The short answer
Faster coding can move work into review, testing or release queues without improving end-to-end delivery. Analyse the whole value stream before declaring a productivity gain.
The question and scope
This article is an AI-assisted research-design draft for editorial review, not a report of a completed experiment. It asks how to evaluate ai software delivery productivity in a documented setting. No measurements, participants or client outcomes are claimed here.
An illustrative test case
Coding throughput increases while the review queue grows. Trace the same task from arrival to accepted release so the analysis does not stop at the stage that improved.
This scenario defines a case to examine; it is not an observed result.
A proposed investigation
Map task arrival, coding completion, review, acceptance and release. Compare matched periods or simulate changes in one stage's capacity. Include rejected work and rework in the timeline.
Record setup versions, inputs and outcomes in a run ledger. Use repeated trials and matched comparison cases; preserve failed runs as well as successful ones.
What to measure
- End-to-end lead time.
- queue time.
- rework.
- accepted throughput.
Retain raw observations and explain exclusions. Human grading needs a documented rubric; acceptance thresholds need a stated rationale.
Interpretation and limitations
Observational periods may differ in scope and staffing. Acknowledge those differences and separate scenario modelling from measured effects.
Distinguish a plausible mechanism from a demonstrated effect. Preserve uncertainty when the design cannot eliminate alternative explanations.
A practical review checklist
- Is the question narrow enough for the available system or dataset?
- Are the comparison conditions genuinely comparable?
- Can a reader trace each conclusion to an observation?
- Are the boundaries and unresolved assumptions visible?
- Does the decision change if the result is negative or inconclusive?
The publication artifact
The useful deliverable is a reproducible protocol, a measurement table and a decision note explaining what the evidence would change. Add actual results only after the investigation has been conducted and reviewed. This blueprint does not establish a novel finding or a provider-wide claim.
Sources and further reading
These primary resources are starting points for the concepts and controls involved. They do not validate the proposed experiment or its unmeasured outcomes. Check version-specific documentation when implementing the protocol.