In today‘s fast-paced software landscape, testing velocity is imperative. Organizations that can deliver high-quality digital experiences quickly have a competitive advantage. Robust test infrastructure acts as the foundation that makes fast yet reliable testing possible.
But what exactly constitutes good test infrastructure? And how can teams avoid common pitfalls that slow testing and, in turn, software delivery? This comprehensive guide will unpack everything you need to know.
Defining Test Infrastructure
Test infrastructure refers to the environments, tools, processes and standards that enable and support software testing activities. At a high level, it consists of:
- Test environments – The hardware and software configurations where tests are executed
- Test data – The datasets used to feed into test cases
- Testing tools – Software that supports test execution, reporting and analysis
- Processes – Standards and workflows for test planning, development, execution and maintenance
Robust infrastructure directly impacts testing velocity. According to recent surveys, inadequate test environments and data issues cause around 40% of all testing delays.
The Problem with Manual Testing Infrastructure
Many teams still rely heavily on manual software testing processes. But as development velocities increase, manual testing becomes a bottleneck.
Common Pain Points
- Repetitive, mundane testing leads to fatigue and human errors
- Difficult to scale testing efforts for large codebases
- Reproducing defects is time-consuming
- Harder to achieve comprehensive test coverage
- Creates knowledge silos instead of collaboration
Let‘s analyze each of these issues and their impact:
1. Repetitive Tasks Cause Fatigue and Errors
Manually executing test cases is mentally exhausting. Having to log in and repeat the same steps leads to boredom. After a while, testers miss tiny defects or make mistakes capturing results.
Studies show the more repetitive a task, the more severe the impact of fatigue. In extreme cases, productivity may decrease by 30-40% in a single day.
2. Difficult to Scale for Large Codebases
As an application‘s codebase grows from thousands to millions of lines, the number of tests needed also multiplies. Manually covering all the test cases requires an exponentially growing tester army.
Many organizations cannot realistically hire so rapidly. And even with more testers, consistency becomes harder to manage across large distributed teams.
3. Reproducing Defects is Time-Consuming
When a bug surfaces in testing, reproducing it manually means re-executing possibly hundreds of steps. If the defect is intermittent or tied to specific data states, reproducing it can be even trickier.
This makes debugging extremely tedious. Testers have to rely purely on logs and screenshots to communicate defects upstream since they may not surface consistently.
4. Harder to Achieve High Test Coverage
To deliver high quality, teams should test software comprehensively across all flows, functions, UI elements etc. With manual testing, executing such an exhaustive set of test cases is unrealistic for multiple reasons:
- Time needed per test case is higher
- Repeating the same test flows across browsers, devices and environments multiplies effort needed
- Does not scale cost-effectively for large codebases
Most teams thus end up with major test gaps that negatively impact software quality.
5. Creates Knowledge Silos Instead of Collaboration
When testing is a purely manual, downstream activity, testers work in isolation without much interaction with developers or product teams. This creates knowledge silos – testers only understand the app from an external point of view but have less context about code quality or product priorities.
Ultimately, siloed mindsets reduce overall team productivity and software quality.
The Solution – Incorporate Automation
There is no way around it – teams need to upgrade manual testing processes with intelligent automation. Many mundane, repetitive aspects of testing are prime candidates for automation:
- Smoke tests
- Sanity checks
- Regression test suites
- Cross-browser compatibility checks
- Data-driven tests
- API testing flows
Automating these testing tasks provides numerous benefits:
- Frees up human tester time from rote activities
- Improves testing accuracy and coverage
- Enables parallel test execution across multiple environments
- Provides armor for core app functions through regression suites
- Opens up possibilites for exploratory manual testing
According to recent research by Test Automation Intelligence magazine, teams that leverage automation testing deliver software 400% faster than teams relying solely on manual testing processes.
Additionally, automated tests serve as living system documentation that adds to collective team knowledge over time. Well-designed automation suites force collaboration across roles right from the planning stages.
Is Automated Testing Infrastructure Slowing You Down?
Given the clear benefits, most modern engineering teams adopt some level of test automation. But less mature test infrastructure can still pose bottlenecks that slow test cycles.
