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Amin Nikanjam
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2020 – today
- 2025
- [j32]Saeid Jamshidi, Kawser Wazed Nafi, Amin Nikanjam, Foutse Khomh:
Evaluating machine learning-driven intrusion detection systems in IoT: Performance and energy consumption. Comput. Ind. Eng. 204: 111103 (2025) - [j31]Paulina Stevia Nouwou Mindom, Amin Nikanjam, Foutse Khomh:
Harnessing pre-trained generalist agents for software engineering tasks. Empir. Softw. Eng. 30(1): 39 (2025) - [j30]Florian Tambon
, Arghavan Moradi Dakhel, Amin Nikanjam, Foutse Khomh, Michel C. Desmarais, Giuliano Antoniol:
Bugs in large language models generated code: an empirical study. Empir. Softw. Eng. 30(3): 65 (2025) - [j29]Saeid Jamshidi
Understanding the impact of IoT security patterns on CPU usage and energy consumption: a dynamic approach for selecting patterns with deep reinforcement learning. Int. J. Inf. Sec. 24(2): 91 (2025) - [j28]Saeid Jamshidi
Application of deep reinforcement learning for intrusion detection in Internet of Things: A systematic review. Internet Things 31: 101531 (2025) - [j27]Saeid Jamshidi
Self-adaptive cyber defense for sustainable IoT: A DRL-based IDS optimizing security and energy efficiency. J. Netw. Comput. Appl. 239: 104176 (2025) - [i38]Altaf Allah Abbassi, Léuson M. P. da Silva, Amin Nikanjam, Foutse Khomh:
Unveiling Inefficiencies in LLM-Generated Code: Toward a Comprehensive Taxonomy. CoRR abs/2503.06327 (2025) - [i37]Vahid Majdinasab, Amin Nikanjam, Foutse Khomh:
Prism: Dynamic and Flexible Benchmarking of LLMs Code Generation with Monte Carlo Tree Search. CoRR abs/2504.05500 (2025) - [i36]Saeid Jamshidi, Amin Nikanjam, Kawser Wazed Nafi, Foutse Khomh:
Leveraging Machine Learning Techniques in Intrusion Detection Systems for Internet of Things. CoRR abs/2504.07220 (2025) - [i35]Saeid Jamshidi, Kawser Wazed Nafi, Amin Nikanjam, Foutse Khomh:
Evaluating Machine Learning-Driven Intrusion Detection Systems in IoT: Performance and Energy Consumption. CoRR abs/2504.09634 (2025) - 2024
- [j26]Pierre-Olivier Côté, Amin Nikanjam, Nafisa Ahmed
Data cleaning and machine learning: a systematic literature review. Autom. Softw. Eng. 31(2): 54 (2024) - [j25]Florian Tambon
Silent bugs in deep learning frameworks: an empirical study of Keras and TensorFlow. Empir. Softw. Eng. 29(1): 10 (2024) - [j24]Mohammad Mehdi Morovati
Bug characterization in machine learning-based systems. Empir. Softw. Eng. 29(1): 14 (2024) - [j23]Mohammad Mehdi Morovati
Common challenges of deep reinforcement learning applications development: an empirical study. Empir. Softw. Eng. 29(4): 95 (2024) - [j22]Pierre-Olivier Côté
Quality issues in machine learning software systems. Empir. Softw. Eng. 29(6): 149 (2024) - [j21]Arghavan Moradi Dakhel
Effective test generation using pre-trained Large Language Models and mutation testing. Inf. Softw. Technol. 171: 107468 (2024) - [c18]Rached Bouchoucha, Ahmed Haj Yahmed, Darshan Patil, Janarthanan Rajendran, Amin Nikanjam, Sarath Chandar, Foutse Khomh:
Toward Debugging Deep Reinforcement Learning Programs with RLExplorer. ICSME 2024: 87-99 - [i34]Vahid Majdinasab, Amin Nikanjam
Trained Without My Consent: Detecting Code Inclusion In Language Models Trained on Code. CoRR abs/2402.09299 (2024) - [i33]Florian Tambon, Arghavan Moradi Dakhel, Amin Nikanjam
