Shane McIntosh

Information Systems · 2014–2018

A researcher in the Science corpus: 6 papers; trusts 83 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 120 citations into it; #1,877 of 8,771 by trust here, #2,821 by citations
• Software — 99 citations into it; #254 of 1,227 by trust here, #315 by citations
• Artificial Intelligence — 48 citations into it
• Computer Networks and Communications — 37 citations into it; #6,352 of 9,886 by trust here, #7,492 by citations
• Statistics and Probability — 23 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Computer Science Applications — 12 citations into it; #1,779 of 1,855 by trust here, #1,387 by citations
• Analytical Chemistry — 9 citations into it

Whom Shane McIntosh trusts overall

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

1. Ahmed E. Hassan
2. Max Kuhn
3. N. Nagappan
4. Audris Mockus
5. Martin J. Shepperd
6. Eric R. Ziegel
7. Marco D'Ambros
8. Sunghun Kim
9. Ewout W. Steyerberg
10. Christian Bird

Who trusts Shane McIntosh overall

1. Chakkrit Kla Tantithamthavorn
2. Ahmed E. Hassan
3. Ken-ichi Matsumoto
4. Vincent J. Hellendoorn
5. Jaechang Nam
6. Wei Fu
7. Premkumar T. Devanbu
8. Yasutaka Kamei

Papers from Shane McIntosh's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Predicting the location and number of faults in large software systems (IEEE Transactions on Software Engineering)
4. Bagging Predictors
5. Predicting Fault Incidence Using Software Change History (IEEE Trans. Software Eng.)
6. Random Forests
7. The Elements of Statistical Learning
8. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
9. Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings (IEEE Transactions on Software Engineering)
10. Mining metrics to predict component failures (International Conference on Software Engineering)

Their papers

• The Impact of Automated Parameter Optimization on Defect Prediction Models (2018) — 386 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
• 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