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The paper introduces Symetra, a visual analytics system to facilitate human-in-the-loop parameter tuning for symbolic execution engines like KLEE. Symetra provides two complementary overviews of parameter impact on branch coverage, enabling users to analyze and contrast configuration groups. Case studies demonstrate that experts using Symetra can outperform fully automated approaches in both branch coverage and tuning efficiency by interpreting parameter impacts and identifying complementary configurations.
Human intuition, guided by visual analytics, can still beat fully automated parameter tuning for symbolic execution engines.
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.
