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Gopi Krishnan Rajbahadur
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2020 – today
- 2024
- [j9]Hao Li
, Gopi Krishnan Rajbahadur
Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting. IEEE Access 12: 70676-70689 (2024) - [c8]Dayi Lin
Technical Brief on Software Engineering for FMware. ICSE Companion 2024: 431-433 - [c7]Ahmed E. Hassan
Rethinking Software Engineering in the Era of Foundation Models: A Curated Catalogue of Challenges in the Development of Trustworthy FMware. SIGSOFT FSE Companion 2024: 294-305 - [c6]Filipe Roseiro Côgo
A Tutorial on Software Engineering for FMware. SIGSOFT FSE Companion 2024: 710-712 - [i18]Hao Li, Gopi Krishnan Rajbahadur, Dayi Lin, Cor-Paul Bezemer, Zhen Ming Jiang:
Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting. CoRR abs/2401.10359 (2024) - [i17]Ahmed E. Hassan, Dayi Lin, Gopi Krishnan Rajbahadur, Keheliya Gallaba, Filipe Roseiro Côgo, Boyuan Chen, Haoxiang Zhang, Kishanthan Thangarajah, Gustavo Ansaldi Oliva, Jiahuei Lin, Wali Mohammad Abdullah, Zhen Ming Jiang:
Rethinking Software Engineering in the Foundation Model Era: A Curated Catalogue of Challenges in the Development of Trustworthy FMware. CoRR abs/2402.15943 (2024) - [i16]Hao Li, Gopi Krishnan Rajbahadur, Cor-Paul Bezemer:
Studying the Impact of TensorFlow and PyTorch Bindings on Machine Learning Software Quality. CoRR abs/2407.05466 (2024) - [i15]Xu Yang, Gopi Krishnan Rajbahadur, Dayi Lin, Shaowei Wang, Zhen Ming Jiang:
SimClone: Detecting Tabular Data Clones using Value Similarity. CoRR abs/2407.12802 (2024) - [i14]Aaditya Bhatia, Dayi Lin, Gopi Krishnan Rajbahadur, Bram Adams, Ahmed E. Hassan:
Data Quality Antipatterns for Software Analytics. CoRR abs/2408.12560 (2024) - [i13]Ximing Dong, Shaowei Wang, Dayi Lin, Gopi Krishnan Rajbahadur, Boquan Zhou, Shichao Liu, Ahmed E. Hassan:
PromptExp: Multi-granularity Prompt Explanation of Large Language Models. CoRR abs/2410.13073 (2024) - [i12]Gopi Krishnan Rajbahadur, Gustavo Ansaldi Oliva, Dayi Lin, Ahmed E. Hassan:
From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap. CoRR abs/2410.20791 (2024) - 2023
- [j8]Michael Pacheco
What makes Ethereum blockchain transactions be processed fast or slow? An empirical study. Empir. Softw. Eng. 28(2): 39 (2023) - [j7]Michael Pacheco
Is My Transaction Done Yet? An Empirical Study of Transaction Processing Times in the Ethereum Blockchain Platform. ACM Trans. Softw. Eng. Methodol. 32(3): 59:1-59:46 (2023) - [c5]Hiroki Yamamoto, Dong Wang, Gopi Krishnan Rajbahadur, Masanari Kondo, Yasutaka Kamei, Naoyasu Ubayashi:
Towards Privacy Preserving Cross Project Defect Prediction with Federated Learning. SANER 2023: 485-496 - 2022
- [j6]Ankur Tagra, Haoxiang Zhang
Revisiting reopened bugs in open source software systems. Empir. Softw. Eng. 27(4): 92 (2022) - [j5]Yingzhe Lyu, Gopi Krishnan Rajbahadur
Towards a Consistent Interpretation of AIOps Models. ACM Trans. Softw. Eng. Methodol. 31(1): 16:1-16:38 (2022) - [j4]Gopi Krishnan Rajbahadur
The Impact of Feature Importance Methods on the Interpretation of Defect Classifiers. IEEE Trans. Software Eng. 48(7): 2245-2261 (2022) - [j3]Lina Gong
Revisiting the Impact of Dependency Network Metrics on Software Defect Prediction. IEEE Trans. Software Eng. 48(12): 5030-5049 (2022) - [c4]Boyuan Chen, Mingzhi Wen, Yong Shi, Dayi Lin, Gopi Krishnan Rajbahadur, Zhen Ming Jiang:
