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BIGCODE SIGNED

Learning from Big Code: Probabilistic Models, Analysis and Synthesis

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EC-Contrib. €

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Project "BIGCODE" data sheet

The following table provides information about the project.

Coordinator
EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH 

Organization address
address: Raemistrasse 101
city: ZUERICH
postcode: 8092
website: https://www.ethz.ch/de.html

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Switzerland [CH]
 Project website http://plml.ethz.ch
 Total cost 1˙500˙000 €
 EC max contribution 1˙500˙000 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2015-STG
 Funding Scheme ERC-STG
 Starting year 2016
 Duration (year-month-day) from 2016-04-01   to  2021-03-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH CH (ZUERICH) coordinator 1˙500˙000.00

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 Project objective

The goal of this proposal is to fundamentally change the way we build and reason about software. We aim to develop new kinds of statistical programming systems that provide probabilistically likely solutions to tasks that are difficult or impossible to solve with traditional approaches.

These statistical programming systems will be based on probabilistic models of massive codebases (also known as ``Big Code') built via a combination of advanced programming languages and powerful machine learning and natural language processing techniques. To solve a particular challenge, a statistical programming system will query a probabilistic model, compute the most likely predictions, and present those to the developer.

Based on probabilistic models of ``Big Code', we propose to investigate new statistical techniques in the context of three fundamental research directions: i) statistical program synthesis where we develop techniques that automatically synthesize and predict new programs, ii) statistical prediction of program properties where we develop new techniques that can predict important facts (e.g., types) about programs, and iii) statistical translation of programs where we investigate new techniques for statistical translation of programs (e.g., from one programming language to another, or to a natural language).

We believe the research direction outlined in this interdisciplinary proposal opens a new and exciting area of computer science. This area will combine sophisticated statistical learning and advanced programming language techniques for building the next-generation statistical programming systems.

We expect the results of this proposal to have an immediate impact upon millions of developers worldwide, triggering a paradigm shift in the way tomorrow's software is built, as well as a long-lasting impact on scientific fields such as machine learning, natural language processing, programming languages and software engineering.

 Publications

year authors and title journal last update
List of publications.
2017 Pavol Bielik, Veselin Raychev, Martin Vechev
Learning a Static Analyzer from Data
published pages: 233-253, ISSN: , DOI: 10.1007/978-3-319-63387-9_12
Computer Aided Verification 2019-06-19
2018 Mislav Balunovic, Pavol Bielik, Martin Vechev
Learning to Solve SMT formulas
published pages: , ISSN: , DOI:
NeurIPS 2018 2019-06-19
2016 Pavol Bielik, Veselin Raychev, Martin Vechev
PHOG: Probabilistic Model for Code
published pages: 2933-2942, ISSN: , DOI:
Proceedings of The 33rd International Conference on Machine Learning 2019-06-19
2017 PAVOL BIELIK, VESELIN RAYCHEV, MARTIN VECHEV
Program Synthesis for Character Level Language Modeling
published pages: , ISSN: , DOI:
International Conference on Learning Representations 2019-05-15
2018 MATTHEW MIRMAN, TIMON GEHR, MARTIN VECHEV
Differentiable Abstract Interpretation for Provably Robust Neural Networks
published pages: , ISSN: , DOI:
Proceedings of the 35 th International Conference on Machine Learning 2019-05-15
2018 Pavol Bielik, Marc Fischer, Martin Vechev
Robust relational layout synthesis from examples for Android
published pages: 1-29, ISSN: 2475-1421, DOI: 10.1145/3276526
Proceedings of the ACM on Programming Languages 2/OOPSLA 2019-05-15

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The information about "BIGCODE" are provided by the European Opendata Portal: CORDIS opendata.

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