform that, they require a deep understanding of the effective situation ... [more ▼]

Smart grids allow operators to monitor the grid continuously, detect occurring incidents, and trigger corrective actions. To perform that, they require a deep understanding of the effective situation within the grid. However, some parameters of the grid may not be known with absolute confidence. Reasoning over the grid despite uncertainty needs the consideration of all possible states. In this paper, we propose an approach to enumerate only valid potential grid states. Thereby, we allow discarding invalid assumptions that poison the results of a given computation procedure. We validate our approach based on a real-world topology from the power grid in Luxembourg. We show that the estimation of cable load is negatively affected by invalid fuse state combinations, in terms of computation time and accuracy. [less ▲]

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See detailTackling the equivalent mutant problem in real-time systems: the 12 commandments of model-based mutation testing
Basile, Davide; Ter Beek, Maurice; Cordy, Maxime UL et al

in SOFTWARE PRODUCT LINE CONFERENCE (2020, October)

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See detailData-driven simulation and optimization for covid-19 exit strategies
Ghamizi, Salah UL; Rwemalika, Renaud UL; Cordy, Maxime UL et al

in Ghamizi, Salah; Rwemalika, Renaud; Cordy, Maxime (Eds.) et al Data-driven simulation and optimization for covid-19 exit strategies (2020, August)

The rapid spread of the Coronavirus SARS-2 is a major challenge that led almost all governments worldwide to take drastic measures to respond to the tragedy. Chief among those measures is the massive ... [more ▼]

The rapid spread of the Coronavirus SARS-2 is a major challenge that led almost all governments worldwide to take drastic measures to respond to the tragedy. Chief among those measures is the massive lockdown of entire countries and cities, which beyond its global economic impact has created some deep social and psychological tensions within populations. While the adopted mitigation measures (including the lockdown) have generally proven useful, policymakers are now facing a critical question: how and when to lift the mitigation measures? A carefully-planned exit strategy is indeed necessary to recover from the pandemic without risking a new outbreak. Classically, exit strategies rely on mathematical modeling to predict the effect of public health interventions. Such models are unfortunately known to be sensitive to some key parameters, which are usually set based on rules-of-thumb.In this paper, we propose to augment epidemiological forecasting with actual data-driven models that will learn to fine-tune predictions for different contexts (e.g., per country). We have therefore built a pandemic simulation and forecasting toolkit that combines a deep learning estimation of the epidemiological parameters of the disease in order to predict the cases and deaths, and a genetic algorithm component searching for optimal trade-offs/policies between constraints and objectives set by decision-makers.Replaying pandemic evolution in various countries, we experimentally show that our approach yields predictions with much lower error rates than pure epidemiological models in 75% of the cases and achieves a 95% R² score when the learning is transferred and tested on unseen countries. When used for forecasting, this approach provides actionable insights into the impact of individual measures and strategies. [less ▲]

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See detailStatistical Model Checking for Variability-Intensive Systems
Cordy, Maxime UL; Papadakis, Mike UL; Legay, Axel

in FUNDAMENTAL APPROACHES TO SOFTWARE ENGINEERING, Dublin 22-25 April 2020 (2020, April)

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See detailPreventing Overloading Incidents on Smart Grids: A Multiobjective Combinatorial Optimization Approach
Antoniadis, Nikolaos UL; Cordy, Maxime UL; Sifaleras, Angelo et al

in Communications in Computer and Information Science (2020, February 15)

Cable overloading is one of the most critical disturbances that may occur in smart grids, as it can cause damage to the distribution power lines. Therefore, the circuits are protected by fuses so that ... [more ▼]

Cable overloading is one of the most critical disturbances that may occur in smart grids, as it can cause damage to the distribution power lines. Therefore, the circuits are protected by fuses so that, the overload could trip the fuse, opening the circuit, and stopping the flow and heating. However, sustained overloads, even if they are below the safety limits, could also damage the wires. To prevent overload, smart grid operators can switch the fuses on or off to protect the circuits, or remotely curtail the over-producing/over-consuming users. Nevertheless, making the most appropriate decision is a daunting decision-making task, notably due to contractual and technical obligations. In this paper, we define and formulate the overloading prevention problem as a Multiobjective Mixed Integer Quadratically Constrained Program. We also suggest a solution method using a combinatorial optimization approach with a state-of-the-art exact solver. We evaluate this approach for this real-world problem together with Creos Luxembourg S.A., the leading grid operator in Luxembourg, and show that our method can suggest optimal countermeasures to operators facing potential overloading incidents. [less ▲]

