Do you dare to try Test-Driven Forensics? Increasing Trust in Desktop Forensics with ADARE
For digital forensic practitioners and tool developers, this work addresses the problem of silent degradation in forensic tool reliability due to rapid software evolution.
The paper introduces test-driven forensics, a methodology that encodes forensic expectations as executable tests to detect regressions and enable state transition testing. ADARE, an open-source framework implementing this approach, revealed substantial undocumented changes in Autopsy's exported report outputs across 25 versions.
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.