# TEP-GNN: Accurate Execution Time Prediction of Functional Tests using Graph Neural Networks

arXiv: https://arxiv.org/abs/2208.11947 · HTML: https://mlsanity.com/paper/2208.11947/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 36 references that are in the map.
- First posted on arXiv 25 Aug 2022.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks](https://mlsanity.com/paper/1810.02244/) (2018)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Meta-Learning: A Survey](https://mlsanity.com/paper/1810.03548/) (2018)
- [Learning to Represent Programs with Graphs](https://mlsanity.com/paper/1711.00740/) (2017)
- [Graph Matching Networks for Learning the Similarity of Graph Structured Objects](https://mlsanity.com/paper/1904.12787/) (2019)
- [Detecting Code Clones with Graph Neural Networkand Flow-Augmented Abstract Syntax Tree](https://mlsanity.com/paper/2002.08653/) (2020)
- [Generative Code Modeling with Graphs](https://mlsanity.com/paper/1805.08490/) (2018)
- [Structured Neural Summarization](https://mlsanity.com/paper/1811.01824/) (2018)
- [Sampling in Software Engineering Research: A Critical Review and Guidelines](https://mlsanity.com/paper/2002.07764/) (2020)
- [Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration](https://mlsanity.com/paper/1811.04122/) (2018)
- [Convolutional Neural Networks over Control Flow Graphs for Software Defect Prediction](https://mlsanity.com/paper/1802.04986/) (2018)
- [An Efficient Procedure for Mining Egocentric Temporal Motifs](https://mlsanity.com/paper/2110.01391/) (2021)
- [Patterns in the Chaos - a Study of Performance Variation and Predictability in Public IaaS Clouds](https://mlsanity.com/paper/1411.2429/) (2014)
- [Comparative Code Structure Analysis using Deep Learning for Performance Prediction](https://mlsanity.com/paper/2102.07660/) (2021)

## Built on by (most cited first, 5 of 5)

- [Meta-Path Learning for Multi-relational Graph Neural Networks](https://mlsanity.com/paper/2309.17113/) (2023)
- [A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction](https://mlsanity.com/paper/2304.13032/) (2023)
- [Reducing Computational Costs in Sentiment Analysis: Tensorized Recurrent Networks vs. Recurrent Networks](https://mlsanity.com/paper/2306.09705/) (2023)
- [Analysing the Behaviour of Tree-Based Neural Networks in Regression Tasks](https://mlsanity.com/paper/2406.11437/) (2024)
- [A Benchmark Dataset for Graph Regression with Homogeneous and Multi-Relational Variants](https://mlsanity.com/paper/2505.23875/) (2025)

## Abstract (arXiv)

Predicting the performance of production code prior to actually executing or benchmarking it is known to be highly challenging. In this paper, we propose a predictive model, dubbed TEP-GNN, which demonstrates that high-accuracy performance prediction is possible for the special case of predicting unit test execution times. TEP-GNN uses FA-ASTs, or flow-augmented ASTs, as a graph-based code representation approach, and predicts test execution times using a powerful graph neural network (GNN) deep learning model. We evaluate TEP-GNN using four real-life Java open source programs, based on 922 test files mined from the projects' public repositories. We find that our approach achieves a high Pearson correlation of 0.789, considerable outperforming a baseline deep learning model. However, we also find that more work is needed for trained models to generalize to unseen projects. Our work demonstrates that FA-ASTs and GNNs are a feasible approach for predicting absolute performance values, and serves as an important intermediary step towards being able to predict the performance of arbitrary code prior to execution.