Common Bottlenecks
- Test environments provisioning issues
- Test data not reset/managed properly
- Insufficient test coverage
- Flaky tests
- Poor analytics/debugging capabilities
Let‘s analyze the impact of each:
1. Test Environment Bottlenecks
If test environments are not available in adequate numbers or take too long to spin up, test cycles get delayed. Testers may also encounter unexpected failures if test beds are not correctly configured.
2. Test Data Issues
Realistic test data is essential for function tests, especially for applications dealing with sensitive data like finance or healthcare. Bad test data leads to false test failures that derail CI/CD pipelines. Resetting test data manually between runs creates further delays.
3. Gaps in Test Coverage
Often teams automate only a subset of test types such as smoke or regression tests. Critical flows like negative path testing, accessibility testing, or visual UI testing have major gaps. Such gaps in test coverage allow defects to pass through the pipeline into production.
4. Flaky Test Suites
Flaky tests are test cases that pass or fail intermittently, confusing CI/CD pipelines. According to a survey by Gremlin, 92% of developers face problems due to test flakiness. Triaging the root cause requires considerable effort.
5. Lack of Debugging Tools
When tests fail unexpectedly, debugging capabilities directly impact mean time to resolution. Insufficient logs, videos, screenshots or metrics make it exponentially harder for testers to pinpoint the exact failure points.
Key Takeaway: Sub-par test infrastructure with bottlenecks negatively impacts an organization‘s ability to release quality software rapidly. Next, let‘s explore solutions to optimize automated testing environments.
Optimizing Automated Testing Infrastructure
Here are 5 best practices to eliminate bottlenecks and supercharge test automation velocity:
1. Invest in Real Devices Cloud for Test Environments
Configuring physical test labs with multiple device-OS combinations is complex and expensive to scale/maintain. A real device cloud provides on-demand access to thousands of mobile devices, browsers and desktop OS versions – both latest and legacy.
With ready test beds available 24/7, testing cycles are no longer gated on physical resource availability. Parallel test execution also maximizes test concurrency.
For example, BrowserStack App Live provides instant access to 2,000+ real mobile devices and browsers. Testers can run automated or manual tests concurrently without managing any physical infrastructure.
2. Ensure Test Data Readiness Across Environments
CI/CD pipelines must incorporate test data management to consistently feed test suites:
- **Masking** for sensitive data fields
- **Synthetic data generation** where needed
- **Backup and restore mechanisms** to reload databases
- Tools to **reset test states** before test runs
Without test data readiness, pipelines risk flaky failures, poor test coverage, or security gaps.
3. Expand Test Coverage Through Automated API Testing
While UI test automation is table-stakes, teams should also incorporate API testing into pipelines. API tests act as a fast safety net for core app functions and business logic:
- Execute earlier than slower UI tests
- Help expand test coverage to validate input payloads, error flows etc.
- Made reusable across multiple consumer interfaces – web, mobile etc
Popular open-source tools like Postman, REST Assured and Karate DSL help simplify API test creation.
4. Build Reliable Test Suites
Here are 4 proven techniques to minimize test flakiness:
- Add checksum validations for test environment readiness
- Insert configurable wait timers between test steps
- Parameterize tests with runtime variables to isolate test data
- Implement retry logic mechanisms for known flaky points
Continuous analysis of historical test runs can further pinpoint infrastructure weaknesses causing intermittent failures.
5. Enable Rapid Test Failure Triage
Tools like BrowserStack provide extensive debugging capabilities for web and mobile apps:
- **Videos** to visually diagnose test runs
- **Network logs** to analyze API calls
- **Smart screenshots** to capture DOM states automatically on failure
- **Device logs** from mobile devices or browser console
Integrations with developer tools like Jenkins, JIRA etc also simplify defects reporting/tracking.
Such capabilities help testers drill down to root causes in minutes instead of days.
The Path Forward
In today‘s highly competitive landscape, organizations must navigate the delicate balance between speed and quality.
Robust test infrastructure acts as the bedrock that enables teams to deliver flawless digital experiences rapidly through techniques like CI/CD, shift-left testing etc.
However, sub-par infrastructure with bottlenecks can still drag down testing velocity. By optimizing test environments, data, tools and processes – engineering teams can unlock testing throughput to meet modern demands.
