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Intro
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Classroom Contents
Deep Learning and Software Engineering - A Retrospective and Paths Forward
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- 1 Intro
- 2 Talk Outline
- 3 What is Machine Learning?
- 4 The Hierarchy of Artificial Intelligence
- 5 Machine Learning Taxonomy
- 6 ML Representations
- 7 Machine Learning vs. Traditional Programming
- 8 When do We Need Machine Learning?
- 9 The Computational Learning Process
- 10 Supervised ML Applied to Image Classificatio
- 11 The Five Elements of the Learning Process
- 12 Feature Engineering for "Canonical" Machine Learnin
- 13 "Canonical" ML Image Classification
- 14 Shortcomings of Traditional ML Techniques
- 15 The Advent of Deep Learning
- 16 Neurons: The Building Blocks of Rich Features
- 17 Neural Networks for Rich Embeddings
- 18 Automated Feature Discovery
- 19 How Can a Model Learn from Deep Embedding
- 20 CNN-Accuracy
- 21 Deep Learning Advantages and Drawbacks
- 22 Mining Software Repositories
- 23 Automation in Software Engineering Research
- 24 Systematic Literature Review
- 25 SLR Search Process
- 26 Publication Distribution By Venue
- 27 Data Processing Techniques by SE Task
- 28 DL4SE Neural Network Architectures
- 29 DLUSE Techniques to Combat Overfitting
- 30 DL4SE Benchmarks
- 31 Consideration of Occam's Razor
- 32 Non-Reproducibility Factors
- 33 Resulting Guidelines
- 34 Future Research Directions in DL4SE (cont'd)
- 35 Ethical and Social Considerations of DL4SE
- 36 HCI Aspects of Al-Assisted Developer Tools
- 37 New Application Areas and Data-Sources
- 38 Combining Empirical Knowledge with Deep Learning
- 39 Software 1.0 vs. Software 2.0
- 40 Software 2.0 = DL-based systems
- 41 Optimization by Gradient Descent to Find The Progra
- 42 Real-world DL-based System (Software 2.0)
- 43 The Transition to Software 2.0
- 44 Traditional SE Development vs. DL Developmer
- 45 Challenges: Software Development for DL
- 46 Challenges: Software Maintenance for DL
- 47 Challenges: Testing for DL
- 48 Challenges: Debugging for DL
- 49 Challenges: DL Deployment
- 50 What are the Next Steps?
