Problems Solved

Obvyr addresses four critical testing challenges that affect engineering teams at every scale. Here's how Obvyr transforms each problem from a persistent pain point into a solved challenge.

1. Flaky Test Detection and Resolution

The Problem

Scenario: Your test suite has a test called test_user_authentication. Sometimes it passes. Sometimes it fails with a timeout. When it fails, you re-run CI—and it passes. The team labels it "flaky" and moves on.

What Actually Happens:

• Developers waste 30-60 minutes investigating each failure, only to discover it's "just flaky"
• Real authentication bugs get masked by the noise of flaky failures
• Teams lose trust in the entire test suite and start ignoring failures
• Eventually, a real authentication bug ships to production because everyone assumed it was another flaky failure

The Traditional Approach:

1. Manually track which tests are flaky (usually in a spreadsheet or tribal knowledge)
2. Investigate flaky tests when someone has time (rarely happens)
3. Disable particularly problematic tests (reducing actual coverage)
4. Hope the problem resolves itself (it doesn't)

Cost to Your Team:

• Time: 5-10 hours per week of senior developer time debugging false negatives
• Quality: Real bugs slip through because teams ignore failures
• Morale: Developers lose confidence in testing infrastructure
• Deployment: Can't deploy confidently because test failures might be noise

The Obvyr Solution

Comprehensive Pattern Detection:

Obvyr collects every execution of test_user_authentication:

• ✅ Passed: 847 times (84.7%)
• ❌ Failed: 153 times (15.3%)
• 📊 Total executions: 1,000 across 45 developers and 12 CI runners

But here's what Obvyr reveals that point-in-time results miss:

Failure Pattern Analysis:

Environment:     Local: 12 failures (3% fail rate)
                 CI:    141 failures (22% fail rate)

Timing:          91% of failures occur between 2-5 seconds
                 92% of passes complete in < 1 second

User Pattern:    Developer "alex": 0 failures in 67 runs (local)
                 Developer "sam": 28 failures in 89 runs (local)
                 CI runner "ci-3": 87 failures in 134 runs

Root Cause Identified:
- CI runner "ci-3" has network latency to auth service
- Developer "sam" has outdated local test data
- Authentication timeout set too aggressively at 2s

Obvyr shows you:

1. It's not random: Failures correlate with specific CI runner and specific developer
2. It's environmental: Different failure rates between local and CI
3. It's timing-related: Failures cluster in 2-5 second range
4. The fix is clear: Increase timeout to 6s, fix CI runner "ci-3" network, update sam's test data

Before vs. After Obvyr

Before:

• "This test is flaky, we'll fix it when we have time"
• Weeks of wasted debugging on each occurrence
• Team ignores all authentication test failures
• Real authentication bug ships to production

After:

• Obvyr identifies pattern within first 50 executions
• Root cause identified in minutes, not weeks
• Targeted fix resolves issue permanently
• Team regains confidence in authentication tests

Measurable Impact:

• ⏱️ Time saved: 8 hours/week of debugging time → 0 hours
• 🎯 Quality improved: 0 flaky tests in production → confidence restored
• 📈 Deployment velocity: Can deploy when tests pass (no more "probably just flaky")

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2. Environment Divergence Resolution

The Problem

Scenario: Your CI pipeline is green. All tests pass. You deploy to production. The payment processing feature breaks immediately. The error? A configuration file that exists in local and CI environments but not in production. The tests never caught it because they assumed the file existed.

What Actually Happens:

• Local environment has different dependencies than CI
• CI environment has different configuration than production
• Tests pass everywhere except where it matters—in production
• Post-mortems conclude "we need better integration testing" (but how?)

The Traditional Approach:

1. Try to manually keep environments in sync (impossible at scale)
2. Run E2E tests in staging that "should" match production (they don't)
3. Hope local development environment matches CI (it doesn't)
4. Debug production issues that "passed all tests"

Cost to Your Team:

• Incidents: 2-5 production issues per month from environment drift
• Trust: Engineering team stops trusting test results
• Time: Hours debugging "worked in CI" production failures
• Velocity: Deployment fear leads to slower release cycles

The Obvyr Solution

Systematic Environment Comparison:

Obvyr collects test execution data from every environment and reveals systematic differences:

Payment Processing Test Analysis:

Test: test_payment_processing_happy_path

Local Development (125 executions):
✅ Pass rate: 100%
⏱️ Avg duration: 0.8s
📁 File dependencies: /config/payment.yml (present)
🌐 External calls: mock payment gateway

CI Environment (89 executions):
✅ Pass rate: 100%
⏱️ Avg duration: 1.2s
📁 File dependencies: /config/payment.yml (present)
🌐 External calls: staging payment gateway

