Sept. 14, 2022, 1:12 a.m. | Tushar Sharma, Maria Kechagia, Stefanos Georgiou, Rohit Tiwari, Indira Vats, Hadi Moazen, Federica Sarro

cs.LG updates on arXiv.org arxiv.org

The advancements in machine learning techniques have encouraged researchers
to apply these techniques to a myriad of software engineering tasks that use
source code analysis, such as testing and vulnerability detection. Such a large
number of studies hinders the community from understanding the current research
landscape. This paper aims to summarize the current knowledge in applied
machine learning for source code analysis. We review studies belonging to
twelve categories of software engineering tasks and corresponding machine
learning techniques, tools, and …

analysis arxiv code code analysis machine machine learning machine learning techniques survey

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Associate Data Engineer

@ Nominet | Oxford/ Hybrid, GB

Data Science Senior Associate

@ JPMorgan Chase & Co. | Bengaluru, Karnataka, India