# On the Effectiveness of Machine Learning-based Call Graph Pruning: An Empirical Study

arXiv: https://arxiv.org/abs/2402.07294 · HTML: https://mlsanity.com/paper/2402.07294/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 63 references that are in the map.
- First posted on arXiv 11 Feb 2024.

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

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- [A Survey of Machine Learning for Big Code and Naturalness](https://mlsanity.com/paper/1709.06182/) (2017)
- [A Manually-Curated Dataset of Fixes to Vulnerabilities of Open-Source Software](https://mlsanity.com/paper/1902.02595/) (2019)
- [A Survey on Machine Learning Techniques for Source Code Analysis](https://mlsanity.com/paper/2110.09610/) (2021)
- [AutoPruner: Transformer-Based Call Graph Pruning](https://mlsanity.com/paper/2209.03230/) (2022)
- [JGraphT -- A Java library for graph data structures and algorithms](https://mlsanity.com/paper/1904.08355/) (2019)
- [Algorithms for Large-scale Network Analysis and the NetworKit Toolkit](https://mlsanity.com/paper/2209.13355/) (2022)
- [On the Effect of Transitivity and Granularity on Vulnerability Propagation in the Maven Ecosystem](https://mlsanity.com/paper/2301.07972/) (2023)
- [Context Sensitivity without Contexts: A Cut-Shortcut Approach to Fast and Precise Pointer Analysis](https://mlsanity.com/paper/2304.12034/) (2023)

## Built on by (most cited first, 1 of 1)

- [Call Me Maybe: Enhancing JavaScript Call Graph Construction using Graph Neural Networks](https://mlsanity.com/paper/2506.18191/) (2025)

## Abstract (arXiv)

Static call graph (CG) construction often over-approximates call relations, leading to sound, but imprecise results. Recent research has explored machine learning (ML)-based CG pruning as a means to enhance precision by eliminating false edges. However, current methods suffer from a limited evaluation dataset, imbalanced training data, and reduced recall, which affects practical downstream analyses. Prior results were also not compared with advanced static CG construction techniques yet. This study tackles these issues. We introduce the NYXCorpus, a dataset of real-world Java programs with high test coverage and we collect traces from test executions and build a ground truth of dynamic CGs. We leverage these CGs to explore conservative pruning strategies during the training and inference of ML-based CG pruners. We conduct a comparative analysis of static CGs generated using zero control flow analysis (0-CFA) and those produced by a context-sensitive 1-CFA algorithm, evaluating both with and without pruning. We find that CG pruning is a difficult task for real-world Java projects and substantial improvements in the CG precision (+25%) meet reduced recall (-9%). However, our experiments show promising results: even when we favor recall over precision by using an F2 metric in our experiments, we can show that pruned CGs have comparable quality to a context-sensitive 1-CFA analysis while being computationally less demanding. Resulting CGs are much smaller (69%), and substantially faster (3.5x speed-up), with virtually unchanged results in our downstream analysis.