@inproceedings {42689,
	title = {FoCA: Failure-oriented Class Augmentation for Robust Image Classification},
	journal = {The 34th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)},
	year = {2022},
	publisher = { IEEE},
	doi = {10.1109/ICTAI56018.2022.00144},
	url = {https://ieeexplore.ieee.org/abstract/document/10098003},
	author = {Mohit Kumar {Ahuja} and Sahil, Sahil and Spieker, Helge}
}
@inproceedings {33066,
	title = {Testing Deep Learning Models: A First Comparative Study of Multiple Testing Techniques},
	journal = {Artificial Intelligence in Software Testing @ 2022 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)},
	year = {2022},
	publisher = { IEEE},
	abstract = {Deep Learning (DL) has revolutionized the capabilities of vision-based systems (VBS) in critical applications such as autonomous driving, robotic surgery, critical infrastructure surveillance, air and maritime traffic control, etc. By analyzing images, voice, videos, or any type of complex signals, DL has considerably increased the situation awareness of these systems. At the same time, while relying more and more on trained DL models, the reliability and robustness of VBS have been challenged and it has become crucial to test thoroughly these models to assess their capabilities and potential errors. To discover faults in DL models, existing software testing methods have been adapted and refined accordingly. In this article, we provide an overview of these software testing methods, namely differential, metamorphic, mutation, and combinatorial testing, as well as adversarial perturbation testing and review some challenges in their deployment for boosting perception systems used in VBS. We also provide a first experimental comparative study on a classical benchmark used in VBS and discuss its results.},
	isbn = {978-1-6654-9628-5},
	doi = {10.1109/ICSTW55395.2022.00035},
	url = {https://ieeexplore.ieee.org/abstract/document/9787976},
	author = {Mohit Kumar {Ahuja} and Gotlieb, Arnaud and Spieker, Helge}
}
@misc {42715,
	title = {T-Largo: Testing of Learning Robots},
	year = {2022},
	type = {Poster for celebrating the 20th anniversary of Simula },
	address = {Simula Research Laboratory - KA23, Oslo, Norway},
	author = {Gotlieb, Arnaud and Mohit Kumar {Ahuja} and Mohamed Bachir {Belaid}}
}
@inproceedings {27711,
	title = {Improving the Reliability of Autonomous Software Systems through  Metamorphic Testing},
	journal = {Proceedings of the 31st European Safety and Reliability Conference (ESREL)},
	year = {2021},
	pages = {1-page abstract},
	publisher = {ESREL},
	isbn = {ISBN: 978-981-18-2016-8},
	author = {Mohit Kumar {Ahuja} and Mohamed Bachir {Belaid} and Bernab{\'e}, Pierre and Gotlieb, Arnaud and Marijan, Dusica and Sharif, Aizaz and Spieker, Helge},
	editor = {Castanier, Bruno and Cepin, Marko and Bigaud, David and Berenguer, Christophe}
}
@inproceedings {27341,
	title = {Opening the Software Engineering Toolbox for the Assessment of Trustworthy AI},
	journal = {1st International Workshop on New Foundations for Human-Centered AI @ ECAI 2020},
	year = {2020},
	month = {08/2020},
	pages = {67-70},
	publisher = {CEUR Workshop Proceedings},
	abstract = {Trustworthiness is a central requirement for the acceptance and success of human-centered artificial intelligence (AI). To deem an AI system as trustworthy, it is crucial to assess its behaviour and characteristics against a gold standard of Trustworthy AI, consisting of guidelines, requirements, or only expectations. While AI systems are highly complex, their implementations are still based on software. The software engineering community has a long-established toolbox for the assessment of software systems, especially in the context of software testing. In this paper, we argue for the application of software engineering and testing practices for the assessment of trustworthy AI. We make the connection between the seven key requirements as defined by the European Commission{\textquoteright}s AI high-level expert group and established procedures from software engineering and raise questions for future work.},
	url = {http://ceur-ws.org/Vol-2659/ahuja.pdf},
	author = {Mohit Kumar {Ahuja} and Mohamed Bachir {Belaid} and Bernab{\'e}, Pierre and Collet, Mathieu and Gotlieb, Arnaud and Lal, Chhagan and Marijan, Dusica and Sen, Sagar and Sharif, Aizaz and Spieker, Helge},
	editor = {Saffiotti, Alessandro and Serafini, Luciano and Lukowicz, Paul}
}
@inproceedings {26562,
	title = {Challenges of Testing Machine Learning Based Systems},
	journal = {Proceedings of the 1st IEEE Artificial Intelligence Testing Conference (AI Test 2019)},
	year = {2019},
	publisher = { IEEE},
	address = {San Francisco, CA, USA},
	author = {Marijan, Dusica and Gotlieb, Arnaud and Mohit Kumar {Ahuja}},
	editor = {IEEE, }
}
@techreport {26779,
	title = {Survey on Testing of Deep Learning Systems},
	year = {2019},
	publisher = {Simula Research Laboratory},
	abstract = {Recent studies have shown that deep learning algorithms used for image classification or object recognition are not sufficiently reliable. These algorithms can be easily fooled by applying perturbations to images or generating artificial images that result in misclassification. In this paper, we provide an overview of software testing methods present in literature to test deep learning systems. We have explored different methods of testing deep neural networks, namely metamorphic testing, mutation testing, differential testing, and adversarial perturbation testing. We present the main findings available from the literature and compare these methods systematically and comprehensively. The results show that systematic testing of deep learning systems can further help to increase the performance of state-of-the-art systems.},
	author = {Mohit Kumar {Ahuja} and Spieker, Helge and Gotlieb, Arnaud and Marijan, Dusica and Mossige, Morten}
}
@misc {26780,
	title = {Training of Deep Learning Models with Reduced Training Dataset using Regression Testing},
	howpublished = {14th Certus User Partner Workshop (UPW), Larvik, Norway},
	year = {2019},
	author = {Mohit Kumar {Ahuja}}
}
