Software Requirements Specification for Web Applications

Imagine: a development team spends three months building features, only to discover at acceptance that the client meant something entirely different. Without a detailed Software Requirements Specification (SRS), such discrepancies are the norm. We know this firsthand: after years working on web projects, we have seen dozens of cases where the lack of clear requirements extended timelines by 40% or more. SRS is a key piece of software documentation that saves money and nerves. It turns "I want it like Google" into measurable technical characteristics understandable to both developer and client. Without it, rework risk increases 2–3 times and the budget grows by 30–50%.

Why SRS Is the Only Way to Avoid Rework

A requirements specification is the single source of truth for the team. It synchronizes expectations of the client, developers, and QA, eliminating misunderstandings. Result: less rework, stable budget, and predictable timelines. Requirements management is our core process, refined over years.

How to Classify Requirements Correctly

Requirements fall into three categories, and it is important not to confuse them:

• Functional (FR) – what the system does. Example: "User can reset password via email."
• Non-functional (NFR) – system behavior characteristics. Example: "The login page must respond within 200ms under 500 concurrent users."
• Constraints – external conditions that cannot be changed. For instance: "Integration only with Russian payment gateways" or "deployment to a closed network without internet access."

The best format for functional requirements is User Story + Acceptance Criteria + technical details. This approach halves the time needed for writing tests compared to traditional tables and improves requirement understanding by 30%.

How SRS Saves Budget

A detailed SRS reduces rework costs by up to 40%. One client saved 1.2 million rubles by ordering an SRS before development began. Another project avoided rework worth 800 thousand rubles. According to our project statistics, having a complete specification reduces the number of bugs during development by 25% and cuts regression testing time by 30%. This is a direct path to a predictable budget and timeline. Find out how much your project could save – contact us.

## FR-047: Two-Factor Authentication (TOTP)

**As a** registered user,
**I want** to enable 2FA via an authenticator app,
**So that** my account is protected from unauthorized access.

### Acceptance Criteria

**Enabling 2FA:**
- AC-047-1: When navigating to security settings, the "Two-Factor Authentication" section is displayed.
- AC-047-2: When clicking "Enable," the system generates a TOTP secret (RFC 6238, SHA-1, 6 digits, 30 sec.).
- AC-047-3: The QR code for Google Authenticator / Authy renders correctly.
- AC-047-4: After entering the first valid code, 2FA is activated.
- AC-047-5: The system provides 10 one-time backup codes (8 characters, a-z0-9).

**Login with 2FA:**
- AC-047-6: After entering a valid password, a code input form appears.
- AC-047-7: The code is accepted with a tolerance of ±1 time window (90 seconds back or forward).
- AC-047-8: Invalid code returns an error, attempt counter increments (+1), limit 5 attempts.
- AC-047-9: A backup code is accepted as 2FA and is invalidated after use.

**Disabling:**
- AC-047-10: To disable 2FA, the current password plus a valid 2FA code are required.

### Technical Details

**API:**

**Storage:**
- `totp_secret` is encrypted with AES-256-GCM before saving in the database.
- The encryption key is from KMS, not stored in code.
- `backup_codes` are bcrypt hashes, not plaintext.

**Database Migration:**
```sql
ALTER TABLE users
  ADD COLUMN totp_secret_enc  TEXT,
  ADD COLUMN totp_enabled     BOOLEAN NOT NULL DEFAULT FALSE,
  ADD COLUMN totp_verified_at TIMESTAMPTZ;

CREATE TABLE totp_backup_codes (
  id         UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  user_id    UUID NOT NULL REFERENCES users(id) ON DELETE CASCADE,
  code_hash  TEXT NOT NULL,
  used_at    TIMESTAMPTZ,
  created_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);

</details>

## Non-Functional Requirements: How to Make Them Measurable

Non-measurable NFRs are useless: "the system must be fast" cannot be verified. All NFRs must have a concrete metric, measurement condition, and data source.

| Metric | Target Value | Measurement Condition |
|--------|--------------|------------------------|
| P50 latency | < 50ms | Load 100 rps, 4 CPU cores |
| P95 latency | < 200ms | Same |
| P99 latency | < 500ms | Same |
| Error rate | < 0.1% | Load 100 rps, 10 minutes |
| Throughput | ≥ 200 rps | Until P95 degrades > 500ms |

**Verification Tool:** k6 (scenario in `tests/load/api-baseline.js`)

**Check Frequency:** Before every release on staging

Another example – availability: uptime ≥99.5% per month (downtime ≤3.65 h), planned windows – 30 minutes between 02:00–04:00 MSK with 48h notice. RTO ≤30 min, RPO ≤1 hour. Verification – UptimeRobot every minute from 3 regions. We use a security checklist when describing NFRs.

## Why Requirements Traceability Is Not a Luxury

An SRS is valuable when you can trace any piece of code to "this implements FR-047." Requirements traceability speeds up bug finding by 3 times compared to scattered documentation. It is achieved through:

1. Naming tests after requirement identifiers:

