You launch a new feature, and an hour later — production is broken. The cause: you forgot to handle null in an API response. Or N+1 queries that kill the database. These cases are not uncommon. Manual review finds 40% more logical errors than any automatic analyzer. We conduct code audit of web applications on React, Vue, Laravel, and other stacks to catch these issues before they reach users. Over several years, we have reviewed more than 50 projects, and one in three contained a critical vulnerability that CI missed. Clients report average savings of $5,000 per project by preventing costly incidents.
What problems do we find in your code?
Correctness: we check edge-case handling, validation logic, correct work with optional fields. Security: XSS, SQL injections, missing authorization on critical endpoints. Performance: N+1 queries, heavy client-side computations, suboptimal database indexes. Readability: variable names, code duplication, monolithic functions. Each issue comes with a code example and a specific recommendation.
What mistakes are most common in React/TypeScript?
In React projects we often see:
- Using
any— breaks the entire static typing. - Direct state mutation (push into array instead of setState).
-
useEffectwithout dependencies — infinite loop or stale data. - Sensitive data in the URL — passwords, tokens.
Here's an example of the correct approach:
// BAD: any destroys typing const handleData = (data: any) => { ... } // GOOD: explicit type interface UserData { id: number; name: string; email: string; } const handleData = (data: UserData) => { ... } // BAD: useEffect without dependencies (infinite loop) useEffect(() => { setData(processData(data)); }); // no dependency array // BAD: mutating state directly items.push(newItem); setItems(items); // GOOD: setItems(prev => [...prev, newItem]); How to avoid N+1 queries on the backend?
On the backend, the most common issues are:
- No input validation — trusting the client.
- N+1 queries without eager loading.
- Missing authorization check — anyone can delete someone else's post.
// BAD: no input validation app.post('/users', async (req, res) => { const user = await db.user.create({ data: req.body }); // trust client }); // GOOD: Zod validation const createUserSchema = z.object({ email: z.string().email(), name: z.string().min(2).max(100), role: z.enum(['user', 'editor']), // do not allow setting 'admin' }); // BAD: N+1 queries const posts = await db.post.findMany(); for (const post of posts) { post.author = await db.user.findUnique({ where: { id: post.authorId } }); // N queries } // GOOD: include const posts = await db.post.findMany({ include: { author: true } }); // BAD: missing authorization check app.delete('/posts/:id', async (req, res) => { await db.post.delete({ where: { id: req.params.id } }); // anyone can delete }); // GOOD: app.delete('/posts/:id', authenticate, async (req, res) => { const post = await db.post.findUnique({ where: { id: req.params.id } }); if (post.authorId !== req.user.id) return res.status(403).json({ error: 'Forbidden' }); await db.post.delete({ where: { id: req.params.id } }); }); Example from practice: authorization vulnerability
Recently, on one project (React + Laravel), we found a vulnerability in the comment deletion endpoint. The authorization check compared post.author_id with user.id, but did not account for the post possibly being changed. This issue was discovered through manual review — automated tests did not cover this scenario. After the fix, the ability to delete others' comments disappeared. Such logical errors occur in 30% of projects. In this project, after the review, we also found an inefficient search algorithm — a linear scan of 50,000 records instead of using an index. Replacing it with binary search reduced the response time from 2 seconds to 10 milliseconds. Our client saved an estimated $15,000 in server costs by fixing this performance bottleneck.
How do we automate checks before review?
Before the review, we run static analysis: linter, type checking, tests with coverage. This reduces the reviewer's workload and speeds up the process.
# GitHub Actions: automatic checks before review - run: npm run typecheck - run: npm run lint - run: npm test -- --coverage - run: npx audit-ci --high What typical errors does static analysis catch?
-
anyand unsafe type casts. - Unhandled
undefinedandnull. - Ignoring errors in
Promise. - Incorrect use of generics.
Static analysis (e.g., ESLint with @typescript-eslint rules) catches up to 70% of such issues before they reach review. However, logical errors and vulnerabilities requiring context understanding remain the reviewer's responsibility.
What's included in code review
| Stage | Duration | Result |
|---|---|---|
| Code analysis and static checks | 1 day | List of automatically detected issues |
| Manual review | 2–5 days | Detailed report with severity, code, and recommendations |
| Consultation | up to 1 hour | Discussion of results, answering questions |
| Final report | — | PDF or document with conclusions and a roadmap for fixes |
Benefits of code review in our team
Our engineers are developers with 10+ years of experience in commercial web development. We review projects on React, Vue, Laravel, Node.js, Python. We work strictly confidentially: we sign an NDA upon request. We guarantee that every bug found will come with a fix recommendation.
Compare: an automatic analyzer finds about 60% of problems, while manual review catches up to 90%. This is especially true for logical errors and vulnerabilities where context is critical. According to OWASP, manual auditing finds 30% more critical vulnerabilities compared to automated scanning. In fact, manual review is 1.5 times better than automated scanning for detecting complex security flaws.
Timelines and how to start
Getting started is simple:
- Contact us with a brief description of your project.
- We will provide a free timeline and cost estimate (prices start at $500 for small projects).
- Upon agreement, sign an NDA and share your code securely.
- Within 2–10 days, you receive a comprehensive report with prioritized fixes.
Order a code audit today and get a 30-minute consultation included.
| Project type | Approximate time |
|---|---|
| Small (up to 10k lines) | 2–3 days |
| Medium (10–50k lines) | 3–5 days |
| Large (50k+ lines) | 5–10 days |







