Distributed vectorized representations of source code commits

Distributed vector representations of source code commits, are generated to become part of a data corpus for machine learning (ML) for analyzing source code. The code commit is received, and time information is referenced to split the source code into pre-change source code and post-change source co...

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Bibliographic Details
Main Authors Sabetta, Antonino, Baumann, Arnaud, Cabrera Lozoya, Rocio, Bezzi, Michele
Format Patent
LanguageEnglish
Published 19.07.2022
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Summary:Distributed vector representations of source code commits, are generated to become part of a data corpus for machine learning (ML) for analyzing source code. The code commit is received, and time information is referenced to split the source code into pre-change source code and post-change source code. The pre-change source code is converted into a first code representation (e.g., based on a graph model), and the post-change source code into a second code representation. A first particle is generated from the first code representation, and a second particle is generated from the second code representation. The first particle and the second particle are compared to create a delta. The delta is transformed into a first commit vector by referencing an embedding matrix to numerically encode the first particle and the second particle. Following classification, the commit vector is stored in a data corpus for performing ML analysis upon source code.
Bibliography:Application Number: US202017080520