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BIGCODE SIGNED

Learning from Big Code: Probabilistic Models, Analysis and Synthesis

Total Cost €

EC-Contrib. €

Partnership

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Outcomes and
results

BIGCODE project word cloud

Explore the words cloud of the BIGCODE project. It provides you a very rough idea of what is the project "BIGCODE" about.

prediction powerful fundamentally shift software worldwide opens model combination statistical automatically facts predict developers generation directions massive building predictions techniques outlined codebases science translation impossible programming query scientific big tomorrow machine language sophisticated types paradigm compute probabilistic models programs fundamental computer languages millions synthesize natural area kinds interdisciplinary context probabilistically learning code difficult believe developer combine solutions synthesis engineering lasting direction

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 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.	CH (ZUERICH)	coordinator	1˙500˙000.00

Map

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

List of publications.
year	authors and title	journal	last update
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.