If you are leading QA transformation initiatives, test automation is likely high on your priority list. The promised outcomes seem glittering – faster testing, improved efficiency, higher quality. However, the road to test automation success also has its fair share of potholes.
Based on my 15+ years in spearheading test automation programs, I break down the top 10 problem areas organizations face:
1. Creating Maintainable Automated Tests
Like any software code, effectiveness of test automation scripts erode over time as the System Under Test changes. Tests start failing when changes take effect. Without designing for maintainability upfront, test code becomes extremely painful to update repeatedly.
72% of organizations in a recent QA transformation survey flagged this as their top test automation challenge.
How You Can Overcome This
- Build modular, reusable libraries – Create globally reused functions for generic activities like login, clicks, field inputs etc.
- Incorporate abstraction – Hide internal complexities of tests via interfaces and higher level bindings.
- Drive through data – Parameterization and data-drivingexternals test data from scripts.
2. Managing Flaky Test Automation
Flakiness creeps in when tests pass/fail intermittently without any code changes. This erodes trust in automation and causes flaky test investigation overhead.
Top factors per QA leaders contributing to flakiness:
| Root Cause | % of Occurence |
|---|---|
| Test environment issues | 33% |
| Asynchronous wait problems | 27% |
| Test data problems | 20% |
| Bad test design | 15% |
| Other reasons | 5% |
Prescription to Avoid Flakes
- Isolate test data for each test class to prevent contamination
- Standardize waits with custom wrappers to manage async calls
- Parameterize tests with data providers to exercise different contexts
- Apply IDE plugins like Flaky Test Detector to surface problems
3. Achieving Good Test Coverage
With complex applications, achieving adequate test coverage through automation gets tougher. Factor in 95% of investments focus on front-end and UI layer testing, per a recent report. This leaves gaps in API, integration, security, performance test coverage.
Smart Ways to Expand Coverage
Utilize risk-based approaches to early identify high priority gaps and address those through automation. Supplement with exploratory testing on lower risk areas.
Get clarity on coverage metrics based on application layers, risk and planned test types. Monitor these coverage dashboards for continuous improvement.
Allocate dedicated time for test analysis as part of automation sprints for engineers to find missed scenarios.
4. Setting Up Realistic Test Environments
To detect defects effectively, test automation requires access to the right test environments, systems and test data. With increasing parallel execution, managing this infrastructure in a large automation pipeline brings scale & consistency headaches.
A big challenge organizations report is maintaining test environments across multiple QA stages from early dev testing to pre-prod UATs. Maintaining integrated test data across connected test systems is also hugely problematic.
Solutions For Seamless Environment Management
Standardize environment naming & access mechanisms through central provisioning systems.
Shift left on environment readiness through early dev involvement rather than just before testing starts.
Implement test virtualization techniques to simulate dependent systems/test data.
Consider test environment PaaS offerings for on-demand access without infrastructure overheads.
5. Achieving Deep Technical Expertise
Test automation demands specialized expertise around languages like Java/C#, JavaScript/TypeScript, scripting frameworks like Selenium/Appium/Cypress, DevOps toolchain familiarity etc.
Building this capability mix requires concerted efforts as indicated in below capability maturity spectrum:
Assessing your team‘s capabilities and putting bridges in place to cross skill gaps is vital for automation success.
Getting Your Team Up the Capability Curve
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Build a competency matrix breaking down must-have vs good-to have tech skills
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Identify training needs and conduct classroom/online training programs to elevate competencies
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Actively monitor and mentor engineers through code reviews and demos to reinforce skills pickup
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Create innovation days to experiment with new age tools like ML based test generation
6. Integrating Open Source and Commercial Tools
While open source tools promote customization, Commercial tools provide enhanced capabilities around operational aspects like reporting, traceability etc. Below comparative analysis helps weigh pros and cons:
![Open Source vs Commercial Tools] (/test-auto-tool-compare.png)
Blending open source for test design agility with commercial tools for rigour and governance is perfect recipe.
7. Walking the Open Source vs Commercial Tightrope
Choosing technology is always hardest part. Open source promises customization while commercial provides support and roadmaps. Below comparitive view helps identify synergies:
![Open Source vs Commercial Tools] (/test-auto-tool-compare-2.png)
Blending both models based on capability priorities is key rather than dogmatic choices.
8. Managing Total Cost of Ownership
Do not underestimate downstream costs with test automation around maintaining scripts, provisioning and accessing test environments, tools, data etc.
A typical cost breakup:
- Tool acquisition/licensing – 20%
- Script development & maintenance – 50%
- Test environment management – 15%
- Miscellaneous costs – 15%
Keeping Automation ROI in Green
Audit automation costs yearly accounting for often hidden environment management and test data expenses
Reuse test components aggressively through modular script design
Utilize cloud based device access solutions like BrowserStack rather than managing device labs
9. Update Processes for Automation
Test automation calls for updated QA processes around releases, environments, tools, metrics etc. Lack of aligned processes leads to breakdowns like:
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Environments not ready for automation testing
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Scripts failing due to release delays
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Caps on tool licenses throttling automation
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Automation metrics not covering business risks
Align Processes to Automation
Update release management processes to ensure build quality upstream
Improve environment management maturity through better provisioning and access
Evolve test planning to allocate time for designing automation scripts
Expand metrics to cover automation coverage, effectiveness etc
10. Manage Organizational Change
For manual testers transforming to automation engineers or developers cooperating more with QA, organizational change resistance surfaces time and again. Lack of motivation, technical challenges, inertia stall initiatives.
Smooth the Automation Transition
Incentivize engineering teams on automation metrics through gamification programs
Invest adequately in training through various self-service and trainer led systems
Showcase automation success stories to reinforce benefits realized
Celebrate important milestones on automation usage to keep momentum
The road to domination with test automation has its fair share of challenges – technical skills shortages, tooling decisions, flaky tests, hidden costs etc. Consciously addressing these top 10 problem areas will accelerate your automation flywheel and deliver better quality software faster.