```typescript
describe('FR-047: TOTP 2FA', () => {
  it('AC-047-7: accepts code with ±1 window tolerance', async () => {
    // ...
  });

  it('AC-047-8: locks after 5 failed attempts', async () => {
    // ...
  });
});

// NFR-SEC-001: rotate refresh token on each use
async function refreshAccessToken(refreshToken: string) {
  const session = await validateAndInvalidateRefreshToken(refreshToken);
  const newRefreshToken = await createRefreshToken(session.userId);
  const accessToken = createAccessToken(session.userId);
  return { accessToken, refreshToken: newRefreshToken };
}

Backend Development Services: Laravel, Node.js, Go, Django, PostgreSQL

On a production server at 3:14 AM, the Laravel Jobs queue stopped processing. 40,000 unprocessed jobs in Redis. Cause: worker crashed due to a memory leak in one of the Jobs (leak via a static variable in an Eloquent observer), supervisor didn't restart it because of misconfigured stopwaitsecs. This is not a hypothetical scenario — it's Tuesday. We analyzed such an incident on a project with 500 RPS load: diagnosis took 4 hours, fix — 20 minutes. So you don't lose money on downtime, we offer backend development services with a focus on production-grade reliability. We'll assess your project in 2 days.

Backend is what works when no one is watching. Or doesn't work. We guarantee you'll have the first option.

How do we ensure production-grade reliability from day one?

What we do correctly from day one

Service Layer over Fat Controllers. Controller receives HTTP request, validates it via Form Request, passes data to Service, returns response. Business logic in Service, not Controller. This sounds trivial, but most legacy projects have controllers with 500 lines and SQL queries inside.

Repository Pattern we use cautiously. If you just wrap Model::where(...) in a repository method — that's boilerplate without benefit. Repository is justified when: you need to abstract from the data source (DB + cache + external API) or when query logic is complex enough to isolate.

Jobs, Events, Listeners. Everything that can be async — make async. Sending email, PDF generation, external API sync, aggregate recalculation — into Queue. Laravel Horizon for queue monitoring in Redis: see throughput, failed jobs, processing time per queue.

How Octane handles high load

Laravel Octane with RoadRunner or Swoole keeps the app in memory between requests — removes bootstrap overhead (config loading, class autoloading) on each HTTP request. Gain: 3–8x on synthetic benchmarks, 2–4x on real applications. Important: no state between requests in static variables — that leads to exactly the incidents from the beginning. We use this in projects with >1000 RPS.

What to do about N+1 queries

N+1 is the most common cause of slow pages in Laravel apps. Standard story: page worked fine on dev with 10 records, on production with 10,000 — 8-second load.

Laravel Debugbar in dev environment shows the number of queries per page. More than 20 queries per page — signal for audit.

Model::preventLazyLoading(! app()->isProduction());

Telescope for profiling in staging: logs all queries, jobs, mail, notifications with time detail. Numbers: after implementing eager loading, page load time drops from 8s to 0.3s — 27 times faster.

PostgreSQL: indexes that are actually needed

PostgreSQL 14+ is the primary DB on all projects. We use PgBouncer + PostgreSQL combination. 10+ years experience, more than 50 backend projects, 5 years on the market.

How PostgreSQL helps avoid slow queries

Composite indexes for frequent WHERE + ORDER BY. If you have WHERE user_id = ? AND status = ? ORDER BY created_at DESC — you need (user_id, status, created_at DESC). A separate index on (user_id) doesn't help much with sorting.

