Modular Maturity Index
The Modular Maturity Index (MMI) is a metrics-based assessment model to systematically evaluate and improve the modularity and maintainability of a software architecture.
Classification
- ComplexityMedium
- Impact areaTechnical
- Decision typeArchitectural
- Organizational maturityIntermediate
Technical context
Principles & goals
Use cases & scenarios
Compromises
- Gaming risk: teams optimize metrics rather than real modularity (Goodhart’s law).
- Misinterpretation: high coupling is labeled “bad” even when domain context justifies it.
- Tool fetish: automated measurement does not replace architectural work and boundary communication.
- Start with a small set of explainable metrics and calibrate interpretation with the team.
- Focus on trends and deltas per change (detect regressions early).
- Always translate findings into concrete actions (owner, scope, expected effect, measurement point).
I/O & resources
- Defined module view (mapping code artifacts to modules/domains)
- Dependency data (build-time and/or runtime)
- Change data (commits, PRs, tickets) for co-change analysis
- MMI maturity view and hotspot map of modularity risks
- Prioritized action backlog (decoupling, boundary refinement, refactoring)
- Trend reporting (improvement/regression) as input to architecture governance
Description
The Modular Maturity Index (MMI) — associated with Dr. Carola Lilienthal — provides a structured way to assess “modularity” based on observable criteria rather than gut feel. It makes architectural quality visible through measurable signals such as coupling, cohesion, dependency structures, and change dynamics, and turns the findings into actionable improvement priorities. MMI is typically used when teams must operate and evolve a system over time: modularity directly affects changeability, testability, delivery speed, and risk. A practical MMI assessment combines (a) a consistent module/domain view (e.g., packages, components, services, or modules in a DDD sense) with (b) a metrics set and (c) a maturity model that translates results into actionable levels. Importantly, MMI is not a tool and not a single metric. It is a concept for architectural diagnosis and steering. Value emerges when teams run measurement as a continuous feedback loop: metrics are not used to “grade people” but as early indicators of technical risk and as navigation aids for refactoring, improving domain boundaries, and reducing coupling.
✔Benefits
- More objective discussions about architecture quality via explainable signals (e.g., coupling, cycles).
- Better refactoring prioritization because hotspots and risks become visible.
- Continuous quality steering: progress and regressions become measurable.
✖Limitations
- Results depend heavily on a meaningful module view; poor boundaries distort diagnosis.
- Metrics explain symptoms, not automatically root causes; interpretation and context remain necessary.
- A score can be overvalued; without an action backlog MMI becomes reporting only.
Trade-offs
Metrics
- Inter-module coupling density
Measures how strongly modules are connected through dependencies; high values indicate hard-to-separate responsibilities.
- Cyclic dependencies (cycle count / cycle size)
Captures number and size of cycles in the dependency graph; cycles hinder independent change and releases.
- Cross-boundary co-change rate
How often changes in one module trigger changes in others; an indicator of unstable or poorly drawn boundaries.
Examples & implementations
Hotspot-driven modularization in a monolith
MMI highlights a small set of highly coupled core areas as primary drivers of change risk. The team targets these hotspots (break cycles, stabilize interfaces) instead of broad restructuring.
Trend measurement as an early-warning system for architectural erosion
A monthly MMI check shows coupling and cyclic dependencies slowly increasing. The team reacts early with architectural work before delivery noticeably slows down.
Decision support for a microservices split
Before splitting into services, co-change and dependencies are analyzed. MMI results show which boundaries are stable and which would create a distributed monolith.
Implementation steps
Define module view and scope (granularity, mapping rules, naming conventions).
Collect baseline: dependency graph, cycles, coupling indicators, and co-change data.
Derive maturity, prioritize hotspots, and define actions as a backlog with target metrics.
Establish measurement as a feedback loop (monthly/per release) and translate trends into architecture work.
⚠️ Technical debt & bottlenecks
Technical debt
- Long-evolved cyclic dependencies that prevent independent releases.
- Cross-cutting logic (shared libraries/utils) as hidden coupling drivers.
- Unclear domain boundaries and missing ownership cause persistent co-change.
Known bottlenecks
Misuse examples
- Setting a management goal to “increase the score” without funding architectural work or capacity.
- Teams artificially reduce visible dependencies (e.g., copy/paste), hiding real coupling.
- Using MMI to justify a pre-decided reorg rather than evaluating options openly.
Typical traps
- Too much detail too early: an overly complex metric set causes analysis paralysis.
- Wrong granularity: too coarse hides issues, too fine creates noise.
- Tooling illusion: good numbers are mistaken for a substitute for clear boundaries and ownership.
Required skills
Architectural drivers
Constraints
- • A consistent module view must be defined and maintained (otherwise measurements drift).
- • Access to code, dependency, and change data is required (repos, build, tickets).
- • The organization must be willing to translate findings into architecture work (capacity/ownership).