# White-Box Performance-Influence Models: A Profiling and Learning Approach

arXiv: https://arxiv.org/abs/2102.06395 · HTML: https://mlsanity.com/paper/2102.06395/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2021.

## Built on by (most cited first, 9 of 9)

- [Predicting Software Performance with Divide-and-Learn](https://mlsanity.com/paper/2306.06651/) (2023)
- [Distilled Lifelong Self-Adaptation for Configurable Systems](https://mlsanity.com/paper/2501.00840/) (2025)
- [Dually Hierarchical Drift Adaptation for Online Configuration Performance Learning](https://mlsanity.com/paper/2507.08730/) (2025)
- [Revealing Domain-Spatiality Patterns for Configuration Tuning: Domain Knowledge Meets Fitness Landscapes](https://mlsanity.com/paper/2603.19897/) (2026)
- [CaRE: Finding Root Causes of Configuration Issues in Highly-Configurable Robots](https://mlsanity.com/paper/2301.07690/) (2023)
- [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)
- [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)

Many modern software systems are highly configurable, allowing the user to tune them for performance and more. Current performance modeling approaches aim at finding performance-optimal configurations by building performance models in a black-box manner. While these models provide accurate estimates, they cannot pinpoint causes of observed performance behavior to specific code regions. This does not only hinder system understanding, but it also complicates tracing the influence of configuration options to individual methods. We propose a white-box approach that models configuration-dependent performance behavior at the method level. This allows us to predict the influence of configuration decisions on individual methods, supporting system understanding and performance debugging. The approach consists of two steps: First, we use a coarse-grained profiler and learn performance-influence models for all methods, potentially identifying some methods that are highly configuration- and performance-sensitive, causing inaccurate predictions. Second, we re-measure these methods with a fine-grained profiler and learn more accurate models, at higher cost, though. By means of 9 real-world Java software systems, we demonstrate that our approach can efficiently identify configuration-relevant methods and learn accurate performance-influence models.