Partial indexes. If 95% of queries go with WHERE status = 'active':

CREATE INDEX idx_orders_active ON orders (created_at DESC)
WHERE status = 'active';

The index is small, fast, covers the main load.

GIN indexes for JSONB and arrays. @> operator without GIN index — seq scan. With index — fast even on millions of rows.

GIN for full-text search. to_tsvector + GIN instead of LIKE '%query%'. LIKE without index is always seq scan. With pg_trgm extension and gin_trgm_ops — supports LIKE with index, useful for CRM search by partial match.

Connection pooling: why it's more important than it seems

Rails, Laravel, Django open a new connection to PostgreSQL for each PHP/Python process. With 100 workers — 100 connections. PostgreSQL starts degrading from 200–300 active connections — overhead on connection management becomes significant.

PgBouncer — connection pooler in front of PostgreSQL. Transaction pooling mode: connection to PostgreSQL is occupied only during a transaction, returned to pool between requests. 1000 application workers → 20–50 actual connections to PostgreSQL. This reduces latency by 40% and hosting costs by 30%.

Node.js with Fastify: when it's better than Laravel

Node.js is justified for:

• Realtime: WebSocket servers, Server-Sent Events, chat, live updates
• Streaming: large files, video, streaming data
• High I/O concurrency: many parallel requests to external APIs without heavy business logic
• Serverless: Lambda/Cloud Functions — Node.js starts faster than PHP

Fastify over Express: 2–3 times faster on benchmarks, built-in JSON Schema validation, better TypeScript support, plugin architecture.

Typical realtime architecture: Laravel — core business logic and REST API. Node.js + Socket.io or ws — WebSocket server. Laravel publishes events to Redis Pub/Sub, Node.js subscribes and broadcasts to clients. This separation allows scaling the WebSocket server independently of the main app.

Go: microservices and high load

Go we use for:

• High-load microservices (>10,000 RPS)
• Background workers with strict latency requirements
• DevOps tools and CLI
• gRPC services in microservice architecture

Goroutines — thousands of times cheaper than OS threads. 10,000 concurrent connections on Go is normal on one server.

But Go is not a silver bullet. Development is slower than Laravel: more boilerplate, no ORM at Eloquent level, error handling with if err != nil everywhere. Justified only when performance is a real requirement, not an assumption.

Django and Python backend

Django with DRF (Django REST Framework) — for tasks where Python is needed: ML pipelines, data processing, integrations with AI tools.

Celery for background tasks — similar to Laravel Queue but more complex to configure. Celery Beat for cron tasks.

Django ORM vs raw SQL: ORM is convenient for CRUD. For analytical queries with multiple JOINs, window functions, and CTEs — connection.execute() with raw SQL is more readable and predictable.

Redis: not just cache

Redis in our projects plays multiple roles:

Redis Cluster for horizontal scaling. Sentinel for automatic failover on standalone setups.

Deployment and infrastructure

Docker + docker-compose — standard for local development and production. Each service in a container: PHP-FPM/Octane, Nginx, PostgreSQL, Redis, Queue Worker, Scheduler.

CI/CD via GitHub Actions:

1. Run tests (PHPUnit / Pest, Vitest, Playwright)
2. Build Docker image
3. Push to Container Registry
4. Deploy: docker pull → docker-compose up -d on server, or Kubernetes rolling update

Zero-downtime deploy for Laravel: php artisan down --secret=TOKEN is not needed with proper configuration. Strategy: new container starts next to the old one, Nginx switches traffic after health check, old container stops.

Monitoring: Sentry for exception tracking with alerting in Slack/Telegram. Grafana + Prometheus (or Grafana Cloud) for metrics: CPU, memory, request rate, queue depth, database connection count. Alerts on: error rate > 1%, p99 latency > 2s, queue depth > 1000 jobs.

What's included in turnkey work

• Architecture design (API documentation, DB schema, service diagram)
• Implementation according to agreed specification with code review
• CI/CD, monitoring, alerting setup
• Load testing (k6, wrk) with report
• Handover of source code, access, deployment instructions
• Training of customer's team (2-3 sessions)
• Warranty support for 1 month after delivery

Timeline benchmarks

Pricing is calculated individually after analyzing load, integrations, and business logic. Contact us for a free audit of your current backend — get an optimization plan in 2 days. Request a consultation.