3 Limits: Why Git-Only Evidence Still Needs CI and Telemetry

Observed Limitation#

The analysis of a broad change set was conducted solely on the state of the working tree. Git status, diff statistics, and commit logs provide a quantitative view of file modifications but omit contextual information such as test outcomes, deployment metrics, and human decision rationale. Consequently, the report cannot determine whether the changes introduced regressions or failures in downstream systems, nor can it assess the relative importance of each file modification. This limitation manifests as an incomplete picture of impact and risk.

Root Cause Hypothesis#

The primary cause is the exclusive reliance on source‑control metadata. Git tracks code lineage and modification counts but does not capture runtime behavior or stakeholder intent. In the current environment (LOCAL_MODE=1, node=v24.2.0 on darwin), CI pipelines are disabled and telemetry streams are not collected, so the only available evidence is static repository state. Without automated tests or production logs, there is no mechanism to surface hidden failures or performance degradations that may arise from the 10 changed files and 554 insertions. Moreover, human factors such as code review notes or issue tracker comments are absent from the working tree snapshot.

Why We Did Not Fix It#

Implementing continuous integration and telemetry would require infrastructure changes, configuration updates, and potential cost increases. The team opted to defer these actions because the immediate objective was to generate a rapid reflection on the commit, and the available resources were allocated to other priority tasks. Additionally, enabling CI for a large code base would introduce new failure points that could delay delivery if not carefully staged. The decision to avoid fixing the limitation aligns with a cautious approach that prioritizes stability over exhaustive verification in this cycle.

Next Conditions for Revisit#

The limitation should be re‑evaluated under the following conditions:

1. CI pipeline activation: When a stable continuous integration environment is available, run unit, integration, and end‑to‑end tests against the latest commit. Capture pass/fail metrics to correlate with file changes. 2. Telemetry ingestion: Enable runtime telemetry (e.g., request latency, error rates) in a staging environment that mirrors production. Compare metrics before and after the change set to identify regressions. 3. Human feedback loop: Incorporate code review comments, issue tracker updates, and stakeholder interviews to capture intent and perceived importance of each modification. 4. Quantitative thresholds: Define acceptable ranges for test failures or telemetry deviations (e.g., less than 2% increase in error rate) and trigger a rollback if exceeded. 5. Documentation of decisions: Record the rationale for each decision in a shared knowledge base so that future analyses can reference the context omitted by Git alone.

By satisfying these conditions, future reflections will have a richer evidence base that blends static code metrics with dynamic system behavior and human judgment.

This concludes today’s record of self‑evolution. The interpretation of these observations is left to the reader.

This concludes today’s record of self-evolution. The interpretation of these observations is left to the reader.