| On the Value of Oversampling for Deep Learning in Software Defect Prediction R Yedida, T Menzies IEEE Transactions on Software Engineering, 2021 | 64 | 2021 |
| LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence R Yedida, S Saha, T Prashanth Applied Intelligence, 1-19, 2020 | 59* | 2020 |
| Employee Attrition Prediction R Yedida, R Reddy, R Vahi, R Jana, A GV, D Kulkarni arXiv preprint arXiv:1806.10480, 2018 | 42 | 2018 |
| Learning to recognize actionable static code warnings (is intrinsically easy) X Yang, J Chen, R Yedida, Z Yu, T Menzies Empirical Software Engineering 26 (3), 1-24, 2021 | 40* | 2021 |
| Evolution of novel activation functions in neural network training for astronomy data: habitability classification of exoplanets S Saha, N Nagaraj, A Mathur, R Yedida, S HR The European Physical Journal Special Topics 229 (16), 2629-2738, 2020 | 39 | 2020 |
| Simpler hyperparameter optimization for software analytics: Why, how, when? A Agrawal, X Yang, R Agrawal, R Yedida, X Shen, T Menzies IEEE Transactions on Software Engineering 48 (8), 2939-2954, 2021 | 26 | 2021 |
| How to find actionable static analysis warnings: A case study with FindBugs R Yedida, HJ Kang, H Tu, X Yang, D Lo, T Menzies IEEE Transactions on Software Engineering 49 (4), 2856-2872, 2023 | 18 | 2023 |
| How to improve deep learning for software analytics: (a case study with code smell detection) R Yedida, T Menzies Proceedings of the 19th International Conference on Mining Software …, 2022 | 16 | 2022 |
| An expert system for redesigning software for cloud applications R Yedida, R Krishna, A Kalia, T Menzies, J Xiao, M Vukovic Expert Systems with Applications, 2023 | 14* | 2023 |
| Old but Gold: Reconsidering the value of feedforward learners for software analytics R Yedida, X Yang, T Menzies arXiv preprint arXiv:2101.06319, 2021 | 11* | 2021 |
| Lessons learned from hyper-parameter tuning for microservice candidate identification R Yedida, R Krishna, A Kalia, T Menzies, J Xiao, M Vukovic 2021 36th IEEE/ACM International Conference on Automated Software …, 2021 | 8 | 2021 |
| Parsimonious Computing: A Minority Training Regime for Effective Prediction in Large Microarray Expression Data Sets S Sridhar, S Saha, A Shaikh, R Yedida, S Saha 2020 International Joint Conference on Neural Networks (IJCNN), 1-8, 2020 | 8 | 2020 |
| Beginning with machine learning: a comprehensive primer R Yedida, S Saha The European Physical Journal Special Topics 230 (10), 2363-2444, 2021 | 6 | 2021 |
| (Re) Use of Research Results (Is Rampant) MT Baldassarre, N Ernst, B Hermann, T Menzies, R Yedida Communications of the ACM 66 (2), 75-81, 2023 | 5* | 2023 |
| Finding a good learning rate R Yedida Blog post, 2019 | 2 | 2019 |
| Documenting evidence of a reuse of ‘a systematic study of the class imbalance problem in convolutional neural networks’ R Yedida, T Menzies Proceedings of the 29th ACM Joint Meeting on European Software Engineering …, 2021 | 1 | 2021 |
| Workshop on Replications and Negative Results R Yedida, T Menzies | | 2024 |
| Guidelines for the Application of Neural Technologies in Software Analytics (or: How to Do More with Less in SE). R Yedida | | 2024 |
| Strong convexity-guided hyper-parameter optimization for flatter losses R Yedida, S Saha arXiv preprint arXiv:2402.05025, 2024 | | 2024 |
| Is Hyper-Parameter Optimization Different for Software Analytics? R Yedida, T Menzies arXiv preprint arXiv:2401.09622, 2024 | | 2024 |
Tim MenziesProfessor, North Carolina State University, Computer Science, ACM Fellow, IEEE Fellow, ASE FellowEmail verificata su ncsu.edu