Production Environment (First deployment):
❌ Pass rate: 0%
⏱️ Avg duration: N/A (immediate failure)
📁 File dependencies: /config/payment.yml (MISSING)
🌐 External calls: production payment gateway

Environment Drift Detected:
⚠️ Configuration file present in local + CI, absent in production
⚠️ Different payment gateway endpoints across environments
⚠️ Production has stricter network timeouts (2s vs 5s in CI)

Obvyr reveals:

1. The specific divergence: /config/payment.yml exists locally and in CI but not in production
2. When it diverged: Production environment missing file since initial setup
3. Impact scope: Affects all payment tests, not caught because tests run in non-production environments
4. Prevention strategy: Add deployment validation that confirms config files exist before deployment

Before vs. After Obvyr

Before:

• Tests pass, production breaks
• Hours of post-incident debugging
• "It worked in CI" becomes a running joke
• Fear of deploying because environments don't match

After:

• Obvyr shows environment divergence before deployment
• Missing configuration file identified in CI vs. production comparison
• Add deployment validation step
• Deploy confidently knowing environments align

Measurable Impact:

• 🚨 Production incidents: 4 per month → 0 per month
• ⏱️ Debugging time: 12 hours/month → 0 hours
• 🚀 Deployment confidence: "Hope it works" → "Know it works"
• 📉 Rollback rate: 15% of deployments → 2% of deployments

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3. Test Value Assessment and Optimisation

The Problem

Scenario: Your CI pipeline takes 45 minutes to run. You have 3,247 tests. Coverage is 87%. But which tests actually matter? Which ones protect critical user journeys? Which ones are just testing framework behaviour?

What Actually Happens:

• Test suite grows indefinitely as developers add tests
• No one knows which tests provide value vs. noise
• CI gets slower and slower
• Team can't remove tests because they don't know what's safe to remove

The Traditional Approach:

1. Look at coverage metrics (tells you lines covered, not value provided)
2. Guess which tests are "important" based on file names
3. Hope you don't break anything when removing tests
4. Suffer through slow CI because optimization is too risky

Cost to Your Team:

• Velocity: 45-minute CI runs block deployments
• Cost: Thousands of dollars/month in CI compute for low-value tests
• Maintenance: Hours maintaining tests that don't protect anything
• Confidence: Can't optimise because you don't know what's safe to remove

The Obvyr Solution

Evidence-Based Test Value Analysis:

Obvyr analyses every test execution to reveal actual value vs. maintenance burden:

Test Suite Analysis (3,247 tests across 6 months):

High-Value Tests (847 tests - 26% of suite):
✅ Caught bugs: 234 actual failures over 6 months
⏱️ CI time: 12 minutes (27% of pipeline)
📊 Failure rate: 2.8% (indicates meaningful validation)
🎯 Coverage: Core user journeys, payment processing, authentication

Examples:
- test_payment_flow_end_to_end: Caught 47 bugs, 0.4% flaky
- test_authentication_with_mfa: Caught 23 bugs, 0% flaky
- test_order_processing_workflow: Caught 31 bugs, 1.2% flaky

Low-Value Tests (1,412 tests - 43% of suite):
❌ Caught bugs: 0 failures over 6 months
⏱️ CI time: 28 minutes (62% of pipeline)
📊 Failure rate: 0% (never caught anything)
🎯 Coverage: Getter/setter methods, framework behaviour, trivial logic

Examples:
- test_user_object_has_email_field: Never failed, tests framework
- test_config_loader_returns_dict: Never failed, tests library
- test_logger_writes_to_file: Never failed, tests third-party tool

Flaky Tests (412 tests - 13% of suite):
⚠️ Flaky rate: >5% inconsistent failures
⏱️ CI time: 5 minutes (11% of pipeline)
📊 Real bugs caught: 2 over 6 months
🎯 Noise generated: 847 false negative investigations

Recommendation:
✅ Keep: 847 high-value tests (26%)
🔄 Fix: 412 flaky tests (13%) - worth fixing
❌ Remove: 1,412 low-value tests (43%)
📝 Review: 576 medium-value tests (18%)

Optimised CI time: 12 minutes (down from 45 minutes)
Maintained effectiveness: Keep 100% of bug detection capability

Obvyr shows you:

1. Which tests catch bugs: Evidence-based value assessment, not assumptions
2. Which tests are noise: Flaky tests that generate false negatives
3. Which tests are waste: Never fail, never catch bugs, just slow down CI
4. Safe optimisation path: Remove 43% of tests with zero risk

Before vs. After Obvyr

Before:

• 3,247 tests, 45-minute CI, "afraid to touch anything"
• Can't optimise because you don't know what's safe to remove
• Paying for compute to run tests that provide zero value
• Coverage metrics say 87% but don't indicate test quality

After:

• 1,835 tests, 12-minute CI, same bug detection capability
• Evidence-based optimisation with zero risk
• 73% reduction in CI compute costs
• Coverage metrics paired with actual value data

Measurable Impact:

• ⏱️ CI time: 45 minutes → 12 minutes (73% reduction)
• 💰 CI costs: $3,200/month → $950/month (70% reduction)
• 🎯 Bug detection: Maintained 100% effectiveness
• 🚀 Deployment velocity: 8 deploys/week → 20 deploys/week

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4. AI-Era Quality Assurance at Scale

The Problem

Scenario: Your team adopts GitHub Copilot and Claude Code. Developer velocity increases 8x for code generation. But test quality? Still manual code review. Still hoping AI-generated tests are reliable. Still discovering AI assumed behaviour instead of validating it.