Bugs in Large Language Models Generated Code: An Empirical Study. CoRR abs/2403.08937 (2024) - [i32]Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, Victor Akinwande, Namir Al-Nuaimi, Najla Alfaraj, Elie Alhajjar, Lora Aroyo, Trupti Bavalatti, Borhane Blili-Hamelin, Kurt D. Bollacker, Rishi Bomassani, Marisa Ferrara Boston, Siméon Campos, Kal Chakra, Canyu Chen, Cody Coleman, Zacharie Delpierre Coudert, Leon Derczynski, Debojyoti Dutta, Ian Eisenberg, James Ezick, Heather Frase, Brian Fuller, Ram Gandikota, Agasthya Gangavarapu, Ananya Gangavarapu, James Gealy, Rajat Ghosh, James Goel, Usman Gohar, Subhra S. Goswami, Scott A. Hale, Wiebke Hutiri, Joseph Marvin Imperial, Surgan Jandial, Nick Judd
Introducing v0.5 of the AI Safety Benchmark from MLCommons. CoRR abs/2404.12241 (2024) - [i31]Vahid Majdinasab, Amin Nikanjam, Foutse Khomh:
DeepCodeProbe: Towards Understanding What Models Trained on Code Learn. CoRR abs/2407.08890 (2024) - [i30]Florian Tambon, Amin Nikanjam, Foutse Khomh, Giuliano Antoniol:
Assessing Programming Task Difficulty for Efficient Evaluation of Large Language Models. CoRR abs/2407.21227 (2024) - [i29]Rached Bouchoucha, Ahmed Haj Yahmed, Darshan Patil, Janarthanan Rajendran, Amin Nikanjam, Sarath Chandar, Foutse Khomh:
Toward Debugging Deep Reinforcement Learning Programs with RLExplorer. CoRR abs/2410.04322 (2024) - [i28]Mohammad Mehdi Morovati, Amin Nikanjam, Foutse Khomh:
Fault Localization in Deep Learning-based Software: A System-level Approach. CoRR abs/2411.08172 (2024) - [i27]Forough Majidi, Foutse Khomh, Heng Li, Amin Nikanjam:
An Efficient Model Maintenance Approach for MLOps. CoRR abs/2412.04657 (2024) - [i26]Paulina Stevia Nouwou Mindom, Léuson M. P. da Silva, Amin Nikanjam, Foutse Khomh:
Continuously Learning Bug Locations. CoRR abs/2412.11289 (2024) - 2023
- [j20]Mohammad Mehdi Morovati
Bugs in machine learning-based systems: a faultload benchmark. Empir. Softw. Eng. 28(3): 62 (2023) - [j19]Paulina Stevia Nouwou Mindom, Amin Nikanjam
A comparison of reinforcement learning frameworks for software testing tasks. Empir. Softw. Eng. 28(5): 111 (2023) - [j18]Arghavan Moradi Dakhel
GitHub Copilot AI pair programmer: Asset or Liability? J. Syst. Softw. 203: 111734 (2023) - [j17]Nazanin Shajoonnezhad
A stochastic variance-reduced coordinate descent algorithm for learning sparse Bayesian network from discrete high-dimensional data. Int. J. Mach. Learn. Cybern. 14(3): 947-958 (2023) - [c17]Ahmed Haj Yahmed, Altaf Allah Abbassi, Amin Nikanjam
Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challenges. ICSME 2023: 26-38 - [c16]Florian Tambon
Mutation Testing of Deep Reinforcement Learning Based on Real Faults. ICST 2023: 188-198 - [i25]Florian Tambon, Vahid Majdinasab, Amin Nikanjam
Mutation Testing of Deep Reinforcement Learning Based on Real Faults. CoRR abs/2301.05651 (2023) - [i24]Zeynab Chitsazian, Saeed Sedighian Kashi, Amin Nikanjam:
Detecting Concept Drift for the reliability prediction of Software Defects using Instance Interpretation. CoRR abs/2305.16323 (2023) - [i23]Pierre-Olivier Côté, Amin Nikanjam, Rached Bouchoucha, Ilan Basta, Mouna Abidi, Foutse Khomh:
Quality Issues in Machine Learning Software Systems. CoRR abs/2306.15007 (2023) - [i22]Mohammad Mehdi Morovati, Amin Nikanjam, Florian Tambon, Foutse Khomh, Zhen Ming Jiang:
Bug Characterization in Machine Learning-based Systems. CoRR abs/2307.14512 (2023) - [i21]Ahmed Haj Yahmed, Altaf Allah Abbassi, Amin Nikanjam
Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challenges. CoRR abs/2308.12438 (2023) - [i20]Arghavan Moradi Dakhel, Amin Nikanjam
Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing. CoRR abs/2308.16557 (2023) - [i19]Pierre-Olivier Côté, Amin Nikanjam
Data Cleaning and Machine Learning: A Systematic Literature Review. CoRR abs/2310.01765 (2023) - [i18]Mohammad Mehdi Morovati, Florian Tambon, Mina Taraghi, Amin Nikanjam
Common Challenges of Deep Reinforcement Learning Applications Development: An Empirical Study. CoRR abs/2310.09575 (2023) - [i17]Paulina Stevia Nouwou Mindom, Amin Nikanjam