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See detailAdversarial Embedding: A robust and elusive Steganography and Watermarking technique
Ghamizi, Salah UL; Cordy, Maxime UL; Papadakis, Mike UL et al

Scientific Conference (2020)

We propose adversarial embedding, a new steganography and watermarking technique that embeds secret information within images. The key idea of our method is to use deep neural networks for image ... [more ▼]

We propose adversarial embedding, a new steganography and watermarking technique that embeds secret information within images. The key idea of our method is to use deep neural networks for image classification and adversarial attacks to embed secret information within images. Thus, we use the attacks to embed an encoding of the message within images and the related deep neural network outputs to extract it. The key properties of adversarial attacks (invisible perturbations, nontransferability, resilience to tampering) offer guarantees regarding the confidentiality and the integrity of the hidden messages. We empirically evaluate adversarial embedding using more than 100 models and 1,000 messages. Our results confirm that our embedding passes unnoticed by both humans and steganalysis methods, while at the same time impedes illicit retrieval of the message (less than 13% recovery rate when the interceptor has some knowledge about our model), and is resilient to soft and (to some extent) aggressive image tampering (up to 100% recovery rate under jpeg compression). We further develop our method by proposing a new type of adversarial attack which improves the embedding density (amount of hidden information) of our method to up to 10 bits per pixel. [less ▲]

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See detailSearch-based adversarial testing and improvement of constrained credit scoring systems
Ghamizi, Salah UL; Cordy, Maxime UL; Gubri, Martin UL et al

in ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE '20), November 8-13, 2020 (2020)

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See detailFeatureNET: Diversity-driven Generation of Deep Learning Models
Ghamizi, Salah UL; Cordy, Maxime UL; Papadakis, Mike UL et al

in International Conference on Software Engineering (ICSE) (2020)

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See detailPandemic Simulation and Forecasting of exit strategies:Convergence of Machine Learning and EpidemiologicalModels
Ghamizi, Salah UL; Rwemalika, Renaud UL; Cordy, Maxime UL et al

Report (2020)

The COVID-19 pandemic has created a public health emergency unprecedented in this century. The lack ofaccurate knowledge regarding the outcomes of the virus has made it challenging for policymakers to ... [more ▼]

The COVID-19 pandemic has created a public health emergency unprecedented in this century. The lack ofaccurate knowledge regarding the outcomes of the virus has made it challenging for policymakers to decideon appropriate countermeasures to mitigate its impact on society, in particular the public health and the veryhealthcare system.While the mitigation strategies (including the lockdown) are getting lifted, understanding the current im-pacts of the outbreak remains challenging. This impedes any analysis and scheduling of measures requiredfor the different countries to recover from the pandemic without risking a new outbreak.Therefore, we propose a novel approach to build realistic data-driven pandemic simulation and forecastingmodels to support policymakers. Our models allow the investigation of mitigation/recovery measures andtheir impact. Thereby, they enable appropriate planning of those measures, with the aim to optimize theirsocietal benefits.Our approach relies on a combination of machine learning and classical epidemiological models, circum-venting the respective limitations of these techniques to allow a policy-making based on established knowl-edge, yet driven by factual data, and tailored to each country’s specific context. [less ▲]

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See detailVerification and abstraction of real-time variability-intensive systems
Cordy, Maxime UL; Legay, Axel

in International Journal on Software Tools for Technology Transfer (2019), 21(6), 635-649

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See detailAutomated evaluation of embedded-system design alternatives
Cordy, Maxime UL; Lazreg, Sami

in Proceedings of the 23rd International Systems and Software Product Line Conference, SPLC 2019, Volume A, Paris, France, September 9-13, 2019 (2019, September)

Detailed reference viewed: 40 (2 UL)