Instrumentation-Driven Evolution-Aware Runtime Verification
Kevin Guan, Owolabi Legunsen
摘要
Runtime verification (RV) found hundreds of bugs by monitoring passing tests against formal specifications (specs). RV first instruments a program to obtain relevant events, e.g., method calls, to monitor. A hindrance to RV adoption, especially in continuous integration, is its high overhead. So, prior work proposed spec-driven evolution-aware techniques to speed up RV. They use complex analysis to re-monitor a subset of specs related to code impacted by changes. But, these techniques assume that RV overhead is dominated by monitoring time, and their designs often sacrifice safety (ability to find all new violations) for speed. We present IMOP, the first instrumentation-driven evolution-aware RV framework. IMOP leverages a recent observation that RV overhead during testing is often dominated by instrumentation, not monitoring. IMOP embodies a family of 14 techniques that aim to safely speed up RV by simply re-instrumenting only changed code. Instrumentation from the old revision is re-used for unchanged code, and all specs are re-monitored in the new revision. We implement IMOP as a Maven plugin and evaluate it on 2,028 revisions of 66 projects, using 160 specs of correct JDK API usage. IMOP is safe by design. It is up to 40.2x faster than re-running RV from scratch after each change, and 17.8x and 6.7x faster than safe and unsafe spec-driven techniques, respectively. IMOP is faster than just applying regression test selection to RV.
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引用它的顶会 Paper4
- Faster Explicit-Trace Monitoring-Oriented Programming for Runtime Verification of Software TestsKevin Guan, Marcelo d'Amorim, Owolabi LegunsenOOPSLA 2025 · 被引用 7 次
- Faster Runtime Verification during Testing via Feedback-Guided Selective MonitoringShinhae Kim, Saikat Dutta, Owolabi LegunsenASE 2025 · 被引用 3 次
- Fine-Grained Analyses for Evolution-Aware Runtime VerificationPengyue Jiang, Kevin Guan, Mahdi Khosravi, Moustafa Ismail 等ICSE 2026 · 被引用 1 次
- A Closer Look at the Use of Reinforcement Learning for Speeding Up Runtime Verification of Software Tests (Experience Paper)Shinhae Kim, Saikat Dutta, Owolabi LegunsenISSTA 2026
它引用的顶会 Paper3
- More Precise Regression Test Selection via Reasoning about Semantics-Modifying ChangesYu Liu, Jiyang Zhang, Pengyu Nie, Milos Gligoric 等ISSTA 2023 · 被引用 19 次
- An In-Depth Study of Runtime Verification Overheads during Software TestingKevin Guan, Owolabi LegunsenISSTA 2024 · 被引用 7 次
- IronSpec: Increasing the Reliability of Formal SpecificationsEli Goldweber, Weixin Yu, Seyed Armin Vakil-Ghahani, Manos KapritsosOSDI 2024 · 被引用 4 次
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