Harnessing Pre-trained Generalist Agents for Software Engineering Tasks. CoRR abs/2312.15536 (2023) - 2022
- [j16]Amin Nikanjam
Faults in deep reinforcement learning programs: a taxonomy and a detection approach. Autom. Softw. Eng. 29(1): 8 (2022) - [j15]Florian Tambon
How to certify machine learning based safety-critical systems? A systematic literature review. Autom. Softw. Eng. 29(2): 38 (2022) - [j14]Mahnoosh Mahdavimoghadam, Amin Nikanjam
Improved reinforcement learning in cooperative multi-agent environments using knowledge transfer. J. Supercomput. 78(8): 10455-10479 (2022) - [j13]Amin Nikanjam
Automatic Fault Detection for Deep Learning Programs Using Graph Transformations. ACM Trans. Softw. Eng. Methodol. 31(1): 14:1-14:27 (2022) - [c15]Moses Openja, Amin Nikanjam
An Empirical Study of Challenges in Converting Deep Learning Models. ICSME 2022: 13-23 - [c14]Saumendu Roy, Gabriel Laberge, Banani Roy, Foutse Khomh, Amin Nikanjam
Why Don't XAI Techniques Agree? Characterizing the Disagreements Between Post-hoc Explanations of Defect Predictions. ICSME 2022: 444-448 - [i16]Saeed Ghadiri, Amin Nikanjam
Novel Metric based on Walsh Coefficients for measuring problem difficulty in Estimation of Distribution Algorithms. CoRR abs/2203.13195 (2022) - [i15]Mohammad Mehdi Morovati, Amin Nikanjam
Bugs in Machine Learning-based Systems: A Faultload Benchmark. CoRR abs/2206.12311 (2022) - [i14]Moses Openja, Amin Nikanjam
An Empirical Study of Challenges in Converting Deep Learning Models. CoRR abs/2206.14322 (2022) - [i13]Arghavan Moradi Dakhel, Vahid Majdinasab, Amin Nikanjam
GitHub Copilot AI pair programmer: Asset or Liability? CoRR abs/2206.15331 (2022) - [i12]Pierre-Olivier Côté, Amin Nikanjam
Quality issues in Machine Learning Software Systems. CoRR abs/2208.08982 (2022) - [i11]Paulina Stevia Nouwou Mindom, Amin Nikanjam, Foutse Khomh:
A Comparison of Reinforcement Learning Frameworks for Software Testing Tasks. CoRR abs/2208.12136 (2022) - 2021
- [j12]Mahsa Fozuni Shirjini, Amin Nikanjam
Stability analysis of the particle dynamics in bat algorithm: standard and modified versions. Eng. Comput. 37(4): 2865-2876 (2021) - [c13]Amin Nikanjam
Design Smells in Deep Learning Programs: An Empirical Study. ICSME 2021: 332-342 - [c12]Paulina Stevia Nouwou Mindom, Amin Nikanjam
On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods. QRS 2021: 260-269 - [c11]Emilio Rivera-Landos, Foutse Khomh, Amin Nikanjam
The Challenge of Reproducible ML: An Empirical Study on The Impact of Bugs. QRS 2021: 1079-1088 - [i10]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) - [i9]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) - [i8]Amin Nikanjam, Foutse Khomh:
Design Smells in Deep Learning Programs: An Empirical Study. CoRR abs/2107.02279 (2021) - [i7]Mahnoosh Mahdavimoghaddam, Amin Nikanjam, Monireh Abdoos:
Multi-agent Reinforcement Learning Improvement in a Dynamic Environment Using Knowledge Transfer. CoRR abs/2107.09807 (2021) - [i6]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) - [i5]Nazanin Shajoonnezhad, Amin Nikanjam:
A Sparse Structure Learning Algorithm for Bayesian Network Identification from Discrete High-Dimensional Data. CoRR abs/2108.09501 (2021) - [i4]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) - [i3]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) - [i2]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) - 2020
- [j11]Mahsa Fozuni Shirjini, Saeed Farzi
MDPCluster: a swarm-based community detection algorithm in large-scale graphs. Computing 102(4): 893-922 (2020) - [i1]Nader Zare, Mohsen Sadeghipour, Ashkan Keshavarzi, Mahtab Sarvmaili, Amin Nikanjam, Reza Aghayari, Arad Firouzkoohi, Mohammad Abolnejad, Sina Elahimanesh, Amin Akhgari:
