Machine learning on source code is a new area of research in the field of artificial intelligence, which, unlike classical problems such as image segmentation, does not yet have established standard techniques. For instance there are standard methods for processing images that make machine learning algorithms pay attention to their two-dimensionality. However, there are currently no common techniques for encoding the semantic structure of source code. Therefore, you need new ways to mathematically represent the code of projects. This technology offers a variety of possible applications, for example, in the area of static code analysis or in the automatic selection of relevant test cases.
Thomas Endres and Samuel Hopstock share methods for transferring classic machine learning approaches to this new field of expertise. Along the way, Thomas and Samuel detail approaches for both automatic and manual training data generation and offer an overview of suitable models and machine learning frameworks for this challenge. They conclude by exploring the possibilities of using such models for the analysis of code.
Samuel Hopstock is working toward his bachelor’s degree in computer science at the Technical University of Munich. He’s also a working student at TNG Technology Consulting in Unterföhring, where he is currently involved in the development of software in the field of machine learning with Python and Java. He is interested in any new technological developments, especially involving Android.
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