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LearnBugs SIGNED

Learning to Find Software Bugs

Total Cost €

EC-Contrib. €

Partnership

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LearnBugs project word cloud

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

life patterns ultimately intersection humans fundamental training contains society programs train software automatically automated instead revolutionize bugs modern detection realize vision millions successful amounts engineering search developers domains reliability efficiency create trained security errors examples radically machine programming learns economy recur replace framework detectors feasible reusable tools though date trivial idea languages suitable created distinguish experts manually democratize model written models disastrous buggy detector share learning core full pattern cornerstone instances bug people code few unleash data language techniques

Project "LearnBugs" data sheet

The following table provides information about the project.

Coordinator	UNIVERSITAET STUTTGART Organization address address: KEPLERSTRASSE 7 city: STUTTGART postcode: 70174 website: www.uni-stuttgart.de 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	Germany [DE]
Total cost	1˙458˙375 €
EC max contribution	1˙458˙375 € (100%)
Programme	1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
Code Call	ERC-2019-STG
Funding Scheme	ERC-STG
Starting year	2020
Duration (year-month-day)	from 2020-03-01 to 2025-02-28

Partnership

Take a look of project's partnership.

#	participants	country	role	EC contrib. [€]
1	UNIVERSITAET STUTTGART UNIVERSITAET STUTTGART Organization address address: KEPLERSTRASSE 7 city: STUTTGART postcode: 70174 website: www.uni-stuttgart.de contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	DE (STUTTGART)	coordinator	1˙458˙375.00

Map

Project objective

'Learning to Find Software Bugs

Software has become the cornerstone of modern society, economy, and life. Since software is created by humans, though, every non-trivial program contains various bugs, i.e., programming errors that may have disastrous consequences. Traditional approaches to find bugs include automated bug detection tools. Such tools search for instances of bug patterns that recur across projects and application domains. However, automated bug detection currently cannot unleash its full potential because each bug detector addresses one bug pattern and one programming language, while creating new bug detectors is feasible only for program analysis experts.

The objective of this proposal is to radically change the way automated bug detection tools are created. The core idea is to replace manually written program analyses with trained machine learning models. To this end, developers will train a bug detector for a particular bug pattern with examples of buggy and non-buggy code, which the model learns to distinguish. The project will realize this vision by developing a reusable framework that addresses several fundamental challenges at the intersection of software engineering, programming languages, and machine learning, e.g.: (i) How to support developers in creating large amounts of training data of buggy and non-buggy code examples? (ii) How to represent programs in a way suitable for advanced machine learning techniques?

The proposed project has the potential to revolutionize how software developers find bugs. To date, no other research has addressed the problem of automatically learning bug detection tools. If successful, the project will 'democratize' bug detection by enabling all software developers, instead of a few program analysis experts, to create and share bug detection tools. Ultimately, the project will contribute to increasing the reliability, security, and efficiency of complex software systems used by millions of people.'

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