PathMiner : A Library for Mining of Path-Based Representations of Code
One recent, significant advance in modeling source code for machine learning algorithms has been the introduction of path-based representation – an approach consisting in representing a snippet of code as a collection of paths from its syntax tree. Such representation efficiently captures the structure of code, which, in turn, carries its semantics and other information. Building the path-based representation involves parsing the code and extracting the paths from its syntax tree; these steps build up to a substantial technical job. With no common reusable toolkit existing for this task, the burden of mining diverts the focus of researchers from the essential work and hinders newcomers in the field of machine learning on code.
In this paper, we present PathMiner – an open-source library for mining path-based representations of code. Path- Miner is fast, flexible, well-tested, and easily extensible to support input code in any common programming language.
Conference DaySun 26 MayDisplayed time zone: Eastern Time (US & Canada) change
11:00 - 11:45
|SCOR: Source Code Retrieval With Semantics and Order|
MSR 2019 Technical PapersPre-print Media Attached
|PathMiner : A Library for Mining of Path-Based Representations of Code|
MSR 2019 Technical Papers
Vladimir KovalenkoTU Delft, Egor BogomolovHigher School of Economics, JetBrains Research, Timofey Bryksin, Alberto BacchelliUniversity of ZurichDOI Pre-print Media Attached
|Import2vec: learning embeddings for software libraries|
MSR 2019 Technical PapersPre-print
|Semantic Source Code Models Using Identifier Embeddings|
MSR 2019 Data Showcase
Vasiliki EfstathiouAthens University of Economics and Business, Diomidis SpinellisAthens University of Economics and BusinessPre-print