Mithra: Anomaly Detection as an Oracle for Cyberphysical Systems

Testing plays an essential role in ensuring the safety and quality of cyberphysical systems (CPSs). One of the main challenges in automated and software-in-the-loop simulation testing of CPSs is defining effective oracles that can check that a given system conforms to expectations of desired behavior. Manually specifying such oracles can be tedious, complex, and error-prone, and so techniques for automatically learning oracles are attractive. Characteristics of CPSs, such as limited or no access to source code, behavior that is non-deterministic and sensitive to noise, and that the system may respond differently to input based on its context introduce considerable challenges for automated oracle learning. We present Mithra, a novel, unsupervised oracle learning technique for CPSs that operates on existing telemetry data. It uses a three-step multivariate time series clustering to discover the set of unique, correct behaviors for a CPS, which it uses to construct robust oracles. We instantiate our proposed technique for ArduPilot, a popular, open-source autopilot software. On a set of 24 bugs, we show that Mithra effectively identifies buggy executions with few false positives and outperforms AR-SI, a state-of-the-art CPS oracle learning technique. We demonstrate Mithra's wider applicability by applying it to an autonomous racer built for the Robot Operating System.