# HINNPerf: Hierarchical Interaction Neural Network for Performance Prediction of Configurable Systems

arXiv: https://arxiv.org/abs/2204.03931 · HTML: https://mlsanity.com/paper/2204.03931/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 57 references that are in the map.
- First posted on arXiv 8 Apr 2022.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Error bounds for approximations with deep ReLU networks](https://mlsanity.com/paper/1610.01145/) (2016)
- [On the Expressive Power of Deep Neural Networks](https://mlsanity.com/paper/1606.05336/) (2016)
- [Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation](https://mlsanity.com/paper/1901.02985/) (2019)
- [The Power of Depth for Feedforward Neural Networks](https://mlsanity.com/paper/1512.03965/) (2015)
- [Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review](https://mlsanity.com/paper/1611.00740/) (2016)
- [HDLTex: Hierarchical Deep Learning for Text Classification](https://mlsanity.com/paper/1709.08267/) (2017)
- [Why Deep Neural Networks for Function Approximation?](https://mlsanity.com/paper/1610.04161/) (2016)
- [Item2Vec: Neural Item Embedding for Collaborative Filtering](https://mlsanity.com/paper/1603.04259/) (2016)
- [Inverting face embeddings with convolutional neural networks](https://mlsanity.com/paper/1606.04189/) (2016)
- [Predicting Performance of Software Configurations: There is no Silver Bullet](https://mlsanity.com/paper/1911.12643/) (2019)

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

- [Deep Configuration Performance Learning: A Systematic Survey and Taxonomy](https://mlsanity.com/paper/2403.03322/) (2024)
- [Dually Hierarchical Drift Adaptation for Online Configuration Performance Learning](https://mlsanity.com/paper/2507.08730/) (2025)
- [Dividable Configuration Performance Learning](https://mlsanity.com/paper/2409.07629/) (2024)
- [Rethinking Performance Analysis for Configurable Software Systems: A Case Study from a Fitness Landscape Perspective](https://mlsanity.com/paper/2412.16888/) (2024)
- [Evaluating Useful Surrogate Models for Configuration Tuning Beyond Accuracy: A Fitness Landscape Analysis Perspective](https://mlsanity.com/paper/2509.21945/) (2025)

## Abstract (arXiv)

Modern software systems are usually highly configurable, providing users with customized functionality through various configuration options. Understanding how system performance varies with different option combinations is important to determine optimal configurations that meet specific requirements. Due to the complex interactions among multiple options and the high cost of performance measurement under a huge configuration space, it is challenging to study how different configurations influence the system performance. To address these challenges, we propose HINNPerf, a novel hierarchical interaction neural network for performance prediction of configurable systems. HINNPerf employs the embedding method and hierarchic network blocks to model the complicated interplay between configuration options, which improves the prediction accuracy of the method. Besides, we devise a hierarchical regularization strategy to enhance the model robustness. Empirical results on 10 real-world configurable systems show that our method statistically significantly outperforms state-of-the-art approaches by achieving average 22.67% improvement in prediction accuracy. In addition, combined with the Integrated Gradients method, the designed hierarchical architecture provides some insights about the interaction complexity and the significance of configuration options, which might help users and developers better understand how the configurable system works and efficiently identify significant options affecting the performance.