# White-Box Analysis over Machine Learning: Modeling Performance of Configurable Systems

arXiv: https://arxiv.org/abs/2101.05362 · HTML: https://mlsanity.com/paper/2101.05362/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2021.

## Built on by (most cited first, 10 of 10)

- [Predicting Software Performance with Divide-and-Learn](https://mlsanity.com/paper/2306.06651/) (2023)
- [Unicorn: Reasoning about Configurable System Performance through the lens of Causality](https://mlsanity.com/paper/2201.08413/) (2022)
- [Deep Configuration Performance Learning: A Systematic Survey and Taxonomy](https://mlsanity.com/paper/2403.03322/) (2024)
- [Requirements Engineering for Machine Learning: A Review and Reflection](https://mlsanity.com/paper/2210.00859/) (2022)
- [Dividable Configuration Performance Learning](https://mlsanity.com/paper/2409.07629/) (2024)
- [PACE: A Program Analysis Framework for Continuous Performance Prediction](https://mlsanity.com/paper/2312.00918/) (2023)
- [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)
- [Uncertainty-Aware Performance Prediction for Highly Configurable Software Systems via Bayesian Neural Networks](https://mlsanity.com/paper/2212.13359/) (2022)
- [Hardness, Structural Knowledge, and Opportunity: An Analytical Framework for Modular Performance Modeling](https://mlsanity.com/paper/2509.11000/) (2025)

## Abstract (arXiv)

Performance-influence models can help stakeholders understand how and where configuration options and their interactions influence the performance of a system. With this understanding, stakeholders can debug performance behavior and make deliberate configuration decisions. Current black-box techniques to build such models combine various sampling and learning strategies, resulting in tradeoffs between measurement effort, accuracy, and interpretability. We present Comprex, a white-box approach to build performance-influence models for configurable systems, combining insights of local measurements, dynamic taint analysis to track options in the implementation, compositionality, and compression of the configuration space, without relying on machine learning to extrapolate incomplete samples. Our evaluation on 4 widely-used, open-source projects demonstrates that Comprex builds similarly accurate performance-influence models to the most accurate and expensive black-box approach, but at a reduced cost and with additional benefits from interpretable and local models.