# On Debugging the Performance of Configurable Software Systems: Developer Needs and Tailored Tool Support

arXiv: https://arxiv.org/abs/2203.10356 · HTML: https://mlsanity.com/paper/2203.10356/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2022.

## Built on by (most cited first, 6 of 6)

- [Faster Configuration Performance Bug Testing with Neural Dual-level Prioritization](https://mlsanity.com/paper/2501.15392/) (2025)
- [CaRE: Finding Root Causes of Configuration Issues in Highly-Configurable Robots](https://mlsanity.com/paper/2301.07690/) (2023)
- [ROCAS: Root Cause Analysis of Autonomous Driving Accidents via Cyber-Physical Co-mutation](https://mlsanity.com/paper/2409.07774/) (2024)
- [Identifying Performance-Sensitive Configurations in Software Systems through Code Analysis with LLM Agents](https://mlsanity.com/paper/2406.12806/) (2024)
- [Uncertainty-Aware Performance Prediction for Highly Configurable Software Systems via Bayesian Neural Networks](https://mlsanity.com/paper/2212.13359/) (2022)
- [Understanding the Complexity and Its Impact on Testing in ML-Enabled Systems](https://mlsanity.com/paper/2301.03837/) (2023)

## Abstract (arXiv)

Determining whether a configurable software system has a performance bug or it was misconfigured is often challenging. While there are numerous debugging techniques that can support developers in this task, there is limited empirical evidence of how useful the techniques are to address the actual needs that developers have when debugging the performance of configurable software systems; most techniques are often evaluated in terms of technical accuracy instead of their usability. In this paper, we take a human-centered approach to identify, design, implement, and evaluate a solution to support developers in the process of debugging the performance of configurable software systems. We first conduct an exploratory study with 19 developers to identify the information needs that developers have during this process. Subsequently, we design and implement a tailored tool, adapting techniques from prior work, to support those needs. Two user studies, with a total of 20 developers, validate and confirm that the information that we provide helps developers debug the performance of configurable software systems.