Foutse Khomh

Biography

Foutse Khomh is a full professor of software engineering at Polytechnique Montréal, a Canada CIFAR AI Chair – Trustworthy Machine Learning Software Systems, and an FRQ-IVADO Research Chair in Software Quality Assurance for Machine Learning Applications. Khomh completed a PhD in software engineering at Université de Montréal in 2011, for which he received an Award of Excellence. He was also awarded a CS-Can/Info-Can Outstanding Young Computer Science Researcher Prize in 2019.

His research interests include software maintenance and evolution, machine learning systems engineering, cloud engineering, and dependable and trustworthy ML/AI. His work has received four Ten-year Most Influential Paper (MIP) awards, and six Best/Distinguished Paper Awards. He has served on the steering committee of numerous organizations in software engineering, including SANER (chair), MSR, PROMISE, ICPC (chair), and ICSME (vice-chair). He initiated and co-organized Polytechnique Montréal‘s Software Engineering for Machine Learning Applications (SEMLA) symposium and the RELENG (release engineering) workshop series.

Khomh co-founded the NSERC CREATE SE4AI: A Training Program on the Development, Deployment and Servicing of Artificial Intelligence-based Software Systems, and is a principal investigator for the DEpendable Explainable Learning (DEEL) project.

He also co-founded Confiance IA, a Quebec consortium focused on building trustworthy AI, and is on the editorial board of multiple international software engineering journals, including IEEE Software, EMSE and JSEP. He is a senior member of IEEE.

Current Students

Nanda Assobjio Brice Yvan

Master's Research - Polytechnique Montréal

Gabriel Laberge

PhD - Polytechnique Montréal

Github

forough majidi

PhD - Polytechnique Montréal

Website

mohammadhossein.malekpour@gmail.com

Mohammadhossein Malekpour

Master's Research - Polytechnique Montréal

Github

Elnathan Tiokou Tiokou Fangang

Mohamed Amine Merzouk

Postdoctorate - Polytechnique Montréal

Co-supervisor :

Arghavan Moradi Dakhel

Postdoctorate - Polytechnique Montréal

Master's Research - Polytechnique Montréal

PhD - Polytechnique Montréal

Github

Ben Braiek Yasmine

Master's Research - Polytechnique Montréal

Publications

AmbieGen tool at the SBST 2022 Tool Competition

Dmytro Humeniuk

Giuliano Antoniol

AmbieGen is a tool for generating test cases for cyber-physical systems (CPS). In the context of SBST 2022 CPS tool competition, it has been… (see more) adapted to generating virtual roads to test a car lane keeping assist system. AmbieGen leverages a two objective NSGA-II algorithm to produce the test cases. It has achieved the highest final score, accounting for the test case efficiency, effectiveness and diversity in both testing configurations.

2022-05-01

International Workshop on Search-Based Software Testing (published)

Challenges in Machine Learning Application Development: An Industrial Experience Report

Md Saidur Rahman

Emilio Rivera

Yann‐Gaël Guéhéneuc

Bernd Lehnert

2022-05-01

2022 IEEE/ACM 1st International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI) (published)

Challenges in Machine Learning Application Development: An Industrial Experience Report

Md. Saidur Rahman

Emilio Martínez Rivera

Yann‐Gaël Guéhéneuc

Bernd Lehnert

SAP is the market leader in enterprise application software offering an end-to-end suite of applications and services to enable their custom… (see more)ers worldwide to operate their business. Especially, retail customers of SAP deal with millions of sales transactions for their day-to-day business. Transactions are created during retail sales at the point of sale (POS) terminals and those transactions are then sent to some central servers for validations and other business operations. A considerable proportion of the retail transactions may have inconsistencies or anomalies due to many technical and human errors. SAP provides an automated process for error detection but still requires a manual process by dedicated employees using workbench software for correction. However, manual corrections of these errors are time-consuming, labor-intensive, and might be prone to further errors due to incorrect modifications. Thus, automated detection and correction of transaction errors are very important regarding their potential business values and the improvement in the business workflow. In this paper, we report on our experience from a project where we develop an AI-based system to automatically detect transaction errors and propose corrections. We identify and discuss the challenges that we faced during this collaborative research and development project, from two distinct perspectives: Software Engineering and Machine Learning. We report on our experience and insights from the project with guidelines for the identified challenges. We collect developers’ feedback for qualitative analysis of our findings. We believe that our findings and recommendations can help other researchers and practitioners embarking into similar endeavours. CCS CONCEPTS • Software and its engineering → Programming teams.

2022-05-01

2022 IEEE/ACM 1st International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI) (published)

Identification of Out-of-Distribution Cases of CNN using Class-Based Surprise Adequacy

Mira Marhaba

Ettore Merlo

Giuliano Antoniol

Machine learning is vulnerable to possible incorrect classification of cases that are out of the distribution observed during training and c… (see more)alibration

2022-05-01

2022 IEEE/ACM 1st International Conference on AI Engineering – Software Engineering for AI (CAIN) (published)

Identification of Out-of-Distribution Cases of CNN using Class-Based Surprise Adequacy

Mira Marhaba

Ettore Merlo

Giuliano Antoniol

Machine learning is vulnerable to possible incorrect classification of cases that are out of the distribution observed during training and c… (see more)alibration

2022-05-01

2022 IEEE/ACM 1st International Conference on AI Engineering – Software Engineering for AI (CAIN) (published)

Clones in deep learning code: what, where, and why?

Hadhemi Jebnoun

Md Saidur Rahman

Biruk Asmare Muse

2022-04-08

Empirical Software Engineering (published)

arxiv.org

Software-Engineering Design Patterns for Machine Learning Applications

Hironori Washizaki

Yann‐Gaël Guéhéneuc

Hironori Takeuchi

Naotake Natori

Takuo Doi

Satoshi Okuda

In this study, a multivocal literature review identified 15 software-engineering design patterns for machine learning applications. Findings… (see more) suggest that there are opportunities to increase the patterns’ adoption in practice by raising awareness of such patterns within the community.

2022-03-01

Computer (published)

Software-Engineering Design Patterns for Machine Learning Applications

Hironori Washizaki