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ORCIDFoutse Khomh
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- affiliation: Polytechnique Montreal, Canada
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2020 – today
- 2023
[j74]Khaled Badran
, Pierre-Olivier Côté, Amanda Kolopanis, Rached Bouchoucha, Antonio Collante, Diego Elias Costa, Emad Shihab, Foutse Khomh:
Can Ensembling Preprocessing Algorithms Lead to Better Machine Learning Fairness? Computer 56(4): 71-79 (2023)- [j73]Biruk Asmare Muse, Foutse Khomh, Giuliano Antoniol:
Refactoring practices in the context of data-intensive systems. Empir. Softw. Eng. 28(2): 46 (2023) - [j72]Mohammad Mehdi Morovati, Amin Nikanjam, Foutse Khomh, Zhen Ming (Jack) Jiang:
Bugs in machine learning-based systems: a faultload benchmark. Empir. Softw. Eng. 28(3): 62 (2023) - [j71]Osama Ehsan, Foutse Khomh, Ying Zou, Dong Qiu:
Ranking code clones to support maintenance activities. Empir. Softw. Eng. 28(3): 70 (2023) - [j70]Florian Tambon, Foutse Khomh, Giuliano Antoniol:
A probabilistic framework for mutation testing in deep neural networks. Inf. Softw. Technol. 155: 107129 (2023) - [j69]Fatemeh Yousefifeshki, Heng Li, Foutse Khomh:
Studying the challenges of developing hardware description language programs. Inf. Softw. Technol. 159: 107196 (2023) - [j68]Arghavan Moradi Dakhel, Michel C. Desmarais, Foutse Khomh:
Dev2vec: Representing domain expertise of developers in an embedding space. Inf. Softw. Technol. 159: 107218 (2023) - [c133]Florian Tambon, Vahid Majdinasab, Amin Nikanjam, Foutse Khomh, Giuliano Antoniol:
Mutation Testing of Deep Reinforcement Learning Based on Real Faults. ICST 2023: 188-198 - [i78]Dmytro Humeniuk, Foutse Khomh, Giuliano Antoniol:
AmbieGen: A Search-based Framework for Autonomous Systems Testing. CoRR abs/2301.01234 (2023) - [i77]Patrick Loic Foalem, Foutse Khomh, Heng Li:
Studying Logging Practice in Machine Learning-based Applications. CoRR abs/2301.04234 (2023) - [i76]Florian Tambon, Vahid Majdinasab, Amin Nikanjam, Foutse Khomh, Giuliano Antoniol:
Mutation Testing of Deep Reinforcement Learning Based on Real Faults. CoRR abs/2301.05651 (2023) - [i75]Seif Abukhalaf, Mohammad Hamdaqa, Foutse Khomh:
On Codex Prompt Engineering for OCL Generation: An Empirical Study. CoRR abs/2303.16244 (2023) - [i74]AmirHossein Naghshzan, Saeed Khalilazar, Pierre Poilane, Olga Baysal, Latifa Guerrouj, Foutse Khomh:
Leveraging Data Mining Algorithms to Recommend Source Code Changes. CoRR abs/2305.00323 (2023) - 2022
- [j67]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) - [j66]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) - [j65]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) - [j64]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) - [j63]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) - [j62]Mouna Abidi, Md. Saidur Rahman, Moses Openja, Foutse Khomh:
Multi-language design smells: a backstage perspective. Empir. Softw. Eng. 27(5): 116 (2022) - [j61]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. Empir. Softw. Eng. 27(6): 130 (2022) - [j60]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. Empir. Softw. Eng. 27(7): 193 (2022) - [j59]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) - [j58]Moses Openja, Mohammad Mehdi Morovati, Le An, Foutse Khomh, Mouna Abidi:
Technical debts and faults in open-source quantum software systems: An empirical study. J. Syst. Softw. 193: 111458 (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) - [c132]Mira Marhaba, Ettore Merlo, Foutse Khomh, Giuliano Antoniol:
Identification of out-of-distribution cases of CNN using class-based surprise adequacy. CAIN 2022: 39-40 - [c131]Moses Openja, Forough Majidi, Foutse Khomh, Bhagya Chembakottu, Heng Li:
Studying the Practices of Deploying Machine Learning Projects on Docker. EASE 2022: 190-200 - [c130]Moses Openja, Amin Nikanjam, Ahmed Haj Yahmed, Foutse Khomh, Zhen Ming Jack Jiang:
An Empirical Study of Challenges in Converting Deep Learning Models. ICSME 2022: 13-23 - [c129]Forough Majidi, Moses Openja, Foutse Khomh, Heng Li:
An Empirical Study on the Usage of Automated Machine Learning Tools. ICSME 2022: 59-70 - [c128]Saumendu Roy, Gabriel Laberge, Banani Roy, Foutse Khomh, Amin Nikanjam, Saikat Mondal:
Why Don't XAI Techniques Agree? Characterizing the Disagreements Between Post-hoc Explanations of Defect Predictions. ICSME 2022: 444-448 - [c127]Houssem Ben Braiek, Ali Tfaily, Foutse Khomh, Thomas Reid, Ciro Guida:
SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design. ASE 2022: 94:1-94:13 - [c126]Dmytro Humeniuk, Giuliano Antoniol, Foutse Khomh:
AmbieGen tool at the SBST 2022 Tool Competition. SBST@ICSE 2022: 43-46 - [c125]Aurel Ikama, Vincent Du, Philippe Belias, Biruk Asmare Muse, Foutse Khomh, Mohammad Hamdaqa:
Revisiting the Impact of Anti-patterns on Fault-Proneness: A Differentiated Replication. SCAM 2022: 56-65 - [c124]Foutse Khomh:
Data quality and model under-specification issues (keynote). SEA4DQ@ESEC/SIGSOFT FSE 2022: 2 - [c123]Poedjadevie Kadjel Ramkisoen, John Businge, Brent van Bladel, Alexandre Decan, Serge Demeyer, Coen De Roover, Foutse Khomh:
PaReco: patched clones and missed patches among the divergent variants of a software family. ESEC/SIGSOFT FSE 2022: 646-658 - [c122]Biruk Asmare Muse, Foutse Khomh, Giuliano Antoniol:
Do Developers Refactor Data Access Code? An Empirical Study. SANER 2022: 25-35 - [i73]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) - [i72]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) - [i71]Biruk Asmare Muse, Foutse Khomh, Giuliano Antoniol:
Do Developers Refactor Data Access Code? An Empirical Study. CoRR abs/2202.03270 (2022) - [i70]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) - [i69]Houssem Ben Braiek, Foutse Khomh:
Testing Feedforward Neural Networks Training Programs. CoRR abs/2204.00694 (2022) - [i68]Mohamed Raed El aoun, Heng Li, Foutse Khomh, Lionel N. Tidjon:
Bug Characteristics in Quantum Software Ecosystem. CoRR abs/2204.11965 (2022) - [i67]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) - [i66]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) - [i65]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) - [i64]Lionel Nganyewou Tidjon, Foutse Khomh:
The Different Faces of AI Ethics Across the World: A Principle-Implementation Gap Analysis. CoRR abs/2206.03225 (2022) - [i63]Lionel Nganyewou Tidjon, Foutse Khomh:
Never trust, always verify : a roadmap for Trustworthy AI? CoRR abs/2206.11981 (2022) - [i62]Mohammad Mehdi Morovati, Amin Nikanjam, Foutse Khomh, Zhen Ming Jiang:
Bugs in Machine Learning-based Systems: A Faultload Benchmark. CoRR abs/2206.12311 (2022) - [i61]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) - [i60]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) - [i59]Lionel Nganyewou Tidjon, Foutse Khomh:
Threat Assessment in Machine Learning based Systems. CoRR abs/2207.00091 (2022) - [i58]Arghavan Moradi Dakhel, Michel C. Desmarais, Foutse Khomh:
Dev2vec: Representing Domain Expertise of Developers in an Embedding Space. CoRR abs/2207.05132 (2022) - [i57]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) - [i56]Florian Tambon, Foutse Khomh, Giuliano Antoniol:
A Probabilistic Framework for Mutation Testing in Deep Neural Networks. CoRR abs/2208.06018 (2022) - [i55]Biruk Asmare Muse, Kawser Wazed Nafi, Foutse Khomh, Giuliano Antoniol:
Data-access performance anti-patterns in data-intensive systems. CoRR abs/2208.08918 (2022) - [i54]Pierre-Olivier Côté, Amin Nikanjam, Rached Bouchoucha, Foutse Khomh:
Quality issues in Machine Learning Software Systems. CoRR abs/2208.08982 (2022) - [i53]Paulina Stevia Nouwou Mindom, Amin Nikanjam, Foutse Khomh:
A Comparison of Reinforcement Learning Frameworks for Software Testing Tasks. CoRR abs/2208.12136 (2022) - [i52]Forough Majidi, Moses Openja, Foutse Khomh, Heng Li:
An Empirical Study on the Usage of Automated Machine Learning Tools. CoRR abs/2208.13116 (2022) - [i51]Houssem Ben Braiek, Thomas Reid, Foutse Khomh:
Physics-Guided Adversarial Machine Learning for Aircraft Systems Simulation. CoRR abs/2209.03431 (2022) - [i50]Houssem Ben Braiek, Ali Tfaily, Foutse Khomh, Thomas Reid, Ciro Guida:
SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design. CoRR abs/2209.03438 (2022) - [i49]Lionel Nganyewou Tidjon, Foutse Khomh:
Reliable Malware Analysis and Detection using Topology Data Analysis. CoRR abs/2211.01535 (2022) - [i48]Mohamed Raed El aoun, Lionel Nganyewou Tidjon, Benjamin Rombaut, Foutse Khomh, Ahmed E. Hassan:
An Empirical Study of Library Usage and Dependency in Deep Learning Frameworks. CoRR abs/2211.15733 (2022) - [i47]Khaled Badran, Pierre-Olivier Côté, Amanda Kolopanis, Rached Bouchoucha, Antonio Collante, Diego Elias Costa, Emad Shihab, Foutse Khomh:
Can Ensembling Pre-processing Algorithms Lead to Better Machine Learning Fairness? CoRR abs/2212.02614 (2022) - [i46]Roozbeh Aghili, Heng Li, Foutse Khomh:
Studying the Characteristics of AIOps Projects on GitHub. CoRR abs/2212.13245 (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]