What Actually Happens:

• AI generates code 10x faster
• Developers manually review AI-generated tests at 1x speed
• Test quality becomes the bottleneck to AI velocity
• AI-generated code ships with AI-assumed quality
• Production incidents reveal AI misunderstood requirements

The Traditional Approach:

1. Manually review every AI-generated test
2. Hope AI understood the testing requirements
3. Run tests and assume passing means good quality
4. Discover AI testing gaps in production

Cost to Your Team:

• Velocity Gap: Code generation 10x faster, quality assurance still 1x
• Quality Risk: AI-generated tests assume behaviour instead of validating it
• Technical Debt: Accumulates faster than manual review can address
• Incidents: AI-code bugs that AI-generated tests didn't catch

The Obvyr Solution

Systematic AI-Generated Test Validation:

Obvyr analyses AI-generated test patterns to reveal quality issues that manual review misses:

AI Code Quality Analysis:

Feature: User authentication with OAuth (AI-generated)

AI-Generated Code Analysis (Week 1):
📝 Lines of code: 847 (AI-generated in 2 hours)
🧪 Tests added: 23 (AI-generated in 30 minutes)
✅ Initial CI: All tests pass

Obvyr Pattern Analysis (Week 2):
⚠️ Test execution pattern detected:

test_oauth_login_success (AI-generated):
- Pass rate: 100% in all environments
- Edge cases tested: 1 (happy path only)
- Error handling tested: 0
- Timeout scenarios: 0
- Network failure scenarios: 0

Vs. Human-generated comparison:
test_oauth_login (human-generated):
- Pass rate: 94% (catches real failures)
- Edge cases tested: 8
- Error handling tested: 5 scenarios
- Timeout scenarios: 3
- Network failure scenarios: 4

AI Test Quality Assessment:
❌ AI tests validate happy path only (100% pass = no edge cases)
❌ No failure scenario coverage (timeout, network, auth server down)
❌ No error handling validation
❌ Assumes OAuth provider always responds in < 1s

Obvyr Recommendation:
🔄 AI-generated tests need:
   - Timeout scenario tests (auth server slow response)
   - Network failure tests (auth server unreachable)
   - Error handling tests (invalid token, expired token)
   - Edge case tests (concurrent logins, token refresh)

Obvyr reveals:

1. AI test quality gaps: AI tests happy path, misses failure scenarios
2. Pattern comparison: Human tests catch failures, AI tests never fail
3. Specific improvements needed: Exact scenarios AI missed
4. Quality validation: Evidence-based assessment of AI-generated test quality

Before vs. After Obvyr

Before:

• AI generates code 10x faster
• Manual code review can't keep up
• AI-generated tests "pass" so they ship
• Production reveals AI missed critical scenarios

After:

• AI generates code 10x faster
• Obvyr analyses test patterns at AI speed
• Identifies AI test quality gaps before deployment
• Production confidence maintained at AI velocity

Measurable Impact:

• 🚀 AI velocity: Maintained 8x code generation speed
• 🛡️ Quality assurance: Automated test validation at AI speed
• 📉 AI-code incidents: 12 per month → 1 per month
• ⏱️ Manual review time: 40 hours/week → 8 hours/week

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5. Compliance and Audit Documentation Burden

The Problem

Scenario: Your organisation operates in a regulated industry (financial services, healthcare, government contracting). Quarterly, your compliance team requests evidence of testing practices for regulatory audits. An enterprise customer security review demands proof of systematic quality assurance before signing a $2M contract.