Cyrus 2D Simulation Team Description Paper2018. CoRR abs/2008.03456 (2020)
2010 – 2019
- 2019
- [j10]Einollah Pira
Using evolutionary algorithms for reachability analysis of complex software systems specified through graph transformation. Reliab. Eng. Syst. Saf. 191 (2019) - 2018
- [j9]Einollah Pira
Searching for violation of safety and liveness properties using knowledge discovery in complex systems specified through graph transformations. Inf. Softw. Technol. 97: 110-134 (2018) - 2017
- [j8]Einollah Pira
Deadlock detection in complex software systems specified through graph transformation using Bayesian optimization algorithm. J. Syst. Softw. 131: 181-200 (2017) - [j7]Ali Keramatpour, Amin Nikanjam
Deployment of Wireless Intrusion Detection Systems to Provide the Most Possible Coverage in Wireless Sensor Networks Without Infrastructures. Wirel. Pers. Commun. 96(3): 3965-3978 (2017) - [c10]Alireza Saleh Sedghpour, Amin Nikanjam:
Overlapping community detection in social networks using a quantum-based genetic algorithm. GECCO (Companion) 2017: 197-198 - 2016
- [j6]Einollah Pira
EMCDM: Efficient model checking by data mining for verification of complex software systems specified through architectural styles. Appl. Soft Comput. 49: 1185-1201 (2016) - [c9]Amin Nikanjam
Multi-structure problems: Difficult model learning in discrete EDAs. CEC 2016: 3448-3454 - 2015
- [j5]Vahid Rafe, Maryam Moradi, Rosa Yousefian, Amin Nikanjam
A meta-heuristic solution for automated refutation of complex software systems specified through graph transformations. Appl. Soft Comput. 33: 136-149 (2015) - [j4]Vahid Rafe, Zahra Paiandeh, Amin Nikanjam
A hybrid optimization algorithm based on harmony search and differential evolution for continuous domain. J. Intell. Fuzzy Syst. 29(5): 2169-2176 (2015) - 2014
- [c8]Hadi Sharifi, Amin Nikanjam
Complexity of model learning in EDAs: multi-structure problems. GECCO (Companion) 2014: 55-56 - 2012
- [j3]Amin Nikanjam
Exploiting Bivariate Dependencies to Speedup Structure Learning in Bayesian Optimization Algorithm. J. Comput. Sci. Technol. 27(5): 1077-1090 (2012) - 2011
- [j2]Amin Nikanjam
Efficient model building in competent genetic algorithms using DSM clustering. AI Commun. 24(3): 213-231 (2011) - [j1]Vahid Rafe, Amin Nikanjam
Galoan: a multi-agent approach to herd cows. Ann. Math. Artif. Intell. 61(4): 333-348 (2011) - [c7]Hadi Sharifi, Amin Nikanjam
Interaction detection for hybrid decomposable problems. GECCO 2011: 1203-1210 - 2010
- [c6]Amin Nikanjam
Enhancing the efficiency of genetic algorithm by identifying linkage groups using DSM clustering. IEEE Congress on Evolutionary Computation 2010: 1-8 - [c5]Amin Nikanjam
A new DSM clustering algorithm for linkage groups identification. GECCO 2010: 367-368
2000 – 2009
- 2009
- [c4]Hossein Karshenas
Combinatorial effects of local structures and scoring metrics in bayesian optimization algorithm. GEC Summit 2009: 263-270 - 2008
- [c3]Mehdi Mohammadi, Amin Nikanjam
An Evolutionary Approach to Clustering Ensemble. ICNC (3) 2008: 77-82 - [c2]Adel Torkaman Rahmani, Alireza Saberi, Mehdi Mohammadi, Amin Nikanjam
SHABaN Multi-agent Team To Herd Cows. ProMAS 2008: 248-252 - 2006
- [c1]Amin Nikanjam
An anticipatory approach to improve XCSF. GECCO 2006: 1595-1596
Coauthor Index
[j32] [j31] [j30] [j29] [j28] [j27] [i38] [i37] [i36] [i35] [j26] [j25] [j24] [j23] [j22] [j21] [c18] [i34] [i33] [i32] [i31] [i30] [i29] [i28] [i27] [i26] [j20] [j19] [j18] [c17] [c16] [i25] [i23] [i22] [i21] [i20] [i19] [i18] [i17] [j16] [j15] [j13] [c15] [c14] [i15] [i14] [i13] [i12] [i11] [c13] [c12] [c11] [i10] [i9] [i8] [i6] [i4] [i3] [i2]
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