Measuring Technical Debt Reduction with Automated Repairs

Audits are treated as a cost because the benefit never shows up in the data

Teams run static analysis, collect warnings, and hand a report to management. The report lives in a ticket, never leaves the backlog. Stakeholders ask, “What did we gain?” and the answer is “nothing we can measure”. The failure is not the audit itself; it is the missing metric that proves a repair paid back technical debt.

Capture a baseline of load‑bearing code

Technical debt is only a problem when it touches code that the system relies on at runtime. Identify that subset with execution tracing: run the test suite or a representative workload while the harness records which functions are exercised and how often.

shpbl-harness run --trace ./repo

The harness emits a CSV of function,callCount,averageTimeMs. Sum the call counts for all functions that belong to a module you plan to repair. In a typical service this “load‑bearing” total might be 1.2 million calls per day, with an average latency of 12 ms.

Record this as pre‑repair load‑bearing impact. It is a concrete metric, not a vague “risk” score.

Apply the automated repair and measure the change

Submit the audit JSON to the Build Intent gate. If the gate returns a PR, merge it and let the same harness run again on the updated code.

The post‑repair CSV will show new call counts and timings. Compare the two files:

• If the repaired module’s call count drops from 800 k to 720 k, the code path is being short‑circuited – a direct reduction of load‑bearing work.
• If the average latency falls from 13 ms to 11 ms, the repair has reduced the time spent per call.

Compute repair impact as the percentage change in the product of call count and latency (a proxy for total time spent in the module).

preImpact  = 800000 * 13 = 10.4 M ms
postImpact = 720000 * 11 = 7.92 M ms
impactΔ    = (10.4‑7.92) / 10.4 ≈ 24 %

A 24 % reduction indicates a measurable decrease in load‑bearing work. It can be taken as an indicator that the repair mitigates technical debt, but it does not constitute proof that the debt has been fully repaid. Because the numbers come from the same harness, they are repeatable and model‑independent.

Report the debt‑payback in a way that convinces stakeholders

Stakeholders care about outcomes, not raw traces. Translate the impact into business‑relevant terms:

• Reduced CPU‑seconds – multiply the saved milliseconds by the number of daily executions.
• Lower cloud cost – map the CPU‑seconds to the provider’s pricing tier.
• Improved SLA headroom – show the margin added to the latency budget.

Present a one‑page summary: baseline impact, post‑repair impact, and the derived cost saving. Link the repair to the audit that produced it, for example the workflow described in the post From audit report to merged repair branch in one step. When the numbers line up, the audit moves from a cost centre to an asset that demonstrably reduces technical debt.

Monday action: run a quick baseline audit

1. Pick a repository that has a test suite.
2. Use the free repository evaluation lane to generate an audit JSON: /evaluation.
3. Run the harness with --trace and export the CSV.
4. Note the total call count and average latency for the top‑three modules.

With those figures in hand you can start estimating repair impact before any code changes. The steps can be completed within a short work session and give you a metric that can be tracked after the first automated repair.