What Actually Happens:

• Engineering team spends 40 hours compiling testing documentation manually
• Screenshots of CI pipelines, test results, coverage reports scattered across tools
• No systematic proof of who ran which tests, when, and with what results
• Historical evidence is incomplete or non-existent
• Audit preparation becomes emergency scramble every quarter

The Traditional Approach:

1. Manually document test execution practices in spreadsheets
2. Screenshot CI results and store in shared drives
3. Hope auditors accept incomplete evidence
4. Divert engineers from development to compile documentation
5. Risk failed audits or delayed customer deals due to insufficient evidence

Cost to Your Team:

• Time: 40-80 hours per audit for documentation compilation
• Opportunity cost: Lost development time during audit preparation
• Risk: Failed audits leading to regulatory penalties
• Revenue: Delayed enterprise deals due to insufficient security documentation
• Compliance: Manual processes are error-prone and incomplete

The Obvyr Solution

Automated Compliance Evidence Collection:

Obvyr automatically captures comprehensive audit trail data as a by-product of normal development:

Complete Test Execution Records:

Audit Period: Q4 2024 (Oct 1 - Dec 31)

Total test executions: 47,823
Environments covered: local (23,456), CI (24,367)
Unique tests run: 3,247
Developers: 45
CI runners: 12

Evidence automatically collected:
✅ Who: User attribution for every test execution
✅ What: Full command and test framework details
✅ When: Precise timestamps for all executions
✅ Where: Environment context (local, CI, staging)
✅ Result: Pass/fail with complete output
✅ Coverage: Historical test effectiveness data

Audit-Ready Reports:

Instead of 40 hours of manual compilation, Obvyr provides:

Security Test Execution Proof:

Security Test Suite Analysis (Q4 2024):

test_authentication_mfa:
- Total executions: 1,247
- Pass rate: 99.8% (3 legitimate failures, all resolved)
- Environments: local (847), CI (400)
- Frequency: Executed before every deployment
- Evidence: Complete execution history with timestamps

test_authorization_rbac:
- Total executions: 1,156
- Pass rate: 100%
- Environments: local (756), CI (400)
- Frequency: Executed before every deployment
- Evidence: Complete execution history with timestamps

Compliance Statement:
✅ Security tests executed systematically
✅ 100% pre-deployment validation
✅ Complete audit trail available
✅ Environmental parity verified

Change Control Evidence:

Deployment Validation Proof:

Production Deployment #247 (Dec 15, 2024):
Pre-deployment test execution:
- CI Run ID: ci-247
- Tests executed: 3,247
- Pass rate: 100%
- Duration: 12.4 minutes
- Timestamp: 2024-12-15 14:23:47 UTC
- Executed by: ci-system
- Environment: production-staging

Evidence: Complete test output and execution context available

Obvyr shows you:

1. Complete audit trail: Every test execution automatically recorded
2. Systematic proof: Evidence of consistent testing practices
3. Historical verification: Years of testing history available on demand
4. Zero documentation overhead: Evidence collected automatically
5. Audit-ready reports: Generate compliance documentation in minutes

Before vs. After Obvyr

Before:

• 40 hours per quarter compiling manual documentation
• Incomplete evidence, missing historical data
• Screenshots and spreadsheets scattered across systems
• Risk of failed audits or delayed customer deals

After:

• 2 hours per quarter generating automated reports from Obvyr
• Complete, immutable audit trail with historical depth
• Comprehensive evidence in centralized platform
• Confident compliance with automated documentation

Measurable Impact:

• ⏱️ Audit preparation time: 40 hours → 2 hours (95% reduction)
• 📋 Evidence completeness: Incomplete → Comprehensive
• 💰 Risk mitigation: Avoid failed audits and delayed deals
• 🎯 Engineering focus: Develop features instead of compiling documentation

Real-World Compliance Scenarios

Scenario 1: Enterprise Customer Security Review

Customer requirement: "Prove that security tests execute before every production deployment"

Without Obvyr:

• Manually compile CI logs from past 6 months
• Create spreadsheet of deployment dates and test results
• Hope evidence is sufficient
• Time: 20 hours of engineering effort

With Obvyr:

• Generate report: "All deployments with pre-deployment test execution"
• Export comprehensive evidence with timestamps and results
• Complete, verifiable proof of systematic testing
• Time: 15 minutes

Scenario 2: Regulatory Audit

Auditor question: "How do you ensure test quality and prevent environmental drift?"

Without Obvyr:

• Describe manual processes
• Provide sample CI screenshots
• Hope auditor accepts verbal assurance
• Risk: Insufficient evidence

With Obvyr:

• Show comprehensive environment comparison data
• Prove systematic flaky test resolution
• Demonstrate historical test effectiveness
• Evidence: Complete, verifiable, systematic

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Summary: From Pain Points to Solved Challenges

Problem	Traditional Approach	Obvyr Solution	Measurable Impact
Flaky Tests	Manual tracking, hope	Pattern detection, root cause analysis	8 hrs/week → 0 hrs debugging
Environment Drift	Try to keep in sync	Systematic divergence detection	4 incidents/month → 0
Test Value	Guess and hope	Evidence-based optimisation	45 min CI → 12 min
AI Quality	Manual review bottleneck	Automated pattern validation	12 incidents/month → 1
Compliance	Manual documentation	Automated audit trail	40 hrs/audit → 2 hrs

Next Steps

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