Ahmed E. Hassan

Information Systems · IEEE Transactions on Software Engineering · 2008–2018

A researcher in the Science corpus: 13 papers; trusts 111 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 176 citations into it; #240 of 8,771 by trust here, #1,220 by citations
• Software — 136 citations into it; #32 of 1,227 by trust here, #175 by citations
• Artificial Intelligence — 74 citations into it; #22,048 of 35,345 by trust here, #27,551 by citations
• Computer Networks and Communications — 60 citations into it; #7,420 of 9,886 by trust here, #7,492 by citations
• Statistics and Probability — 34 citations into it
• Evaluation & Benchmarks — 19 citations into it
• Computer Science Applications — 18 citations into it; #31 of 1,855 by trust here, #634 by citations
• Analytical Chemistry — 9 citations into it

Whom Ahmed E. Hassan trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Max Kuhn
2. Ewout W. Steyerberg
3. N. Nagappan
4. Martin J. Shepperd
5. Audris Mockus
6. Kaspar Rufibach
7. Marco D'Ambros
8. Shane McIntosh
9. Chakkrit Kla Tantithamthavorn
10. Alan F. Karr

Who trusts Ahmed E. Hassan overall

1. Shane McIntosh
2. Yasutaka Kamei
3. Chakkrit Kla Tantithamthavorn
4. Jaechang Nam
5. Marco D'Ambros
6. Foyzur Rahman
7. Song Wang
8. Baljinder Ghotra
9. Bram Adams
10. Tien-Duy B. Le

Papers from Ahmed E. Hassan's perspective

Ordered by how strongly the researchers Ahmed E. Hassan trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Random Forests
4. Bagging Predictors
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. The Elements of Statistical Learning
7. Predicting Fault Incidence Using Software Change History (IEEE Trans. Software Eng.)
8. Predicting the location and number of faults in large software systems (IEEE Transactions on Software Engineering)
9. Gradient-based learning applied to document recognition
10. Et al

Their papers

• What Do Mobile App Users Complain About? (2015) — 430 citations
• Measuring Program Comprehension: A Large-Scale Field Study with Professionals (2018) — 346 citations
• The Impact of Automated Parameter Optimization on Defect Prediction Models (2018) — 386 citations
• The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models (2018) — 295 citations
• An Empirical Comparison of Model Validation Techniques for Defect Prediction Models (2017) — 512 citations
• Automated Parameter Optimization of Classification Techniques for Defect Prediction Models (2016) — 353 citations
• An empirical study of the impact of modern code review practices on software quality (2015) — 356 citations
• Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models (2015) — 418 citations
• What are developers talking about? An analysis of topics and trends in Stack Overflow (2014) — 617 citations
• The impact of code review coverage and code review participation on software quality: a case study of the qt, VTK, and ITK projects (2014) — 342 citations
• A large-scale empirical study of just-in-time quality assurance (2013) — 694 citations
• Predicting faults using the complexity of code changes (2009) — 722 citations
• On mapping fracture networks onto continuum (2008) — 72 citations