The Interaction between Inputs and Configurations fed to Software Systems: an Empirical Study
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Abstract (arXiv)
Widely used software systems such as video encoders are by necessity highly configurable, with hundreds or even thousands of options to choose from. Their users often have a hard time finding suitable values for these options (i.e. finding a proper configuration of the software system) to meet their goals for the tasks at hand, e.g. compress a video down to a certain size. One dimension of the problem is of course that performance depends on the input data: a video as input to an encoder like x264 or a file system fed to a tool like xz. To achieve good performance, users should therefore take into account both dimensions of (1) software variability and (2) input data. In this problem-statement paper, we conduct a large study over 8 configurable systems that quantifies the existing interactions between input data and configurations of software systems. The results exhibit that (1) inputs fed to software systems interact with their configuration options in non monotonous ways, significantly impacting their performance properties (2) tuning a software system for its input data makes it possible to multiply its performance by up to ten (3) input variability can jeopardize the relevance of performance predictive models for a field deployment.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Dec 2021.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 2; 2026: 2.
6 papers.
- 2024Predicting Configuration Performance in Multiple Environments with Sequential Meta-learningcited by 17
- 2025Faster Configuration Performance Bug Testing with Neural Dual-level Prioritizationcited by 9
- 2026Evaluating LLMs on Real-World Software Performance Optimizationcited by 2
- 2025Prompting for Performance: Exploring LLMs for Configuring Softwarecited by 1
- 2022Uncertainty-Aware Performance Prediction for Highly Configurable Software Systems via Bayesian Neural Networkscited by 0
- 2026LLMSYS-HPOBench: Hyperparameter Optimization Benchmark Suite for Real-World LLM Systemscited by 0
- 2026Evaluating LLMs on Real-World Software Performance Optimization24 Jun 2026
- 2026LLMSYS-HPOBench: Hyperparameter Optimization Benchmark Suite for Real-World LLM Systems8 May 2026
- 2025Prompting for Performance: Exploring LLMs for Configuring Software13 Jul 2025
- 2025Faster Configuration Performance Bug Testing with Neural Dual-level Prioritization26 Jan 2025
- 2024Predicting Configuration Performance in Multiple Environments with Sequential Meta-learning5 Feb 2024
- 2022Uncertainty-Aware Performance Prediction for Highly Configurable Software Systems via Bayesian Neural Networks27 Dec 2022