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Title: Resources on Machine Learning for Big Code and Naturalness · Machine Learning for Big Code and Naturalness

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direct link

Domain: ml4code.github.io

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Links:

Contribute to ML4Codehttps://ml4code.github.io/contributing.html
Machine Learning for Big Code and Naturalness https://ml4code.github.io/
List of Papershttps://ml4code.github.io/papers.html
Papers by Taghttps://ml4code.github.io/tags.html
2D Map of Papershttps://ml4code.github.io/tsne-viz.html
Topic-based Explorerhttps://ml4code.github.io/topic-viz.html
Resources, Courses & Eventshttps://ml4code.github.io/resources.html
Contributinghttps://ml4code.github.io/contributing.html
Miltos Allamanishttps://miltos.allamanis.com
Jekyllhttps://jekyllrb.com
Hydehttps://github.com/poole/hyde
Tutorial: An Introduction to Learning from Programshttp://vmcaischool19.tecnico.ulisboa.pt/
slideshttp://vmcaischool19.tecnico.ulisboa.pt/~vmcaischool19.daemon/wp/wordpress/wp-content/uploads/2019/01/Learning_from_Programs.pptx
Tutorial: Modelling Natural Language, Programs, and their Intersectionhttp://naacl2018.org/tutorial.html
slideshttps://github.com/neubig/naacl18tutorial
videohttps://vimeo.com/channels/naacl2018/279154278
learnbigcode.github.iohttp://learnbigcode.github.io
surveyhttps://arxiv.org/abs/1709.06182
learnbigcode.github.iohttp://learnbigcode.github.io/datasets/
Analyzing Software using Deep Learninghttp://software-lab.org/teaching/summer2020/asdl/
videoshttps://www.youtube.com/playlist?list=PLBmY8PAxzwIHIKq4tYLws25KqGvUM4iFD
Seminars on Applications of Deep Learning in Software Engineering and Programming Languageshttps://sites.google.com/view/mlplse-sp18/
Machine learning for programminghttps://www.cl.cam.ac.uk/teaching/1920/P252/
Deep Learning for Symbolic Reasoninghttp://tiarkrompf.github.io/cs590/2018/
Machine Learning for Software Engineeringhttp://gousios.org/courses/ml4se/
Deep Learning for Codehttps://dl4c.github.io
NLP4Prog Workshophttps://nlp4prog.github.io/2021/
Workshop on Computer-Assisted Programminghttps://capworkshop.github.io/
ML on Code devroom at FOSDEM19https://fosdem.org/2019/schedule/track/ml_on_code/
videoshttps://video.fosdem.org/2019/H.2213/
Machine Learning for Programminghttp://ml4p.org/
videoshttps://www.youtube.com/watch?v=dQaAp9wdFtQ&list=PLMPy362FkW9pd96bwh0BuCGMo6fdMQ2aw
International Workshop on Machine Learning techniques for Programming Languageshttps://conf.researchr.org/track/ecoop-issta-2018/ML4PL-2018-papers
Workshop on Machine Learning and Programming Languageshttps://pldi18.sigplan.org/track/mapl-2018-papers
Workshop on NLP for Software Engineeringhttps://nl4se.github.io/
The 55th CREST Open Workshop - Bimodal Program Analysishttp://crest.cs.ucl.ac.uk/cow/55/
Workshop on NLP for Software Engineeringhttps://nlse-fse.github.io/
Programming with “Big Code”http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=15472
Sofware Analysishttp://rightingcode.org/
videoshttps://www.youtube.com/playlist?list=PLF3-CvSRq2SaApl3Lnu6Tu_ecsBr94543
Program Analysishttps://software-lab.org/teaching/winter2020/pa/
videoshttps://www.youtube.com/playlist?list=PLBmY8PAxzwIEGtnJiucyGAnwWpxACE633
Applications of Data Science for Software Engineering 2020https://www.youtube.com/watch?v=34hcH7Js41I&list=PLmAXH4O57P5_0IflYjLIg8l0IupZPbdlY
nlc2cmdhttp://nlc2cmd.us-east.mybluemix.net/#/
CodeSearchNet Challenge: Evaluating the State of Semantic Code Searchhttps://github.com/github/CodeSearchNet
CodRep 2019: Machine Learning on Source Code Competitionhttps://github.com/KTH/codrep-2019
CodRep 2018: Machine Learning on Source Code Competitionhttps://github.com/KTH/CodRep-competition
source{d}https://sourced.tech/
herehttps://github.com/src-d/awesome-machine-learning-on-source-code
Autormated Program Repairhttps://www.monperrus.net/martin/automatic-software-repair
Martin Monperrushttps://www.monperrus.net/martin/

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