Параметры обучения:
- Факультет вычислительной математики и кибернетики
- Направление: 09.06.01 Информатика и вычислительная техника
- Специальность: 05.13.11 Математическое и программное обеспечение вычислительных машин, комплексов и компьютерных сетей
- Тема диссертационной работы: Методы статического анализа и оптимизирующих преобразований в компиляторе динамически-типизированного языка
Useful links:
Literature:
- Abstract Interpretation: A Unified Lattice Model for Static Analysis of Programs by Construction or Approximation of Fixpoints, Cousot, P. and R. Cousot
- Adaptive Optimization for Self: reconciling high performance with exploratory programming, Urz Holzle, Ph.D. thesis, Stanford University
- Concrete type inference: delivering object-oriented applications. Ole Agesen.
- Code Generation On the Fly: A Key to Portable Software, Michael Steffen Oliver Franz, Swiss Federal Institute of Technology Zurich
- The Design and Evaluation ohttp://www.techort.com/wp-content/uploads/2017/09/1506611720_568_v8-under-the-hood-blog-company-jug-ru-group-habrahabr.jpegf A High Performance Samlltalk System, David Michael Ungar, Ph. D. thesis, University of California, Berkeley
- Static Analysis of Dynamic Languages, Magnus Mdsen, Ph. D. thesis, Aarhus University
- Improved Type Specialization for Dynamic Scripting Languages, Madhukar N Kedlaya et. al
- Fast and Precise Hybrid Type Inference for JavaScript, Brian Hackett, Shu-yu Guo, Mozilla
- Type Feedback vs. Concrete Type Inference: A Comparison of Optimization Techniques for Object-Oriented Languages, Ole Agesen, Urs Holzle
- Constraint-Based Type Inference and Parametric Polymorphism, Ole Agesen
- The Cartesian Product Algorithm. Simple and Precise Type Inference of Parametric Polymorphism, Ole Agesen
- A Simple Graph-Based Intermediate Representation, Cliff Click, Michael Paleczny
- “Basic Polymorphic Typechecking” by Luca Cardelli
- “Sifting Out the Gold. Delivering Compact Applications from an Exploratory Object-Oriented Programming Environment”, Ole Agesen, David Ungar
- Object-Oriented Type Inference, Jens Palsberg and Michael I. Schwartzback
- Optimizing Dynamically-Dispatched Calls with Run-Time Type Feedback, Urs Holzle and David Ungar
- Type analysis for JavaScript, Simon Holm Jensen1, Anders Møller1, Peter Thiemann
- Incremental Inference of Concrete Types, Plevyak
- Type Inference of SELF Analysis of Objects with Dynamic and Multiple Inheritance, Ole Agesen, Jens Palsberg, Michael I. Schwartzbach
- Making Type Inference Practical, Nicholas Oxhoj, Jens Palsberg, Michael I. Schwartzbach
- “DeepFix: Fixing Common C Language Errors by Deep Learning” by Rahul Gupta, Soham Pal, Aditya Kanade, Shirish Shevade
- “Learning Programs from Noisy Data” by Veselin Raychev, Pavol Bielik, Martin Vechev and Andreas Krause
- “Automatic Patch Generation by Learning Correct Code” by Fan Long and Martin Rinard
- The Strength of Random Search on Automated Program Repair, Yuhua Qi , Xiaoguang Mao , Yan Lei, Ziying Dai and Chengsong Wang
- History Driven Program Repair, Xuan-Bach D. Le, David Lo, Claire Le Goues
- Exploring Machine Learning Techniques for Fault Localization, Luciano C. Ascari, Lucilia Y. Araki, Aurora R.T. Pozo, Silvia R. Vergilio