Symetra: Visual Analytics for the Parameter Tuning Process of Symbolic Execution Engines

2026-04-06

AI Summary

This work addresses the challenge that symbolic execution engines involve numerous parameters with complex, interdependent effects, often leading users—due to limited understanding—to rely on suboptimal default configurations, while existing automated tuning approaches lack interpretability. To bridge this gap, the authors propose a human-in-the-loop parameter tuning paradigm and develop Symetra, a visual analytics system that enables dual-perspective overviews of how parameters influence branch coverage. Symetra supports interactive comparison of configuration sets and facilitates pattern recognition. Experimental results demonstrate that expert users leveraging Symetra not only accurately interpret parameter interactions and identify complementary configurations but also achieve significantly higher branch coverage and tuning efficiency compared to fully automated methods, thereby effectively overcoming the interpretability bottleneck in symbolic execution parameter optimization.

Abstract

Symbolic execution engines such as KLEE automatically generate test cases to maximize branch coverage, but their numerous parameters make it difficult to understand the parameters' impact, leading the user to rely on suboptimal default configurations. While automated tuners have shown promising results, they provide limited insights into why certain configurations work well, motivating the need for Human-in-the-Loop approaches. In this work, we present a visual analytics system, Symetra, designed to support Human-in-the-Loop parameter tuning of symbolic execution engines. To handle a large number of parameters and their configurations, we provide two complementary overviews of their impact on branch coverage values and patterns. Building on these overviews, our system enables collective analysis, allowing the user to contrast groups of configurations and identify differences that may affect branch coverage. We also report on case studies and a Human-in-the-Loop tuning process, demonstrating that experts not only interpreted parameter impacts and identified complementary configurations, but also improved upon fully automated approaches in both branch coverage and tuning efficiency.

Problem

• symbolic execution
• parameter tuning
• branch coverage
• visual analytics
• Human-in-the-Loop

Innovation

• Visual Analytics
• Symbolic Execution
• Human-in-the-Loop
• Parameter Tuning
• Branch Coverage