
Open access
Date
2015Type
- Conference Paper
ETH Bibliography
yes
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Abstract
Programming tools based on probabilistic models of massive codebases (aka "Big Code") promise to solve important programming tasks that were difficult or practically infeasible to address before. However, building such tools requires solving a number of hard problems at the intersection of programming languages, program analysis and machine learning. In this paper we summarize some of our experiences and insights obtained by developing several such probabilistic systems over the last few years (some of these systems are regularly used by thousands of developers worldwide). We hope these observations can provide a guideline for others attempting to create such systems. We also present a prediction approach we find suitable as a starting point for building probabilistic tools, and discuss a practical framework implementing this approach, called Nice2Predict. We release the Nice2Predict framework publicly - the framework can be immediately used as a basis for developing new probabilistic tools. Finally, we present programming applications that we believe will benefit from probabilistic models and should be investigated further. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000110465Publication status
publishedExternal links
Book title
1st Summit on Advances in Programming Languages (SNAPL 2015)Journal / series
Leibniz International Proceedings in Informatics (LIPIcs)Volume
Pages / Article No.
Publisher
Schloss Dagstuhl – Leibniz-Zentrum für InformatikEvent
Subject
Probabilistic tools; Probabilistic inference and learning; Program analysis; Open-source softwareOrganisational unit
03948 - Vechev, Martin / Vechev, Martin
03948 - Vechev, Martin / Vechev, Martin
Related publications and datasets
Is cited by: https://doi.org/10.3929/ethz-b-000498126
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ETH Bibliography
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