Abstract DSFB does not compete with OpenTelemetry, Jaeger, Datadog, Sentry, ELK, Prometheus, Grafana, or any existing observability stack — it augments them. Those systems continue to operate unchanged. DSFB reads the residual streams they already produce — execution traces, structured logs, distributed spans, metric time-series, and exception telemetry — and returns a typed, deterministic, human-readable interpretation of what those residuals mean structurally. On the TrainTicket public microservice benchmark under a fixed Stage III read-only protocol, this augmentation collapses thousands of raw trace anomaly alerts into a small set of policy-governed Review/Escalate episodes while preserving the public fixture’s available structural transition signal. The upstream observability infrastructure is not modified, replaced, or disabled. If DSFB is removed, upstream behavior is unchanged. Software systems in production already produce dense residual streams through logging frameworks, distributed tracing, application performance monitoring, and error tracking, but operational debugging is still dominated by manual log triage and scalar threshold alerts that suppress temporal structure. This paper studies the DSFB Structural Semiotics Engine as a deterministic augmentation layer over those existing telemetry streams. It does not propose a replacement debugger, a new observability platform, or an AI-powered root-cause engine. Instead, it maps execution-trace residual trajectories into explicit objects — residual sign, admissibility envelope, grammar state, and provenance-aware motif entries — so that latency drift, memory-leak creep, error-rate escalation, and structural state-transition anomalies can be represented in a typed and inspectable form. The paper makes a bounded claim. It shows how deterministic intermediate representations can support auditability arguments and developer review, and how the DSFB formal objects can be instantiated using software debugging observables such as span-duration residuals, error-rate deviations, resource-consumption trajectories, and state-transition sequences. It does not prove ISO 25010 compliance, completed tool qualification, universal superiority over APM/AIOps/ML baselines, or physical root-cause attribution from public trace data alone. The bounded empirical claim is developer-facing: DSFB does not alter upstream observability logic, alert thresholds, or diagnostic tooling, but it converts a large trace-review surface into a small set of structured, human-readable debugging episodes. On the F-11 TrainTicket deployment-regression fixture, DSFB-Debug processes 35, 604 real Jaeger spans and, at post-Phase-8 9-axis bank-aware fusion with consensus N≥7, collapses 24, 919 raw cell-level alerts into one Layer-2 consensus episode and one Layer-3 typed-confirmed episode, with a clean-window false-episode rate of 0. 0023 and deterministic replay verified under Theorem 9. Across twelve vendored real-byte fixtures spanning nine upstream public datasets, the artifact establishes reproducibility anchors rather than universal production generalization. DSFB-Debug is deterministic, ML-free, and edge-oriented: it contains no neural network, no learned model, no training data, and no inference-time dependency beyond per-window arithmetic. Its operational core is noₛtd, zero-runtime-dependency, and forbid (unsafecode), with standard-library audit, notebook, benchmarking, and report paths separated from the noₛtd core. The result is a deterministic detector-field semiotics layer that sits above existing observability infrastructure and routes otherwise-discarded residuals through a 32-motif heuristics bank under a 9-axis bank-aware fusion configuration, producing replayable forensic evidence packets instead of opaque anomaly scores. Keywords: DSFB; Drift-Slew Fusion Bootstrap; deterministic debugging; software debugging; observability; OpenTelemetry; distributed tracing; Jaeger; trace anomaly detection; residual analysis; residual semiotics; structural semiotics; deterministic interpretability; deterministic replay; Theorem 9; read-only augmentation; non-ML anomaly interpretation; forensic evidence packet; detector-field fusion; multi-detector fusion; 9-axis fusion; heuristics bank; motif routing; routed evidence principle; anti-hallucination ladder; trace event collapse; review surface compression ratio; RSCR; microservice debugging; production telemetry; AIOps; application performance monitoring; log analysis; span-duration residuals; error-rate residuals; service reliability engineering; SRE; incident response; root-cause triage; auditability; reproducible research; noₛtd Rust; Rust crate; edge diagnostics; SBIR; mission software assurance; deterministic software assurance; NIST SP 800-53; public benchmark datasets; TrainTicket; TADBench; Defects4J; BugsInPy; PROMISE; AIOps Challenge; DeepTraLog; MultiDim-Localization; LO2; Illinois SocialNetwork
Riaan De Beer (Fri,) studied this question.
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