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ORCIDFoutse Khomh
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- affiliation: Polytechnique Montreal, Canada
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2020 – today
- 2022
[j64]Amin Nikanjam
, Mohammad Mehdi Morovati, Foutse Khomh, Houssem Ben Braiek:
Faults in deep reinforcement learning programs: a taxonomy and a detection approach. Autom. Softw. Eng. 29(1): 8 (2022)- [j63]Florian Tambon, Gabriel Laberge, Le An, Amin Nikanjam, Paulina Stevia Nouwou Mindom, Yann Pequignot, Foutse Khomh, Giulio Antoniol, Ettore Merlo, François Laviolette:
How to certify machine learning based safety-critical systems? A systematic literature review. Autom. Softw. Eng. 29(2): 38 (2022) - [j62]Hironori Washizaki, Foutse Khomh, Yann-Gaël Guéhéneuc, Hironori Takeuchi, Naotake Natori, Takuo Doi, Satoshi Okuda:
Software-Engineering Design Patterns for Machine Learning Applications. Computer 55(3): 30-39 (2022) - [j61]Hadhemi Jebnoun, Md. Saidur Rahman, Foutse Khomh, Biruk Asmare Muse:
Clones in deep learning code: what, where, and why? Empir. Softw. Eng. 27(4): 84 (2022) - [j60]Mohammad Masudur Rahman, Foutse Khomh, Marco Castelluccio:
Works for Me! Cannot Reproduce - A Large Scale Empirical Study of Non-reproducible Bugs. Empir. Softw. Eng. 27(5): 111 (2022) - [j59]Mouna Abidi, Md. Saidur Rahman, Moses Openja, Foutse Khomh:
Multi-language design smells: a backstage perspective. Empir. Softw. Eng. 27(5): 116 (2022) - [j58]Dmytro Humeniuk, Foutse Khomh, Giuliano Antoniol:
A search-based framework for automatic generation of testing environments for cyber-physical systems. Inf. Softw. Technol. 149: 106936 (2022) - [j57]Amin Nikanjam, Houssem Ben Braiek, Mohammad Mehdi Morovati, Foutse Khomh:
Automatic Fault Detection for Deep Learning Programs Using Graph Transformations. ACM Trans. Softw. Eng. Methodol. 31(1): 14:1-14:27 (2022) - [j56]Gias Uddin, Yann-Gaël Guéhéneuc, Foutse Khomh, Chanchal K. Roy:
An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Datasets. ACM Trans. Softw. Eng. Methodol. 31(3): 48:1-48:38 (2022) - [j55]Morteza Verdi, Ashkan Sami, Jafar Akhondali, Foutse Khomh, Gias Uddin, Alireza Karami Motlagh:
An Empirical Study of C++ Vulnerabilities in Crowd-Sourced Code Examples. IEEE Trans. Software Eng. 48(5): 1497-1514 (2022) - [c124]Moses Openja, Forough Majidi, Foutse Khomh, Bhagya Chembakottu, Heng Li:
Studying the Practices of Deploying Machine Learning Projects on Docker. EASE 2022: 190-200 - [c123]Dmytro Humeniuk, Giuliano Antoniol, Foutse Khomh:
AmbieGen tool at the SBST 2022 Tool Competition. SBST@ICSE 2022: 43-46 - [c122]Biruk Asmare Muse, Foutse Khomh, Giuliano Antoniol:
Do Developers Refactor Data Access Code? An Empirical Study. SANER 2022: 25-35 - [i62]Biruk Asmare Muse, Csaba Nagy, Anthony Cleve, Foutse Khomh, Giuliano Antoniol:
FIXME: Synchronize with Database An Empirical Study of Data Access Self-Admitted Technical Debt. CoRR abs/2201.02180 (2022) - [i61]Biruk Asmare Muse, Mohammad Masudur Rahman, Csaba Nagy, Anthony Cleve, Foutse Khomh, Giuliano Antoniol:
On the Prevalence, Impact, and Evolution of SQL Code Smells in Data-Intensive Systems. CoRR abs/2201.02215 (2022) - [i60]Biruk Asmare Muse, Foutse Khomh, Giuliano Antoniol:
Do Developers Refactor Data Access Code? An Empirical Study. CoRR abs/2202.03270 (2022) - [i59]Dmytro Humeniuk, Foutse Khomh, Giuliano Antoniol:
A Search-Based Framework for Automatic Generation of Testing Environments for Cyber-Physical Systems. CoRR abs/2203.12138 (2022) - [i58]Houssem Ben Braiek, Foutse Khomh:
Testing Feedforward Neural Networks Training Programs. CoRR abs/2204.00694 (2022) - [i57]Mohamed Raed El aoun, Heng Li, Foutse Khomh, Lionel N. Tidjon:
Bug Characteristics in Quantum Software Ecosystem. CoRR abs/2204.11965 (2022) - [i56]Mohamed Raed El aoun, Heng Li, Foutse Khomh, Moses Openja:
Understanding Quantum Software Engineering Challenges An Empirical Study on Stack Exchange Forums and GitHub Issues. CoRR abs/2205.03181 (2022) - [i55]Moses Openja, Mohammad Mehdi Morovati, Le An, Foutse Khomh, Mouna Abidi:
Technical Debts and Faults in Open-source Quantum Software Systems: An Empirical Study. CoRR abs/2206.00666 (2022) - [i54]Moses Openja, Forough Majidi, Foutse Khomh, Bhagya Chembakottu, Heng Li:
Studying the Practices of Deploying Machine Learning Projects on Docker. CoRR abs/2206.00699 (2022) - [i53]Lionel Nganyewou Tidjon, Foutse Khomh:
The Different Faces of AI Ethics Across the World: A Principle-Implementation Gap Analysis. CoRR abs/2206.03225 (2022) - [i52]Lionel Nganyewou Tidjon, Foutse Khomh:
Never trust, always verify : a roadmap for Trustworthy AI? CoRR abs/2206.11981 (2022) - [i51]Mohammad Mehdi Morovati, Amin Nikanjam, Foutse Khomh, Zhen Ming Jiang:
Bugs in Machine Learning-based Systems: A Faultload Benchmark. CoRR abs/2206.12311 (2022) - [i50]Moses Openja, Amin Nikanjam, Ahmed Haj Yahmed, Foutse Khomh, Zhen Ming Jiang:
An Empirical Study of Challenges in Converting Deep Learning Models. CoRR abs/2206.14322 (2022) - [i49]Arghavan Moradi Dakhel, Vahid Majdinasab, Amin Nikanjam, Foutse Khomh, Michel C. Desmarais, Zhen Ming Jiang:
GitHub Copilot AI pair programmer: Asset or Liability? CoRR abs/2206.15331 (2022) - [i48]Lionel Nganyewou Tidjon, Foutse Khomh:
Threat Assessment in Machine Learning based Systems. CoRR abs/2207.00091 (2022) - [i47]Arghavan Moradi Dakhel, Michel C. Desmarais, Foutse Khomh:
Dev2vec: Representing Domain Expertise of Developers in an Embedding Space. CoRR abs/2207.05132 (2022) - [i46]Ahmed Haj Yahmed, Houssem Ben Braiek, Foutse Khomh, Sonia Bouzidi, Rania Zaatour:
DiverGet: A Search-Based Software Testing Approach for Deep Neural Network Quantization Assessment. CoRR abs/2207.06282 (2022) - 2021
- [j54]Zeinab Azadeh Kermansaravi, Md. Saidur Rahman, Foutse Khomh, Fehmi Jaafar, Yann-Gaël Guéhéneuc:
Investigating design anti-pattern and design pattern mutations and their change- and fault-proneness. Empir. Softw. Eng. 26(1): 9 (2021) - [j53]Mohammad Masudur Rahman, Foutse Khomh, Shamima Yeasmin, Chanchal K. Roy:
The forgotten role of search queries in IR-based bug localization: an empirical study. Empir. Softw. Eng. 26(6): 116 (2021) - [j52]Gias Uddin, Fatima Sabir, Yann-Gaël Guéhéneuc, Omar Alam, Foutse Khomh:
An empirical study of IoT topics in IoT developer discussions on Stack Overflow. Empir. Softw. Eng. 26(6): 121 (2021) - [j51]Mohab Aly, Foutse Khomh, Soumaya Yacout:
What Do Practitioners Discuss about IoT and Industry 4.0 Related Technologies? Characterization and Identification of IoT and Industry 4.0 Categories in Stack Overflow Discussions. Internet Things 14: 100364 (2021) - [j50]Amine Barrak, Ellis E. Eghan, Bram Adams, Foutse Khomh:
Why do builds fail? - A conceptual replication study. J. Syst. Softw. 177: 110939 (2021) - [j49]Rodrigo F. Silva, Mohammad Masudur Rahman, Carlos Eduardo de Carvalho Dantas, Chanchal Kumar Roy, Foutse Khomh, Marcelo de Almeida Maia:
Improved retrieval of programming solutions with code examples using a multi-featured score. J. Syst. Softw. 181: 111063 (2021) - [j48]Patanamon Thongtanunam, Ayushi Rastogi, Foutse Khomh, Serge Demeyer, Meiyappan Nagappan, Kelly Blincoe, Gregorio Robles:
Shadow Program Committee Initiative: Process and Reflection. ACM SIGSOFT Softw. Eng. Notes 46(4): 16-18 (2021) - [j47]Mouna Abidi, Md. Saidur Rahman, Moses Openja, Foutse Khomh:
Are Multi-Language Design Smells Fault-Prone? An Empirical Study. ACM Trans. Softw. Eng. Methodol. 30(3): 29:1-29:56 (2021) - [j46]Gias Uddin, Foutse Khomh, Chanchal K. Roy:
Automatic API Usage Scenario Documentation from Technical Q&A Sites. ACM Trans. Softw. Eng. Methodol. 30(3): 31:1-31:45 (2021) - [j45]Gias Uddin, Foutse Khomh:
Automatic Mining of Opinions Expressed About APIs in Stack Overflow. IEEE Trans. Software Eng. 47(3): 522-559 (2021) - [j44]Gias Uddin, Olga Baysal, Latifa Guerrouj, Foutse Khomh:
Understanding How and Why Developers Seek and Analyze API-Related Opinions. IEEE Trans. Software Eng. 47(4): 694-735 (2021) - [c121]Arghavan Moradi Dakhel, Michel C. Desmarais, Foutse Khomh:
Assessing Developer Expertise from the Statistical Distribution of Programming Syntax Patterns. EASE 2021: 90-99 - [c120]Amin Nikanjam, Foutse Khomh:
Design Smells in Deep Learning Programs: An Empirical Study. ICSME 2021: 332-342 - [c119]Mohamed Raed El aoun, Heng Li, Foutse Khomh, Moses Openja:
Understanding Quantum Software Engineering Challenges An Empirical Study on Stack Exchange Forums and GitHub Issues. ICSME 2021: 343-354 - [c118]Paulina Stevia Nouwou Mindom, Amin Nikanjam, Foutse Khomh, John Mullins:
On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods. QRS 2021: 260-269 - [c117]Emilio Rivera-Landos, Foutse Khomh, Amin Nikanjam:
The Challenge of Reproducible ML: An Empirical Study on The Impact of Bugs. QRS 2021: 1079-1088 - [c116]Dmytro Humeniuk, Giuliano Antoniol, Foutse Khomh:
Data Driven Testing of Cyber Physical Systems. SBST@ICSE 2021: 16-19 - [c115]Dmytro Humeniuk, Giuliano Antoniol, Foutse Khomh:
SWAT tool at the SBST 2021 Tool Competition. SBST@ICSE 2021: 42-43 - [c114]Alaleh Hamidi, Giuliano Antoniol, Foutse Khomh, Massimiliano Di Penta, Mohammad Hamidi:
Towards Understanding Developers' Machine-Learning Challenges: A Multi-Language Study on Stack Overflow. SCAM 2021: 58-69 - [c113]Mahmood Vahedi, Mohammad Masudur Rahman, Foutse Khomh, Gias Uddin, Giuliano Antoniol:
Summarizing Relevant Parts from Technical Videos. SANER 2021: 434-445 - [c112]Mbarka Soualhia, Foutse Khomh, Sofiène Tahar:
Failure Analysis of Hadoop Schedulers using an Integration of Model Checking and Simulation. SCSS 2021: 114-128 - [p2]Osama Ehsan, Liliane Barbour, Foutse Khomh, Ying Zou:
Is Late Propagation a Harmful Code Clone Evolutionary Pattern? An Empirical Study. Code Clone Analysis 2021: 151-167 - [e9]Nobukazu Yoshioka, Hironori Washizaki, Eduardo B. Fernández, Tomoko Kaneko, Shuichiro Yamamoto, Fuyuki Ishikawa, Foutse Khomh, Giuliano Antoniol:
Proceedings of the International Workshop on Evidence-based Security and Privacy in the Wild and the 1st International Workshop on Machine Learning Systems Engineering co-located with 25th Asia-Pacific Software Engineering Conference (APSEC 2018), Nara, Japan, December 4, 2018. CEUR Workshop Proceedings 2809, CEUR-WS.org 2021 [contents] - [i45]Amin Nikanjam, Mohammad Mehdi Morovati, Foutse Khomh, Houssem Ben Braiek:
Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approach. CoRR abs/2101.00135 (2021) - [i44]Gias Uddin, Olga Baysal, Latifa Guerrouj, Foutse Khomh:
Understanding How and Why Developers Seek and Analyze API-related Opinions. CoRR abs/2102.08495 (2021) - [i43]Gias Uddin, Foutse Khomh, Chanchal K. Roy:
Automatic API Usage Scenario Documentation from Technical Q&A Sites. CoRR abs/2102.08502 (2021) - [i42]Gias Uddin, Foutse Khomh, Chanchal K. Roy:
Mining API Usage Scenarios from Stack Overflow. CoRR abs/2102.08874 (2021) - [i41]Dmytro Humeniuk, Giuliano Antoniol, Foutse Khomh:
Data Driven Testing of Cyber Physical Systems. CoRR abs/2102.11491 (2021) - [i40]Zeinab Azadeh Kermansaravi, Md. Saidur Rahman, Foutse Khomh, Fehmi Jaafar, Yann-Gaël Guéhéneuc:
Investigating Design Anti-pattern and Design Pattern Mutations and Their Change- and Fault-proneness. CoRR abs/2104.00058 (2021) - [i39]Amin Nikanjam, Houssem Ben Braiek, Mohammad Mehdi Morovati, Foutse Khomh:
Automatic Fault Detection for Deep Learning Programs Using Graph Transformations. CoRR abs/2105.08095 (2021) - [i38]Amin Nikanjam, Foutse Khomh:
Design Smells in Deep Learning Programs: An Empirical Study. CoRR abs/2107.02279 (2021) - [i37]Florian Tambon, Giulio Antoniol, Foutse Khomh:
HOMRS: High Order Metamorphic Relations Selector for Deep Neural Networks. CoRR abs/2107.04863 (2021) - [i36]Florian Tambon, Gabriel Laberge, Le An, Amin Nikanjam, Paulina Stevia Nouwou Mindom, Yann Pequignot, Foutse Khomh, Giulio Antoniol, Ettore Merlo, François Laviolette:
How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review. CoRR abs/2107.12045 (2021) - [i35]Ettore Merlo, Mira Marhaba, Foutse Khomh, Houssem Ben Braiek, Giuliano Antoniol:
Models of Computational Profiles to Study the Likelihood of DNN Metamorphic Test Cases. CoRR abs/2107.13491 (2021) - [i34]Hadhemi Jebnoun, Md. Saidur Rahman, Foutse Khomh, Biruk Asmare Muse:
