# Predicting Software Performance with Divide-and-Learn

arXiv: https://arxiv.org/abs/2306.06651 · HTML: https://mlsanity.com/paper/2306.06651/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 74 references that are in the map.
- First posted on arXiv 11 Jun 2023.

## Builds on (most cited first, 16 of 16)

- [Finding Faster Configurations using FLASH](https://mlsanity.com/paper/1801.02175/) (2018)
- [Multi-Objectivizing Software Configuration Tuning (for a single performance concern)](https://mlsanity.com/paper/2106.01331/) (2021)
- [Do Performance Aspirations Matter for Guiding Software Configuration Tuning?](https://mlsanity.com/paper/2301.03290/) (2023)
- [Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling](https://mlsanity.com/paper/1602.02485/) (2016)
- [An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing Systems](https://mlsanity.com/paper/1606.06543/) (2016)
- [Lifelong Dynamic Optimization for Self-Adaptive Systems: Fact or Fiction?](https://mlsanity.com/paper/2201.07096/) (2022)
- [Robust Dialogue State Tracking with Weak Supervision and Sparse Data](https://mlsanity.com/paper/2202.03354/) (2022)
- [Hyperparameter Optimization for Effort Estimation](https://mlsanity.com/paper/1805.00336/) (2018)
- [Planning Landscape Analysis for Self-Adaptive Systems](https://mlsanity.com/paper/2203.12472/) (2022)
- [White-Box Analysis over Machine Learning: Modeling Performance of Configurable Systems](https://mlsanity.com/paper/2101.05362/) (2021)
- [Does Configuration Encoding Matter in Learning Software Performance? An Empirical Study on Encoding Schemes](https://mlsanity.com/paper/2203.15988/) (2022)
- [Low-Level Augmented Bayesian Optimization for Finding the Best Cloud VM](https://mlsanity.com/paper/1712.10081/) (2017)
- [White-Box Performance-Influence Models: A Profiling and Learning Approach](https://mlsanity.com/paper/2102.06395/) (2021)
- [Faster Discovery of Faster System Configurations with Spectral Learning](https://mlsanity.com/paper/1701.08106/) (2017)
- [ConPredictor: Concurrency Defect Prediction in Real-World Applications](https://mlsanity.com/paper/1804.03589/) (2018)
- [Towards Quality Assurance of Software Product Lines with Adversarial Configurations](https://mlsanity.com/paper/1909.07283/) (2019)

## Built on by (most cited first, 16 of 16)

- [Predicting Configuration Performance in Multiple Environments with Sequential Meta-learning](https://mlsanity.com/paper/2402.03183/) (2024)
- [Adapting Multi-objectivized Software Configuration Tuning](https://mlsanity.com/paper/2404.04744/) (2024)
- [Distilled Lifelong Self-Adaptation for Configurable Systems](https://mlsanity.com/paper/2501.00840/) (2025)
- [Faster Configuration Performance Bug Testing with Neural Dual-level Prioritization](https://mlsanity.com/paper/2501.15392/) (2025)
- [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)
- [Learning Software Bug Reports: A Systematic Literature Review](https://mlsanity.com/paper/2507.04422/) (2025)
- [CoTune: Co-evolutionary Configuration Tuning](https://mlsanity.com/paper/2509.24694/) (2025)
- [Light over Heavy: Automated Performance Requirements Quantification with Linguistic Inducement](https://mlsanity.com/paper/2511.03421/) (2025)
- [Evaluating Useful Surrogate Models for Configuration Tuning Beyond Accuracy: A Fitness Landscape Analysis Perspective](https://mlsanity.com/paper/2509.21945/) (2025)
- [Causally Perturbed Fairness Testing](https://mlsanity.com/paper/2510.18719/) (2025)
- [CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG](https://mlsanity.com/paper/2605.08333/) (2026)
- [Contexts Matter: An Empirical Study on Contextual Influence in Fairness Testing for Deep Learning Systems](https://mlsanity.com/paper/2408.06102/) (2024)
- [Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning](https://mlsanity.com/paper/2604.18862/) (2026)
- [LLMSYS-HPOBench: Hyperparameter Optimization Benchmark Suite for Real-World LLM Systems](https://mlsanity.com/paper/2605.08305/) (2026)

## Abstract (arXiv)

Predicting the performance of highly configurable software systems is the foundation for performance testing and quality assurance. To that end, recent work has been relying on machine/deep learning to model software performance. However, a crucial yet unaddressed challenge is how to cater for the sparsity inherited from the configuration landscape: the influence of configuration options (features) and the distribution of data samples are highly sparse. In this paper, we propose an approach based on the concept of 'divide-and-learn', dubbed DaL. The basic idea is that, to handle sample sparsity, we divide the samples from the configuration landscape into distant divisions, for each of which we build a regularized Deep Neural Network as the local model to deal with the feature sparsity. A newly given configuration would then be assigned to the right model of division for the final prediction. Experiment results from eight real-world systems and five sets of training data reveal that, compared with the state-of-the-art approaches, DaL performs no worse than the best counterpart on 33 out of 40 cases (within which 26 cases are significantly better) with up to 1.94x improvement on accuracy; requires fewer samples to reach the same/better accuracy; and producing acceptable training overhead. Practically, DaL also considerably improves different global models when using them as the underlying local models, which further strengthens its flexibility. To promote open science, all the data, code, and supplementary figures of this work can be accessed at our repository: https://github.com/ideas-labo/DaL.