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Learning from Big Code: Probabilistic Models, Analysis and Synthesis

Project description

Software solutions to unsolvable challenges

In a bid to tackle long-standing software challenges, the BIGCODE project, funded by the European Research Council, aims to develop revolutionary statistical programming systems. Specifically, it will provide developers with probabilistically likely solutions to tasks that have proven difficult or impossible to solve using traditional approaches. By harnessing the power of massive codebases, advanced programming languages, and cutting-edge machine learning and natural language processing techniques, the project will delve into three fundamental research directions: statistical programme synthesis, prediction of programme properties, and statistical translation of programmes. By merging sophisticated statistical learning and advanced programming language techniques, this interdisciplinary proposal will develop the next-generation statistical programming systems.

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.

Fields of science (EuroSciVoc)

Programme(s)

Topic(s)

Funding Scheme

ERC-STG - Starting Grant

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Call for proposal

(opens in new window) ERC-2015-STG

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Host institution

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH
Net EU contribution
€ 1 500 000,00
Address
Raemistrasse 101
8092 Zuerich
Switzerland
Region
Schweiz/Suisse/Svizzera Zürich Zürich
Activity type
Higher or Secondary Education Establishments
Links
Total cost
€ 1 500 000,00

Beneficiaries (1)