# PACE: A Program Analysis Framework for Continuous Performance Prediction

arXiv: https://arxiv.org/abs/2312.00918 · HTML: https://mlsanity.com/paper/2312.00918/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 65 references that are in the map.
- First posted on arXiv 1 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks](https://mlsanity.com/paper/1810.02244/) (2018)
- [What Does Explainable AI Really Mean? A New Conceptualization of Perspectives](https://mlsanity.com/paper/1710.00794/) (2017)
- [Detecting Code Clones with Graph Neural Networkand Flow-Augmented Abstract Syntax Tree](https://mlsanity.com/paper/2002.08653/) (2020)
- [White-Box Analysis over Machine Learning: Modeling Performance of Configurable Systems](https://mlsanity.com/paper/2101.05362/) (2021)
- [A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction](https://mlsanity.com/paper/2304.13032/) (2023)
- [Comparative Code Structure Analysis using Deep Learning for Performance Prediction](https://mlsanity.com/paper/2102.07660/) (2021)
- [Automated User Experience Testing through Multi-Dimensional Performance Impact Analysis](https://mlsanity.com/paper/2104.03453/) (2021)
- [Short Paper: Static and Microarchitectural ML-Based Approaches For Detecting Spectre Vulnerabilities and Attacks](https://mlsanity.com/paper/2210.14452/) (2022)

## Built on by (most cited first, 3 of 3)

- [RobuNFR: Evaluating the Robustness of Large Language Models on Non-Functional Requirements Aware Code Generation](https://mlsanity.com/paper/2503.22851/) (2025)
- [A Benchmark Dataset for Graph Regression with Homogeneous and Multi-Relational Variants](https://mlsanity.com/paper/2505.23875/) (2025)
- [VulStyle: A Multi-Modal Pre-Training for Code Stylometry-Augmented Vulnerability Detection](https://mlsanity.com/paper/2604.26313/) (2026)

## Abstract (arXiv)

Software development teams establish elaborate continuous integration pipelines containing automated test cases to accelerate the development process of software. Automated tests help to verify the correctness of code modifications decreasing the response time to changing requirements. However, when the software teams do not track the performance impact of pending modifications, they may need to spend considerable time refactoring existing code. This paper presents PACE, a program analysis framework that provides continuous feedback on the performance impact of pending code updates. We design performance microbenchmarks by mapping the execution time of functional test cases given a code update. We map microbenchmarks to code stylometry features and feed them to predictors for performance predictions. Our experiments achieved significant performance in predicting code performance, outperforming current state-of-the-art by 75% on neural-represented code stylometry features.