mlsanityData to 7 Oct 2026

PACE: A Program Analysis Framework for Continuous Performance Prediction

arXiv 2312.00918 ↗First posted 1 Dec 2023cs.SE cs.LG cs.PFcited by 3

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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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 9 of its 65 references that are in the map. First posted on arXiv 1 Dec 2023.

Builds on

9 of 65 references.

References · 9

  1. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  2. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
  3. 2017What Does Explainable AI Really Mean? A New Conceptualization of Perspectivescited by 72
  4. 2020Detecting Code Clones with Graph Neural Networkand Flow-Augmented Abstract Syntax Treecited by 68
  5. 2021White-Box Analysis over Machine Learning: Modeling Performance of Configurable Systemscited by 10
  6. 2023A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Predictioncited by 4
  7. 2021Comparative Code Structure Analysis using Deep Learning for Performance Predictioncited by 3
  8. 2021Automated User Experience Testing through Multi-Dimensional Performance Impact Analysiscited by 1
  9. 2022Short Paper: Static and Microarchitectural ML-Based Approaches For Detecting Spectre Vulnerabilities and Attackscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025RobuNFR: Evaluating the Robustness of Large Language Models on Non-Functional Requirements Aware Code Generationcited by 5
  2. 2025A Benchmark Dataset for Graph Regression with Homogeneous and Multi-Relational Variantscited by 0
  3. 2026VulStyle: A Multi-Modal Pre-Training for Code Stylometry-Augmented Vulnerability Detectioncited by 0