More code review does not automatically mean worse code. But a review process that treats every valid comment as a required change can turn a small issue into a chain of fixes, added complexity, and new maintenance work. In a 2026 first-person account, developer Mei Hammer describes just that pattern; empirical studies find associations between review activity and code smells, but do not show that reviews cause them.
How can a correct review comment still lead to a bad change?
Hammer’s account begins with a project that accumulated 68 review comments over 10 rounds, resulting in 62 fixes. The fixes were individually defensible, but the sequence expanded the code to handle an edge case Hammer considered extremely unlikely. As Hammer puts it, “The reviewer was not wrong once. That turned out to be the problem.” This is the author’s description of a project experience, not an independently verified case study. Read Hammer’s account on DEV Community.
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The distinction is between identifying something real and deciding that changing it is worthwhile. A review can correctly point out a possible failure mode while the proposed fix costs more—in implementation effort, code complexity, or future maintenance—than the risk justifies. If each comment triggers another layer of defensive logic, the review may improve local correctness while making the overall design harder to understand.
What do studies say about reviews and code smells?
They show a relationship in some datasets, not a causal effect. A 2024 exploratory study analyzed pull requests from 25 Java projects and classified them using four smell types: god class, data class, long method, and long parameter list. It found that 37.1% of accepted PRs and 44.8% of rejected PRs were classified as smelly. The study also reported more discussion and review comments in smelly PRs. These figures describe that dataset, not software projects generally. See the study in Software: Practice and Experience.
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The association does not establish that review produced the smells. More complex or harder-to-understand changes could attract more comments and also contain more smells. The study authors note that smell detection is subjective: “code smells are not formally defined, and the interpretation can vary from one developer’s intuition to another.” The results therefore support paying attention to the relationship, not treating comment volume as proof that review harms code.
A 2018 quasi-experiment with 11 professional developers explored whether considering groups of smells could help reveal design problems. In that study, 36.36% of participants found more design problems when reasoning about multiple smells, while 63.63% reported fewer false positives. The authors also found that this analysis can be difficult and time-consuming without prioritization and visualization tools. The small sample and specific task limit how broadly those results can be applied. Read the study in the Journal of the Brazilian Computer Society.
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How should a team decide whether to act on a comment?
Hammer proposes evaluating the consequence of leaving the issue alone alongside the cost of addressing it. The point is not to dismiss review findings; it is to make the decision proportional to the risk and transparent about uncertainty.
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- Likelihood: How often is the relevant condition expected to occur, and what assumptions support that estimate?
- Cost to fix: What work and code changes are needed now? Does the solution expand scope or introduce machinery for a narrow case?
- Ongoing maintenance: How much future understanding, testing, or upkeep will the change require?
- Uncertainty: Which parts are measured, and which are estimates or judgment calls? State that distinction instead of disguising a guess as a precise rate.
For illustration, Hammer estimates a rare configuration-key collision at 0.01 incidents per year and the resulting maintenance burden at 0.5 hours per year. Those are the author’s illustrative estimates, not measured incident rates. Their value is in making the assumptions visible: a team can question the likelihood, impact, and expected upkeep before deciding whether a fix is proportionate.
Hammer also cautions that parts of the proposed triage routine and its thresholds were refined through argument rather than validated outcomes. The author’s exercise involving a second-grader was retrospective, not a live merge gate. Treat the routine as a prompt for better discussion, not a proven scoring system or a universal cutoff.
How can teams spot a chain of review-driven changes?
Counting comments or review rounds alone cannot show whether later changes are repeatedly reacting to code changed in earlier rounds. Hammer describes a script called chain-check, intended to identify comments that land on code changed after prior review rounds. The author reports that an earlier version surfaced defects and that its logic was revised. That is an author-reported tool account, not an independent evaluation of the script’s accuracy or effectiveness. See the tool discussion in Hammer’s article.
A useful process question is whether the team can see when a change triggered another review comment, which then led to a further change in the same area. That pattern does not prove the process is failing: some iterations resolve genuine issues. It does, however, create an opportunity to pause and ask whether the latest change still addresses a meaningful risk or is accumulating complexity to satisfy increasingly narrow possibilities.
What the evidence does—and does not—justify
The evidence supports a measured conclusion. Review comments and code smells can co-occur, and groups of smells may help some developers reason about design problems. Neither finding means that more review inherently creates worse code. Hammer’s example illustrates a plausible failure mode: a team can lose sight of the cost of successive fixes when it treats every correct observation as a mandate to change the code.
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The practical response is to distinguish observations from decisions. Ask what harm is prevented, how likely it is, what the change adds, and how confidently those costs can be estimated. The full triage approach and chain-detection idea remain proposals rather than a method shown to improve live projects with known outcomes.
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