Decision-Driven vs. Spec-Driven Software Engineering

Which one is the sturdier foundation for agile, enterprise-grade products

TL;DR

• DDSE treats decisions as the system of record. It gives teams enforceable governance, contract-first parallel delivery, and CI/CD validation that keeps AI and humans aligned. (DDSE Foundation)
• Spec-Driven Development (SDD) treats the spec as the system of record. Tooling like GitHub’s Spec Kit turns specs into plans/tasks for coding agents, but deep governance, traceability norms, and contract-level guardrails are left to each team’s interpretation. (GitHub)

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1) What each method actually claims

DDSE—decisions as the single source of truth

The DDSE site and spec establish a typed hierarchy of Technical Decision Records (TDRs)—MDD, ADR, CDR, EDR, IDR, plus TDM—kept inside the repo and cross-linked to work and code. v1.1 adds Contract Decision Records (CDR), a Greenfield Architecture Pattern, and required decision-to-implementation traceability. Templates include a machine-parsable AI Context so agents follow constraints; CI pipelines must validate compliance. (DDSE Foundation)

SDD—specs as the system of record

GitHub’s Spec Kit defines SDD as turning specifications into executable, agent-driven plans and tasks via a CLI and agent prompts. The flow is: define principles → write the spec → plan the tech → derive tasks → implement with agents. It intentionally optimizes for activation energy and agent compatibility across tools like Copilot, Claude Code, Gemini CLI, and others. (GitHub)

Context: SDD echoes “executable specs” traditions (Specification by Example / ATDD) where examples/specs act as the truth. This heritage is useful for readers comparing mental models. (martinfowler.com)

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2) Side-by-side at a glance

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3) Evidence that matters in production

3.1 Enforceable alignment in CI/CD

• DDSE mandates CI stages for template validation, decision compliance, and AI-assisted review. That is governance you can run. (DDSE Foundation)
• SDD encourages checks (analysis, checklists) but leaves enforcement patterns to teams and agents. In practice, the rigor varies by org maturity. (GitHub)

Why this matters at scale: If you cannot prove alignment at merge time, drift creeps in. DDSE makes drift detection and gatekeeping a first-class, audited activity; SDD can match this only if your team designs that discipline on top.

3.2 Contract-first parallel delivery (cutting cycle time cleanly)

• DDSE’s CDR is a concrete, versioned contract primitive with mock servers, TypeScript type generation, and contract tests spelled out—purpose-built for FE/BE parallelism without breaking changes. (DDSE Foundation)
• SDD can describe contracts in the spec, but does not standardize a contract artifact with mock/type/test hooks. Teams must supply those practices. (GitHub)

Result: DDSE reduces hand-offs and rework in large programs. The CDR page even illustrates how AI “optimizations” that break response schemas are blocked by the contract guardrails. (DDSE Foundation)

3.3 Decision memory (institutional knowledge that survives)

• DDSE documents the rationale lineage from ADRs to broader Decision Memory, explicitly positioning decisions as organization-scale cognitive infrastructure for the AI era. (DDSE Foundation)
• SDD’s spec is also a knowledge artifact, but sources don’t prescribe a decision-level taxonomy, status model, or cross-reference graph on par with TDR–>code standards. (GitHub)

Why this matters for sustainability: When teams churn, why decisions were made is the first thing lost. DDSE’s decision graph creates a reusable substrate for audits, refactors, and onboarding—especially when AI is writing a lot of the code.

3.4 Alignment with agile reality (not ceremony)

• The DDSE home page maps decisions into sprint planning, standups, reviews, and retros—with metrics for decision effectiveness and continuous improvement. (DDSE Foundation)
• SDD emphasizes setup speed and agent flow; its agile integration exists, but is not spelled out as a governance cadence. (GitHub)

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4) Honest strengths of SDD (and when to pick it)

• Low activation energy. Teams can ship faster early by “writing a good spec” and letting Spec Kit orchestrate plans/tasks across many coding agents. (GitHub)
• Familiar mental model. For teams with “executable spec” DNA (SBE/ATDD), SDD feels natural, and can raise quality quickly by eliminating “vibe coding.” (martinfowler.com)

Use SDD when: you’re prototyping, exploring variants, or modernizing a contained subsystem with a small team and strong existing governance. The spec can carry you far—provided you supplement the missing controls.

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5) Where DDSE is clearly superior for enterprises

1. Audit-ready governance by design DDSE prescribes decision lifecycles, authority matrices, and CI-enforced checks. These are normative (MUST/SHOULD) parts of the spec—not optional playbooks. (DDSE Foundation)

2. Contract-bound AI and parallel teams The CDR model yields mock servers, types, and tests from the same source, letting front-end and back-end sprint in parallel without runtime schema surprises—even with AI in the loop. (DDSE Foundation)

3. Decision-to-implementation traceability DDSE requires code-level annotations that reference decisions, closing the loop for forensics, audits, and refactors. (DDSE Foundation)

4. Agile scaling without re-litigating architecture DDSE encodes decision backlogs and review cadences into sprint rituals; that dramatically reduces “architecture déjà vu” across squads and quarters. (DDSE Foundation)

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6) Potential gaps and how to mitigate them

• DDSE external validation. The site/spec is comprehensive, but independent benchmarks and large-scale case studies aren’t (yet) published on the site. Counter this by piloting DDSE quality gates (CI checks + CDR contracts) on one value stream and measuring drift, rework, and change-failure rate. (DDSE Foundation)
• SDD governance variability. SDD can be hardened—just add decision lifecycles, repo-resident records, and contract tests. In other words, you can evolve SDD toward DDSE—but that’s precisely the point: DDSE already bakes this in. (GitHub)

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7) Pragmatic adoption playbook

If you’re already running SDD/Spec Kit:

• Keep the spec as the stakeholder-friendly narrative, but introduce TDRs alongside it (ADR/EDR/IDR) and promote your API sections into formal CDRs. Enforce with CI checks. (DDSE Foundation)

If you’re new and want enterprise-grade from day 1:

• Follow DDSE’s Greenfield Architecture Pattern: MDD/ADR → CDR → parallel implementation guarded by types/mocks/contract tests. Wire the CI gates in week one. (DDSE Foundation)

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8) Conclusion

Both DDSE and SDD are serious responses to AI-accelerated development. If your priority is speed of first delivery, SDD with Spec Kit is a strong, pragmatic starting point. If your mandate is sustainable agility at scale—with provable governance, stable parallel delivery, and AI that cannot quietly diverge from your contracts—DDSE is the sturdier, more complete operating model. The spec tells the story, but decisions run the enterprise. (DDSE Foundation)

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References (key sources)

• DDSE site & spec v1.1: overview, agile integration, TDRs, AI Context, CI/CD, governance, and CDRs. (DDSE Foundation)
• Contracts in SDLC: CDR structure, mock servers, type generation, contract testing, and AI guardrails. (DDSE Foundation)
• DDSE Foundation GitHub: project home. (GitHub)
• Spec-Driven Development / Spec Kit: repo readme, methodology, agent support, CLI workflow. (GitHub)
• GitHub blog & Microsoft Dev Blog on Spec Kit context and goals. (The GitHub Blog)
• ADR lineage (Nygard/Cognitect) and SBE background for readers mapping mental models. (Cognitect.com)

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