🤖 AI Summary
This study addresses the challenges posed by rapid evolution in digital forensic systems and tools, which induces drift in evidentiary behaviors and tool outputs, thereby undermining result reproducibility and trustworthiness. To mitigate this, the authors propose a test-driven forensic methodology that introduces state-transition testing for causal attribution, encoding forensic expectations as executable specifications. The approach integrates virtual machine environments with computer vision–guided GUI automation to simulate authentic user interactions and verify system state changes. An open web platform is developed to facilitate sharing and replication of experiments. The method’s efficacy is demonstrated through five case studies, including a regression analysis across 25 versions of Autopsy, which uncovered numerous undocumented, substantial changes in its reporting output.
📝 Abstract
Digital forensic relies on validated tools and established procedures, yet the underlying operating systems, applications, and analysis tools evolve rapidly. This evolution can cause artifact behavior and tool outputs to drift, silently degrading repeatability and confidence in long-lived forensic interpretations. We present test-driven forensics, a practical approach that treats forensic expectations as executable specifications: expected artifacts and expected tool outputs are encoded as tests that can be rerun across versions to detect regressions. Crucially, our approach also enables State Transition Testing, validating the system's expected state after each user action rather than only performing post-mortem checks on a final disk image; this supports causal attribution and makes transient behavior testable. We implement the methodology in ADARE, an open-source framework that runs controlled experiments in virtual machines and simulates realistic user activity via computer-vision-guided GUI automation. ADARE includes a companion web platform for sharing experiments, environments, and results to facilitate independent reruns and peer verification. We evaluate ADARE in five case studies spanning artifact research and tool validation. In particular, a 25-version regression study of Autopsy reveals substantial, largely undocumented changes in exported report outputs, demonstrating how executable tests make drift measurable and reproducible at scale.