Clones in Deep Learning Code: What, Where, and Why? CoRR abs/2107.13614 (2021) - [i33]Rodrigo F. Silva, Mohammad Masudur Rahman, Carlos Eduardo de Carvalho Dantas, Chanchal K. Roy, Foutse Khomh, Marcelo de Almeida Maia:
Improved Retrieval of Programming Solutions With Code Examples Using a Multi-featured Score. CoRR abs/2108.02702 (2021) - [i32]Mohammad Masudur Rahman, Foutse Khomh, Marco Castelluccio:
Why are Some Bugs Non-Reproducible? An Empirical Investigation using Data Fusion. CoRR abs/2108.05316 (2021) - [i31]Mohammad Masudur Rahman, Foutse Khomh, Shamima Yeasmin, Chanchal K. Roy:
The Forgotten Role of Search Queries in IR-based Bug Localization: An Empirical Study. CoRR abs/2108.05341 (2021) - [i30]Emilio Rivera-Landos, Foutse Khomh, Amin Nikanjam:
The challenge of reproducible ML: an empirical study on the impact of bugs. CoRR abs/2109.03991 (2021) - [i29]Gabriel Laberge, Yann Pequignot, Foutse Khomh, Mario Marchand, Alexandre Mathieu:
Partial order: Finding Consensus among Uncertain Feature Attributions. CoRR abs/2110.13369 (2021) - [i28]Gias Uddin, Yann-Gaël Guéhéneuc, Foutse Khomh, Chanchal K. Roy:
An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Datasets. CoRR abs/2111.03196 (2021) - [i27]Paulina Stevia Nouwou Mindom, Amin Nikanjam, Foutse Khomh, John Mullins:
On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods. CoRR abs/2111.04865 (2021) - [i26]Iren Mazloomzadeh, Gias Udin, Foutse Khomh, Ashkan Sami:
Reputation Gaming in Stack Overflow. CoRR abs/2111.07101 (2021) - [i25]Florian Tambon, Amin Nikanjam, Le An, Foutse Khomh, Giuliano Antoniol:
Silent Bugs in Deep Learning Frameworks: An Empirical Study of Keras and TensorFlow. CoRR abs/2112.13314 (2021) - [i24]Md. Saidur Rahman, Foutse Khomh, Alaleh Hamidi, Jinghui Cheng, Giuliano Antoniol, Hironori Washizaki:
Machine Learning Application Development: Practitioners' Insights. CoRR abs/2112.15277 (2021) - 2020
- [j43]Bowen Xu, Le An, Ferdian Thung, Foutse Khomh, David Lo:
Why reinventing the wheels? An empirical study on library reuse and re-implementation. Empir. Softw. Eng. 25(1): 755-789 (2020) - [j42]Rodrigo Morales, Foutse Khomh, Giuliano Antoniol:
RePOR: Mimicking humans on refactoring tasks. Are we there yet? Empir. Softw. Eng. 25(4): 2960-2996 (2020) - [j41]Gias Uddin, Foutse Khomh, Chanchal K. Roy:
Mining API usage scenarios from stack overflow. Inf. Softw. Technol. 122: 106277 (2020) - [j40]Cristiano Politowski, Foutse Khomh, Simone Romano, Giuseppe Scanniello, Fábio Petrillo, Yann-Gaël Guéhéneuc, Abdou Maiga:
A large scale empirical study of the impact of Spaghetti Code and Blob anti-patterns on program comprehension. Inf. Softw. Technol. 122: 106278 (2020) - [j39]Antoine Barbez, Foutse Khomh, Yann-Gaël Guéhéneuc:
A machine-learning based ensemble method for anti-patterns detection. J. Syst. Softw. 161 (2020) - [j38]Houssem Ben Braiek, Foutse Khomh:
On testing machine learning programs. J. Syst. Softw. 164: 110542 (2020) - [j37]Bram Adams, Foutse Khomh:
The Diversity Crisis of Software Engineering for Artificial Intelligence. IEEE Softw. 37(4): 104-108 (2020) - [j36]Mbarka Soualhia, Foutse Khomh, Sofiène Tahar:
A Dynamic and Failure-Aware Task Scheduling Framework for Hadoop. IEEE Trans. Cloud Comput. 8(2): 553-569 (2020) - [c111]Solomon Berhe, Marc Maynard, Foutse Khomh:
Software Release Patterns When is it a good time to update a software component? ANT/EDI40 2020: 618-625 - [c110]Devansh Tiwari, Hironori Washizaki, Yoshiaki Fukazawa, Tomoyuki Fukuoka, Junji Tamaki, Nobuhiro Hosotani, Munetaka Kohama, Yann-Gaël Guéhéneuc, Foutse Khomh:
Commit - Defect and Architectural Metrics - based Quality Assessment of C Language. ENASE 2020: 579-586 - [c109]Ehsan Firouzi, Ashkan Sami, Foutse Khomh, Gias Uddin:
On the use of C# Unsafe Code Context: An Empirical Study of Stack Overflow. ESEM 2020: 39:1-39:6 - [c108]Mouna Abidi, Foutse Khomh:
Towards the Definition of Patterns and Code Smells for Multi-language Systems. EuroPLoP 2020: 37:1-37:13 - [c107]Cyrine Zid, Dmytro Humeniuk, Foutse Khomh, Giuliano Antoniol:
Double Cycle Hybrid Testing of Hybrid Distributed IoT System. ICSE (Workshops) 2020: 529-532 - [c106]Moses Openja, Bram Adams, Foutse Khomh:
Analysis of Modern Release Engineering Topics : - A Large-Scale Study using StackOverflow -. ICSME 2020: 104-114 - [c105]Mohammad Masudur Rahman, Foutse Khomh, Marco Castelluccio:
Why are Some Bugs Non-Reproducible? : -An Empirical Investigation using Data Fusion-. ICSME 2020: 605-616 - [c104]Hironori Washizaki, Hironori Takeuchi, Foutse Khomh, Naotake Natori, Takuo Doi, Satoshi Okuda:
Practitioners' insights on machine-learning software engineering design patterns: a preliminary study. ICSME 2020: 797-799 - [c103]Fabiano Pecorelli, Fabio Palomba, Foutse Khomh, Andrea De Lucia:
Developer-Driven Code Smell Prioritization. MSR 2020: 220-231 - [c102]Biruk Asmare Muse, Mohammad Masudur Rahman, Csaba Nagy, Anthony Cleve, Foutse Khomh, Giuliano Antoniol:
On the Prevalence, Impact, and Evolution of SQL Code Smells in Data-Intensive Systems. MSR 2020: 327-338 - [c101]Hadhemi Jebnoun, Houssem Ben Braiek, Mohammad Masudur Rahman, Foutse Khomh:
The Scent of Deep Learning Code: An Empirical Study. MSR 2020: 420-430 - [c100]Mouna Abidi, Moses Openja, Foutse Khomh:
Multi-language Design Smells: A Backstage Perspective. MSR 2020: 615-618 - [e8]Foutse Khomh, Pasquale Salza, Gemma Catolino:
Proceedings of the 4th ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation, MaLTeSQuE@ESEC/SIGSOFT FSE 2020, Virtual Event, USA, November 13, 2020. ACM 2020, ISBN 978-1-4503-8124-6 [contents] - [e7]Kostas Kontogiannis, Foutse Khomh, Alexander Chatzigeorgiou, Marios-Eleftherios Fokaefs, Minghui Zhou:
27th IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2020, London, ON, Canada, February 18-21, 2020. IEEE 2020, ISBN 978-1-7281-5143-4 [contents] - [i23]Cristiano Politowski, Foutse Khomh, Simone Romano, Giuseppe Scanniello, Fábio Petrillo, Yann-Gaël Guéhéneuc, Abdou Maiga:
A Large Scale Empirical Study of the Impact of Spaghetti Code and Blob Anti-patterns on Program Comprehension. CoRR abs/2009.02438 (2020) - [i22]Mouna Abidi, Md. Saidur Rahman, Moses Openja, Foutse Khomh:
