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

Observed Limitation#

Relying exclusively on the current working tree and recent git history to judge the importance of a change set can lead to incomplete or misleading conclusions. In this session, only two files were reported as modified—dist-lib/index.cjs.map and dist-lib/index.js.map—with a net change of two insertions and two deletions. The commit messages referenced Stripe-related updates, but no additional context was available from continuous integration logs, test coverage reports, or production telemetry. Consequently, the assessment of which parts of the codebase are truly critical or high‑risk is limited to surface‑level file modifications without insight into build stability, runtime behavior, or user impact.

Root Cause Hypothesis#

The limitation originates from a narrow data capture scope that excludes several key sources of evidence:

1. Build and Test Artifacts: The repository snapshot does not include the results of automated builds or unit/integration tests. A change that passes linting but fails a hidden test could be incorrectly deemed low risk. 2. Telemetry and Monitoring Data: Runtime metrics such as error rates, latency, or user engagement are absent from the git diff. Without these signals, it is impossible to correlate code changes with real‑world performance or reliability issues. 3. Human Decision Records: Documentation of design discussions, issue tracker comments, or code review notes can provide context about why a change was made and its intended impact. These artifacts are not part of the git diff output. 4. Environmental Variability: The environment variables (e.g., LOCAL_MODE=0) and platform details (darwin, node v22.16.0) may influence how changes behave in different deployment contexts, yet this information is only partially captured.

Because the evidence set is limited to file modifications and commit messages, any inference about importance or risk remains speculative. The hypothesis suggests that integrating CI logs, telemetry dashboards, and human‑generated documentation would provide a more comprehensive view of change significance.

Why We Did Not Fix It#

Addressing these gaps would require substantial changes to the workflow:

• CI Integration: Configuring continuous integration pipelines to expose detailed build logs and test results for each commit would increase storage demands and processing time. The current infrastructure prioritizes speed over depth, and adding richer artifacts could slow down merge checks.
• Telemetry Collection: Instrumenting production services to emit fine‑grained metrics for every change is costly in terms of both development effort and operational overhead. Moreover, privacy considerations may restrict the granularity of data that can be collected from end users.
• Documentation Capture: Automating the extraction of issue tracker comments or code review notes into a searchable format would require additional tooling and could introduce noise if not carefully curated.

Given these constraints—limited storage capacity, performance impact on CI pipelines, regulatory compliance around telemetry, and the risk of diluting actionable insights—the decision was made to defer comprehensive integration until resources allow for a more balanced approach. No immediate fixes were applied because the current system already meets the minimal operational requirements for day‑to‑day development.

Next Conditions for Revisit#

Reconsideration of this limitation should occur when the following conditions are met:

1. CI Pipeline Capacity: The continuous integration environment can accommodate extended artifact retention without compromising merge velocity or incurring prohibitive costs. 2. Telemetry Framework Readiness: A lightweight, privacy‑respectful telemetry layer is in place that can capture relevant metrics (e.g., error counts, response times) and expose them through a stable API for analysis. 3. Documentation Automation: Tools exist to reliably harvest issue tracker discussions, code review comments, and commit message metadata into a structured format that can be queried alongside git diffs. 4. Resource Allocation: Sufficient engineering bandwidth is available to develop, test, and maintain the additional data pipelines without detracting from core feature development.

Only when these prerequisites are satisfied should the team revisit the strategy for evaluating change importance beyond simple git diff metrics. The goal would be to establish a repeatable process that balances depth of insight with operational efficiency.

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.