A refactoring can reduce duplication and still make changes more difficult if it adds confusing abstractions or shifts complexity elsewhere. Evaluating the outcome takes more than comparing a score or checking that the tests pass: you need to define what you want to improve, observe several signals, and assess whether the system is easier to change.
This guide explains how to measure the maintainability of a PHP application before and after refactoring, without turning metrics into standalone goals. The method works for a class, module, or business workflow, as long as you compare the same scope and record the context.
Why a single metric does not prove maintainability
Maintainability is the ability to understand, test, and modify a system with reasonable risk. No single indicator represents these capabilities on its own. Fewer lines do not necessarily mean less complexity; higher test coverage does not guarantee useful tests; and a favorable static analysis score does not reveal whether the team understands the design.
Metrics can also change for reasons unrelated to the refactoring: an updated tool, different analysis rules, changes in scope, or generated code included in the report. For this reason, a figure should be interpreted alongside its calculation method, the area analyzed, and known limitations.
The goal is not to prove that the change “improved the code” in the abstract. It is to check whether it solved a specific problem without introducing disproportionate risks.
Define what you want to make easier to change
Before measuring, formulate a verifiable, focused hypothesis. For example: “Separating the calculation rules from this class should make it possible to test them without starting the HTTP flow” or “Reducing this service’s dependencies should limit the changes needed to add a rule.” The hypothesis identifies an expected outcome; it does not prescribe an architecture just because it is fashionable.
Define the affected area and operations: classes, endpoints, commands, jobs, or business rules. Also note what is out of scope, such as an external integration or known technical debt that will not be addressed. Without that scope, a comparison may mix the effects of the refactoring with other changes.
It helps to translate the hypothesis into practical observations. Should the rule be easier to locate? Will it be possible to test it without a real database? Will fewer modules need to be modified for a common change? Choose a small number of questions the team can answer with evidence.
Establish a baseline before changing the code
Record the initial state at an identifiable repository revision, using the same tools and configuration you will use afterward. For a PHP area, it may be useful to combine these signals:
- Complexity: Review long methods, conditional branches, and points where decisions accumulate. Cyclomatic complexity can provide guidance, but it does not by itself describe how difficult a rule is to understand.
- Coupling: Identify direct dependencies, mixed responsibilities, and connections to infrastructure. Observe whether a change requires understanding or touching unrelated components.
- Duplication: Locate repeated logic, distinguishing accidental duplication from similar rules that should evolve separately.
- Tests: Record which important behaviors are covered, which tests are reliable, and which dependencies make a test require external services.
- Operations and delivery: Note relevant production failures or regressions, difficulties deploying the change, and associated manual steps, if they relate to the area.
If you use static analysis, keep a record of the tool name, rules, version, and command. A number without this information is not reproducible. Also separate generated or external code when the configuration allows it, and document the exclusions.
Choose verifiable indicators and record their limits
Select indicators that address the hypothesis. For a refactoring intended to isolate logic, useful indicators might include the number of dependencies needed to test it, tests covering relevant behaviors, and how long it takes a team member to locate the point that needs changing. To reduce coupling, you could observe how many components need to be modified for a representative task.
Do not treat times or counts as universal truths: task size, the person’s experience, and interruptions all have an effect. If you use them, record those conditions and treat the result as a signal, not as a comparison of productivity between people.
Document each indicator with four details: what it measures, how it is obtained, what result was expected, and what it cannot prove. For example, a dependency count may show a reduction, but it does not prove that the new abstractions are easy to understand. The technical review needs to check that.
Compare before and after using the same method
After the refactoring, repeat the analysis over the same scope, with the same configuration and, where possible, under equivalent conditions. Separate functional changes from structural ones in the review. If both were changed at once, identify which evidence corresponds to each and avoid attributing the entire result to the refactoring.
Alongside the metrics, test a real or representative change: fix a rule, add a business condition, or adapt an integration. Check which files had to be modified, which tests were needed, and where questions arose. There is no need to create an artificial task if comparable work already exists; however, avoid presenting an anecdotal impression as a conclusive result.
A consistent improvement combines signals: for example, fewer dependencies for testing a rule, more focused tests, and a change process the team can explain. If a method’s complexity decreases but the logic becomes more dispersed or the flow becomes harder to follow, the overall result needs review.
Combine measurements with review and team feedback
The technical review should ask whether responsibilities are clearer, whether names reflect the domain, and whether the new interfaces address a real need. It should also look for risks introduced: duplicated contracts, confusingly inverted dependencies, accidental behavior changes, or a new layer that only forwards calls.
Team feedback provides evidence about understanding and operations. Ask whether it is easier to locate the rule, write a test, or estimate the scope of a change. Tie responses to specific examples and record disagreements: an architecture can be clear to the person who created it and opaque to the person who maintains the system.
In PHP, also validate that the refactoring respects existing contracts: types, exceptions, side effects, serialization, and observable API behavior. A better-organized internal design does not make up for a functional regression or an unanticipated incompatibility.
Common mistakes when measuring maintainability
- Chasing coverage: Increasing the percentage can add superficial tests. Assess whether behaviors and risk cases are tested, not just whether lines are executed.
- Rewarding fewer lines: Compressing code can hide decisions and make changes harder to review. Size is not a direct measure of clarity.
- Optimizing a score: Adapting code to a tool may leave the design problem untouched. Investigate what the change means and confirm its effect in a real task.
- Comparing different scopes: Mixing new files, excluded code, or changed rules invalidates the comparison. Keep the method stable or explain the differences.
- Ignoring behavior: A refactoring must preserve expected behavior unless the functional change is defined and validated separately.
A short template for deciding what to do next
When you finish the evaluation, record:
- Area and objective: Which part was refactored and what you wanted to make easier to change.
- Baseline and method: Code revision, tools, configuration, scope, and exclusions.
- Observed signals: Results before and after, plus the limits of each indicator.
- Practical validation: Tests, technical review, and evidence from a representative change.
- Outstanding risks: Untested behavior, deferred technical debt, and operational effects to verify.
- Decision: Continue if the hypothesis holds and risks are acceptable; adjust if signals are mixed or there is an understanding problem; stop if the cost or risk exceeds the expected benefit.
The final question is not whether the code has a better score, but whether the team can modify that area with greater clarity and control. A reproducible baseline, several complementary signals, and an honest review make it possible to reach that decision without confusing refactoring activity with a demonstrated improvement.
