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MSR 2019
Sun 26 - Mon 27 May 2019 Montreal, QC, Canada
co-located with ICSE 2019

The emergence of online open source repositories in the recent years has led to an explosion in the volume of openly available source code, coupled with metadata that relate to a variety of software development activities. As an effect, in line with recent advances in machine learning research, software maintenance activities are switching from symbolic formal methods to data–driven methods. In this context, the rich semantics hidden in source code identifiers provide opportunities for building semantic representations of code which can assist tasks of code search and reuse. To this end, we deliver in the form of pretrained vector space models, distributed code representations for six popular programming languages, namely, Java, Python, PHP, C, C++, and C#. The models are produced using fastText, a state–of–the–art library for learning word representations. Each model is trained on data from a single programming language; the code mined for producing all models amounts to over 13.000 repositories. We indicate dissimilarities between natural language and source code, as well as variations in coding conventions in between the different programming languages we processed. We describe how these heterogeneities guided the data preprocessing decisions we took and the selection of the training parameters in the released models. Finally, we propose potential applications of the models and discuss limitations of the models.

Sun 26 May

Displayed time zone: Eastern Time (US & Canada) change

11:00 - 11:45
Session I: Representations for Mining (Part 1)MSR 2019 Technical Papers / MSR 2019 Data Showcase at Place du Canada
Chair(s): Chanchal K. Roy University of Saskatchewan
SCOR: Source Code Retrieval With Semantics and Order
MSR 2019 Technical Papers
Pre-print Media Attached
PathMiner : A Library for Mining of Path-Based Representations of Code
MSR 2019 Technical Papers
Vladimir Kovalenko TU Delft, Egor Bogomolov Higher School of Economics, JetBrains Research, Timofey Bryksin , Alberto Bacchelli University of Zurich
DOI Pre-print Media Attached
Import2vec: learning embeddings for software libraries
MSR 2019 Technical Papers
Bart Theeten Nokia Bell Labs, Belgium, Frederik Vandeputte , Tom Van Cutsem Nokia Bell Labs
Semantic Source Code Models Using Identifier Embeddings
MSR 2019 Data Showcase
Vasiliki Efstathiou Athens University of Economics and Business, Diomidis Spinellis Athens University of Economics and Business