Dynamic Neural Program Embedding for Program Repair

TL;DR

This paper introduces a semantic program embedding learned from execution traces, which improves program repair and error prediction over traditional syntax-based embeddings by capturing program behavior more accurately.

Contribution

The paper presents a novel semantic embedding method for programs based on execution traces, enhancing program analysis tasks like repair and error prediction.

Findings

Semantic embedding outperforms syntactic embeddings in error type prediction

Semantic embedding improves search efficiency in program repair

Evaluation on educational platforms demonstrates effectiveness

Abstract

Neural program embeddings have shown much promise recently for a variety of program analysis tasks, including program synthesis, program repair, fault localization, etc. However, most existing program embeddings are based on syntactic features of programs, such as raw token sequences or abstract syntax trees. Unlike images and text, a program has an unambiguous semantic meaning that can be difficult to capture by only considering its syntax (i.e. syntactically similar pro- grams can exhibit vastly different run-time behavior), which makes syntax-based program embeddings fundamentally limited. This paper proposes a novel semantic program embedding that is learned from program execution traces. Our key insight is that program states expressed as sequential tuples of live variable values not only captures program semantics more precisely, but also offer a more natural fit for Recurrent Neural Networks to model. We evaluate different syntactic and semantic program embeddings on predicting the types of errors that students make in their submissions to an introductory programming class and two exercises on the CodeHunt education platform. Evaluation results show that our new semantic program embedding significantly outperforms the syntactic program embeddings based on token sequences and abstract syntax trees. In addition, we augment a search-based program repair system with the predictions obtained from our se- mantic embedding, and show that search efficiency is also significantly improved.