CausalCut: Test Case Data for Cyber-Physical Systems

Data for the replication package of the research paper 'Causality-Driven Test Case Minimisation for Cyber-Physical Systems'. The dataset is 24.9 GB in size and was last updated on 2026-05-12. It is authored by Michael Foster and shared under a CC-BY-4.0 license.

Use Cases

• Evaluating causal inference techniques for test case reduction based on runtime data.
• Benchmarking test case minimisation algorithms against state-of-the-art methods.
• Analyzing failure-inducing manipulations in cyber-physical systems like an artificial pancreas and a water treatment plant.

Strengths

• Dataset size is 24.9 GB, indicating substantial data volume.
• Associated with a peer-reviewed research methodology evaluated on two distinct cyber-physical systems.
• Openly licensed under CC-BY-4.0, allowing for broad reuse and sharing.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect bias inherent to the specific systems (artificial pancreas, water treatment plant) studied.

Provenance

Source

Michael Foster

Collection Method

Likely generated from fuzzing experiments and runtime data collection for cyber-physical systems.

Freshness

Last updated 2026-05-12 07:04:16; freshness should be verified.