# Code Vectors: Understanding Programs Through Embedded Abstracted Symbolic Traces

arXiv: https://arxiv.org/abs/1803.06686 · HTML: https://mlsanity.com/paper/1803.06686/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2018.

## Built on by (most cited first, 7 of 7)

- [CURE: Code-Aware Neural Machine Translation for Automatic Program Repair](https://mlsanity.com/paper/2103.00073/) (2021)
- [Reasoning Runtime Behavior of a Program with LLM: How Far Are We?](https://mlsanity.com/paper/2403.16437/) (2024)
- [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023)
- [Learning to Make Compiler Optimizations More Effective](https://mlsanity.com/paper/2102.13514/) (2021)
- [deGraphCS: Embedding Variable-based Flow Graph for Neural Code Search](https://mlsanity.com/paper/2103.13020/) (2021)
- [Test2Vec: An Execution Trace Embedding for Test Case Prioritization](https://mlsanity.com/paper/2206.15428/) (2022)
- [Embedding Code Contexts for Cryptographic API Suggestion:New Methodologies and Comparisons](https://mlsanity.com/paper/2103.08747/) (2021)

## Abstract (arXiv)

With the rise of machine learning, there is a great deal of interest in treating programs as data to be fed to learning algorithms. However, programs do not start off in a form that is immediately amenable to most off-the-shelf learning techniques. Instead, it is necessary to transform the program to a suitable representation before a learning technique can be applied. In this paper, we use abstractions of traces obtained from symbolic execution of a program as a representation for learning word embeddings. We trained a variety of word embeddings under hundreds of parameterizations, and evaluated each learned embedding on a suite of different tasks. In our evaluation, we obtain 93% top-1 accuracy on a benchmark consisting of over 19,000 API-usage analogies extracted from the Linux kernel. In addition, we show that embeddings learned from (mainly) semantic abstractions provide nearly triple the accuracy of those learned from (mainly) syntactic abstractions.