Are Multi-language Design Smells Prevalent? An Empirical Study. CoRR abs/2010.14331 (2020)
2010 – 2019
- 2019
- [j35]Le An, Marco Castelluccio, Foutse Khomh:
An empirical study of DLL injection bugs in the Firefox ecosystem. Empir. Softw. Eng. 24(4): 1799-1822 (2019) - [j34]Marco Castelluccio, Le An, Foutse Khomh:
An empirical study of patch uplift in rapid release development pipelines. Empir. Softw. Eng. 24(5): 3008-3044 (2019) - [j33]Ikram El Asri, Noureddine Kerzazi, Gias Uddin, Foutse Khomh, Mohammed Amine Janati Idrissi:
An empirical study of sentiments in code reviews. Inf. Softw. Technol. 114: 37-54 (2019) - [j32]Mohab Aly, Foutse Khomh, Mohammed Haoues, Alejandro Quintero, Soumaya Yacout:
Enforcing security in Internet of Things frameworks: A Systematic Literature Review. Internet Things 6 (2019) - [j31]Mohab Aly, Foutse Khomh, Yann-Gaël Guéhéneuc, Hironori Washizaki, Soumaya Yacout:
Is Fragmentation a Threat to the Success of the Internet of Things? IEEE Internet Things J. 6(1): 472-487 (2019) - [j30]Fábio Petrillo, Yann-Gaël Guéhéneuc, Marcelo Pimenta, Carla Maria Dal Sasso Freitas, Foutse Khomh:
Swarm debugging: The collective intelligence on interactive debugging. J. Syst. Softw. 153: 152-174 (2019) - [j29]David Johannes, Foutse Khomh, Giuliano Antoniol:
A large-scale empirical study of code smells in JavaScript projects. Softw. Qual. J. 27(3): 1271-1314 (2019) - [c99]Mouna Abidi, Manel Grichi, Foutse Khomh:
Behind the scenes: developers' perception of multi-language practices. CASCON 2019: 72-81 - [c98]Manel Grichi, Mouna Abidi, Yann-Gaël Guéhéneuc, Foutse Khomh:
State of practices of Java native interface. CASCON 2019: 274-283 - [c97]Mbarka Soualhia, Chunyan Fu, Foutse Khomh:
Infrastructure fault detection and prediction in edge cloud environments. SEC 2019: 222-235 - [c96]Mouna Abidi, Manel Grichi, Foutse Khomh, Yann-Gaël Guéhéneuc:
Code smells for multi-language systems. EuroPLoP 2019: 12:1-12:13 - [c95]Mouna Abidi, Foutse Khomh, Yann-Gaël Guéhéneuc:
Anti-patterns for multi-language systems. EuroPLoP 2019: 42:1-42:14 - [c94]Gias Uddin, Foutse Khomh, Chanchal K. Roy:
Towards crowd-sourced API documentation. ICSE (Companion Volume) 2019: 310-311 - [c93]Antoine Barbez, Foutse Khomh, Yann-Gaël Guéhéneuc:
Deep Learning Anti-Patterns from Code Metrics History. ICSME 2019: 114-124 - [c92]Houssem Ben Braiek, Foutse Khomh:
DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks. ICSME 2019: 454-458 - [c91]Hironori Washizaki, Hiromu Uchida, Foutse Khomh, Yann-Gaël Guéhéneuc:
Studying Software Engineering Patterns for Designing Machine Learning Systems. IWESEP 2019: 49-54 - [c90]Houssem Ben Braiek, Foutse Khomh:
TFCheck : A TensorFlow Library for Detecting Training Issues in Neural Network Programs. QRS 2019: 426-433 - [c89]John Businge, Moses Openja, David Kavaler, Engineer Bainomugisha, Foutse Khomh, Vladimir Filkov:
Studying Android App Popularity by Cross-Linking GitHub and Google Play Store. SANER 2019: 287-297 - [p1]Yann-Gaël Guéhéneuc, Foutse Khomh:
Empirical Software Engineering. Handbook of Software Engineering 2019: 285-320 - [e6]