Towards Training Reproducible Deep Learning Models. ICSE 2022: 2202-2214 - [i11]Yingzhe Lyu, Gopi Krishnan Rajbahadur, Dayi Lin, Boyuan Chen, Zhen Ming Jiang:
Towards a consistent interpretation of AIOps models. CoRR abs/2202.02298 (2022) - [i10]Boyuan Chen, Mingzhi Wen, Yong Shi, Dayi Lin, Gopi Krishnan Rajbahadur, Zhen Ming Jiang:
Towards Training Reproducible Deep Learning Models. CoRR abs/2202.02326 (2022) - [i9]Gopi Krishnan Rajbahadur, Shaowei Wang, Yasutaka Kamei, Ahmed E. Hassan:
The impact of feature importance methods on the interpretation of defect classifiers. CoRR abs/2202.02389 (2022) - [i8]Lina Gong, Gopi Krishnan Rajbahadur, Ahmed E. Hassan, Shujuan Jiang:
Revisiting the Impact of Dependency Network Metrics on Software Defect Prediction. CoRR abs/2202.06145 (2022) - [i7]Gopi Krishnan Rajbahadur, Shaowei Wang, Yasutaka Kamei, Ahmed E. Hassan:
Impact of Discretization Noise of the Dependent variable on Machine Learning Classifiers in Software Engineering. CoRR abs/2202.06146 (2022) - [i6]Gopi Krishnan Rajbahadur, Shaowei Wang, Yasutaka Kamei, Ahmed E. Hassan:
The Impact of Using Regression Models to Build Defect Classifiers. CoRR abs/2202.06157 (2022) - [i5]Ankur Tagra, Haoxiang Zhang, Gopi Krishnan Rajbahadur, Ahmed E. Hassan:
Revisiting reopened bugs in open source software systems. CoRR abs/2202.08701 (2022) - [i4]Michael Pacheco, Gustavo Ansaldi Oliva, Gopi Krishnan Rajbahadur, Ahmed E. Hassan:
What makes Ethereum blockchain transactions be processed fast or slow? An empirical study. CoRR abs/2206.08905 (2022) - [i3]Michael Pacheco, Gustavo Ansaldi Oliva, Gopi Krishnan Rajbahadur, Ahmed E. Hassan:
Is my transaction done yet? An empirical study of transaction processing times in the Ethereum Blockchain Platform. CoRR abs/2206.08959 (2022) - 2021
- [j2]Gopi Krishnan Rajbahadur
Impact of Discretization Noise of the Dependent Variable on Machine Learning Classifiers in Software Engineering. IEEE Trans. Software Eng. 47(7): 1414-1430 (2021) - [i2]Daniel Lee, Gopi Krishnan Rajbahadur, Dayi Lin, Mohammed Sayagh, Cor-Paul Bezemer, Ahmed E. Hassan:
An Empirical Study of the Characteristics of Popular Minecraft Mods. CoRR abs/2103.14439 (2021) - [i1]Gopi Krishnan Rajbahadur, Erika Tuck, Li Zi, Zhang Wei, Dayi Lin, Boyuan Chen, Zhen Ming Jiang, Daniel Morales German:
Can I use this publicly available dataset to build commercial AI software? Most likely not. CoRR abs/2111.02374 (2021) - 2020
- [b1]Gopi Krishnan Rajbahadur:
Understanding the Impact of Experimental Design Choices on Machine Learning Classifiers in Software Analytics. Queen's University at Kingston, Ontario, Canada, 2020 - [j1]Daniel Lee, Gopi Krishnan Rajbahadur
An empirical study of the characteristics of popular Minecraft mods. Empir. Softw. Eng. 25(5): 3396-3429 (2020)
2010 – 2019
- 2019
- [c3]Gopi Krishnan Rajbahadur, Gustavo Ansaldi Oliva, Ahmed E. Hassan, Juergen Dingel:
Pitfalls Analyzer: Quality Control for Model-Driven Data Science Pipelines. MoDELS 2019: 12-22 - 2018
- [c2]Gopi Krishnan Rajbahadur, Andrew J. Malton, Andrew Walenstein, Ahmed E. Hassan:
A Survey of Anomaly Detection for Connected Vehicle Cybersecurity and Safety. Intelligent Vehicles Symposium 2018: 421-426 - 2017
- [c1]Gopi Krishnan Rajbahadur
The impact of using regression models to build defect classifiers. MSR 2017: 135-145
Coauthor Index
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last updated on 2024-12-01 00:15 CET by the dblp team
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