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Code Audit & Code Quality
Know what's in the software before it goes into production.
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A large share of the code being written today comes from a language model. It looks finished, usually runs, and brings problems a classic review doesn't look for: dependencies that don't exist, checks that only look like checks, and migrations nobody can roll back. A code audit says what's actually in the codebase — evidenced, not estimated.
Code Audit & Code Quality: Why Provimedia is the right partner
What sets us apart in Code Audit & Code Quality.
Inventory
What's in the codebase, how it's connected, and where the work piles up
Security
Dependencies, secrets in the source code, inputs without validation
Technical Debt
Assessed by the future work it costs, not by line count
Test Coverage
Not the percentage, but whether the tests can actually fail
Delivery
Deploy, migration and new packages — the three places where it gets expensive
Report With Order
What comes first, what comes next, and what's deliberately left for later
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Code Audit & Code Quality: How we work
Our proven approach to Code Audit & Code Quality projects.
- 1
Access and Scope
Source code, dependencies, delivery path — and the question of what you actually want to know
- 2
Automated Review
Static analysis, dependency and secret scanning across the entire codebase
- 3
Manual Review
The parts no tool can judge: intent, scope, side effects
- 4
Report and Prioritisation
Findings with their location, sorted by impact, with a suggestion for first steps
- 1
Access and Scope
Source code, dependencies, delivery path — and the question of what you actually want to know
- 2
Automated Review
Static analysis, dependency and secret scanning across the entire codebase
- 3
Manual Review
The parts no tool can judge: intent, scope, side effects
- 4
Report and Prioritisation
Findings with their location, sorted by impact, with a suggestion for first steps
Code Audit & Code Quality: Technologies we use
What a Code Audit Answers
The question is rarely "is the code good". It's: what's actually here, where does the work pile up, and what will keep us busy next year. An audit answers that with findings backed by evidence rather than impressions — every finding can be traced, and anyone who disagrees can do so at the same spot.
Why AI-Generated Code Needs Its Own Review
A large share of the code being written today comes from a language model. It reads cleanly, follows conventions, and still carries patterns a human developer rarely produces: dependencies on packages that don't exist, error handling that swallows the error, and tests that are green without checking anything. A review calibrated for careless mistakes doesn't look for that.
Four Perspectives
Security asks about dependencies, secrets in the source code, and inputs without validation. Structure asks where the same thing was built more than once and where changes pile up. Test coverage doesn't ask for a percentage, it asks whether the tests can actually fail. And delivery asks about the three places where a mistake gets expensive: deploy, migration, and every new package.
What You End Up With
A report with findings, each with its location and the steps to reproduce it, ordered by impact. A list without an order is barely usable for a team that has to keep delivering at the same time. Whatever can be automated then belongs in the delivery pipeline — otherwise the result is outdated from the very next commit.
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Code Audit & Code Quality: Frequently asked questions
Answers to the most important questions about Code Audit & Code Quality.
What exactly does a code audit check?
An audit looks at four things: security (dependencies, secrets in the source code, unvalidated input), structure (where work piles up, what's built more than once), the state of testing (can the tests actually fail), and the delivery path. The result is a report with findings and a prioritized order.
How is this different from a code review?
A review assesses a change; an audit assesses the existing codebase. The review asks “is this change okay”, the audit asks “what's actually here, and which parts of it will come back to bite us”. Both need each other: an audit without ongoing reviews has to be repeated every year.
Why does AI-generated code need its own kind of review?
Because it's wrong in a different way. It reads cleanly, follows conventions, and still carries patterns a human developer rarely produces: packages that don't exist, error handling that swallows the error, and tests that pass green without actually checking anything. A review calibrated for careless mistakes won't catch that.
Do we get a list or an assessment in the end?
Both, and in this order: the findings with their location and the way to reproduce them, and on top of that, a ranking by impact. A list without a priority order is barely usable for a team that has to keep delivering alongside it.
Can we continue the review ourselves afterward?
Yes, and that's the point. An audit that only leaves behind a report goes stale after the very next commit. What can be automated belongs in the delivery pipeline; what needs judgment belongs in a review.