# Epicure: Distilling Sequence Model Predictions into Patterns

arXiv: https://arxiv.org/abs/2308.08203 · HTML: https://mlsanity.com/paper/2308.08203/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 16 Aug 2023.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://mlsanity.com/paper/1909.09436/) (2019)
- [UniXcoder: Unified Cross-Modal Pre-training for Code Representation](https://mlsanity.com/paper/2203.03850/) (2022)
- [Learning to Represent Programs with Graphs](https://mlsanity.com/paper/1711.00740/) (2017)
- [code2seq: Generating Sequences from Structured Representations of Code](https://mlsanity.com/paper/1808.01400/) (2018)
- [The Adverse Effects of Code Duplication in Machine Learning Models of Code](https://mlsanity.com/paper/1812.06469/) (2018)
- [A Convolutional Attention Network for Extreme Summarization of Source Code](https://mlsanity.com/paper/1602.03001/) (2016)
- [DIRE: A Neural Approach to Decompiled Identifier Naming](https://mlsanity.com/paper/1909.09029/) (2019)
- [Learning Natural Coding Conventions](https://mlsanity.com/paper/1402.4182/) (2014)
- [A General Path-Based Representation for Predicting Program Properties](https://mlsanity.com/paper/1803.09544/) (2018)
- [Learning to Complete Code with Sketches](https://mlsanity.com/paper/2106.10158/) (2021)
- [Learning Deterministic Weighted Automata with Queries and Counterexamples](https://mlsanity.com/paper/1910.13895/) (2019)
- [Context2Name: A Deep Learning-Based Approach to Infer Natural Variable Names from Usage Contexts](https://mlsanity.com/paper/1809.05193/) (2018)
- [Impact of Evaluation Methodologies on Code Summarization](https://mlsanity.com/paper/2108.09619/) (2021)
- [Recovering Variable Names for Minified Code with Usage Contexts](https://mlsanity.com/paper/1906.03488/) (2019)

## Abstract (arXiv)

Most machine learning models predict a probability distribution over concrete outputs and struggle to accurately predict names over high entropy sequence distributions. Here, we explore finding abstract, high-precision patterns intrinsic to these predictions in order to make abstract predictions that usefully capture rare sequences. In this short paper, we present Epicure, a method that distils the predictions of a sequence model, such as the output of beam search, into simple patterns. Epicure maps a model's predictions into a lattice that represents increasingly more general patterns that subsume the concrete model predictions. On the tasks of predicting a descriptive name of a function given the source code of its body and detecting anomalous names given a function, we show that Epicure yields accurate naming patterns that match the ground truth more often compared to just the highest probability model prediction. For a false alarm rate of 10%, Epicure predicts patterns that match 61% more ground-truth names compared to the best model prediction, making Epicure well-suited for scenarios that require high precision.