Economic DORA: Practice-Level Analysis of DevOps Metrics in AI- Assisted Solo Development

I present an N=1 longitudinal case study of my own development practice using AI

coding assistants (Claude Code, Claude Chat) over 57 days (276 commits) to build a

production web application from zero to functional deployment. I introduce the

Economic DORA framework, extending traditional DevOps Research and

Assessment (DORA) metrics with token economics and granular method attribution.

Through retrospective git commit analysis and development chat log inference, I

identified five development practices with statistically large measured effect sizes:

(1) proactive Architecture Decision Record creation before feature implementation

(Φ = 0.89, p < 0.001), (2) structured problem agreement protocols (Φ = 0.63), (3)

systematic documentation updates after incidents (Φ = 1.0 for recurrence

prevention), (4) cross-session continuity tracking (estimated 20% time savings), and

(5) Day 1 methodology setup (associated with 80% fewer failures over 60 days).

A critical temporal finding emerged: methodology adoption timing mattered more

than methodology existence—proactive adoption was associated with zero initial

failures (0% failure rate across 3 features), while reactive adoption only prevented

recurrence but not the costly original incidents. I estimate 63% token cost savings

(1,600–2,100 tokens) and 71% time savings (2.5 hours) per feature when optimal

practices are followed, though these figures are based on estimated token costs and

require prospective validation.

I introduce PRISM (Performance, Recovery, Investment, Stability, Method), a

composite scoring system (0–100) extending traditional DORA by adding token

economics (Investment) as a first-class dimension. The first four components (P, R,

I, S) generate the scored composite, while Method Attribution serves as an

explanatory layer revealing why scores change. PRISM’s Investment component

(token cost per feature) provided leading indicators of degradation, declining from

20/25 (Elite) in September to 10/25 (Low) in November, one week before Change

Failure Rate spiked from 2.4% to 31.6%. Method Attribution—a tagging layer

tracking development approaches (PLANNED, QUICK, etc.)—revealed the rootcause: PLANNED commits dropped from 44% to 8%, correlating with PRISM

degradation (r = −0.94). This demonstrates that token cost increases combined with

methodology shifts can predict future failures, enabling proactive intervention

before quality collapses.

While limited to a single developer and greenfield project context, this work

contributes: (1) a novel framework combining DevOps metrics with AI economics,

(2) practice-level granular insights beyond aggregate method classification, (3)

complete transparency with open dataset and classification criteria, and (4) a

detailed replication protocol for N=20 validation studies. I openly acknowledge

limitations including retrospective classification bias, estimated (not measured)

token costs for the study period, and uncontrolled confounding variables, and I

invite critical peer review and independent replication.

I invite collaboration on prospective validation—contact me to participate in

N=20 replication study.