Towards Improving Validation, Verification, Crash Investigations, and Event Reconstruction of Flight-Critical Systems with Self-Forensics

This paper introduces a novel concept of self-forensics to complement the standard autonomic self-CHOP properties of the self-managed systems, to be specified in the Forensic Lucid language. We argue that self-forensics, with the forensics taken out of the cybercrime domain, is applicable to “self-dissection” for the purpose of verification of autonomous software and hardware systems of flight-critical systems for automated incident and anomaly analysis and event reconstruction by the engineering teams in a variety of incident scenarios during design and testing as well as actual flight data.

💡 Research Summary

The paper introduces a new paradigm called “self‑forensics” aimed at enhancing the safety and reliability of flight‑critical systems such as commercial aircraft, spacecraft, and unmanned aerial vehicles. While traditional autonomic computing focuses on the four CHOP properties—self‑configuration, self‑optimization, self‑protection, and self‑healing—these mechanisms primarily address normal‑operation maintenance and performance tuning. They do not provide a systematic way to capture, preserve, and analyze evidence when abnormal events occur, which is essential for accident investigation, root‑cause analysis, and post‑mortem verification.

Self‑forensics fills this gap by enabling a system to automatically generate forensic evidence about its own internal state, sensor readings, control actions, and software stack during both design‑time testing and real‑time operation. The authors propose a domain‑specific language, Forensic Lucid, built on the Lucid family of data‑flow languages. Forensic Lucid extends the original paradigm with constructs for time‑continuous log streams, event triggers, and state‑transition modeling, allowing engineers to express complex incident scenarios as single logical expressions. At runtime, the language runtime engine translates raw telemetry and log data into “evidence objects” that are stored in a structured, queryable form.

Evidence preservation is tackled through a “forensic checkpoint” mechanism. When a critical condition is detected, the system snapshots its entire execution context—including raw sensor values, memory dumps, and software version information—and writes the encrypted checkpoint to both the primary flight data recorder (FDR) and a redundant secure storage medium. This dual‑write strategy guarantees tamper‑evidence and long‑term integrity, satisfying both engineering and regulatory requirements.

The paper also details how self‑forensics integrates with existing verification and validation (V&V) workflows. During model‑in‑the‑loop or hardware‑in‑the‑loop testing, engineers annotate simulation models with Forensic Lucid scripts that define “evidence insertion points.” As test cases execute, the runtime automatically produces evidence objects that are directly correlated with test outcomes, enabling quantitative assessment of requirement coverage and early detection of design flaws.

Two prototype case studies illustrate the approach. In the first, an altitude‑hold autopilot module experiences a sudden temperature spike in one engine. The self‑forensic subsystem records a checkpoint within two seconds, automatically correlates the temperature anomaly with the subsequent throttle reduction command, and injects a corrective maneuver into the FDR log. In the second case, a multi‑UAV swarm performs an emergency landing; forensic evidence from each vehicle is merged to reconstruct inter‑vehicle communication failures and control law conflicts, revealing a subtle timing bug that would have been missed by conventional post‑flight analysis. Both studies demonstrate a dramatic reduction in investigation time—from days to minutes—and a clear improvement in the reproducibility of findings.

Future work outlined by the authors includes standardizing Forensic Lucid for cross‑domain adoption (e.g., automotive, medical devices), developing high‑performance inference engines capable of real‑time causal reasoning, and establishing legal‑grade certification processes for forensic data handling.

In summary, self‑forensics extends autonomic computing by adding a systematic, language‑driven forensic layer that automatically captures, secures, and reasons about evidence of system behavior. This capability bridges the gap between design‑time verification and run‑time incident response, offering a powerful tool for engineers tasked with ensuring the safety of increasingly autonomous, software‑intensive flight systems.