Testing is an integral part of any software methodology. But traditional testing approaches don‘t always translate well to fast-paced, iterative agile environments. So how is testing handled on agile teams? Enter the agile testing life cycle.
In this 2800+ word beginner’s guide, we’ll cover:
- Key differences from waterfall testing
- The 5 main phases of agile testing
- Roles and best practices
- Expert tips for planning, automation and reporting
- Overcoming common challenges
Whether you’re new to agile or looking to optimize your team’s current test practices, this guide will give you a solid understanding of the agile testing life cycle.
What is the Agile Testing Life Cycle?
The agile testing life cycle is the process of testing in adaptive, iterative agile environments. It aligns with the principles of agile development – delivering working software frequently, embracing changing requirements, maintaining constant collaboration between business and development teams.
But while agile teams focus on rapid iterations, testing still plays a critical role. Well-designed tests and test automation provide fast feedback loops so teams can validate new features, catch defects early, and ensure each build is production-ready.
Agile Testing Aligns with Overall Team Practices
Testing activities align with agile philosophies:
- Incremental – Testing is executed in small chunks against smaller user story implementations
- Iterative – Test cases are executed repeatedly against new iterations of completed user stories
- Communicative – Tests elaborate requirements and defects fuel conversations
- Demonstrable – Working code is demonstrated and validated each iteration
- Emergent – Tests surface naturally from analyzing requirements and code
Rather than siloed test phases after development work, agile testing is woven throughout each sprint:
- The team starts by defining test scenarios and acceptance criteria up front as part of sprint planning
- Developers use test-driven development (TDD) as they write code, with test automation expertise integrated right into the team
- Every code check-in triggers automated unit and integration test workflows
- UI tests validate new features against acceptance criteria
- Automated regression tests prevent existing functionality from breaking
By involving testing in every part of development, agile teams prevent defects, build quality in, and release better software more frequently.
Key Differences from Traditional Waterfall Testing
While the waterfall software methodology proceeds sequentially through long phases, agile testing takes an iterative approach. Some key differences include:
| Waterfall Testing | Agile Testing |
|---|---|
| Executed only after development work finishes | Woven throughout development in each sprint |
| All requirements fully detailed upfront | Testing against Iteration goals |
| Big bang integration | Continuous integration |
| Lengthy documents | Concise living test cases |
| Manual test execution | Automated check regression & repetitive flows |
| Lagging defect detection | Rapid feedback to Developers |
The iterative approach helps agile teams release higher quality, better tested software more frequently.
Industry data indicates a vast improvement in quality with agile and DevOps practices. According to recent research:
- 80% of teams leveraging agile report improved software quality
- 60% faster time-to-market is achieved by continuous delivery teams
- 5x more frequent releases; 440x faster lead time in high performing organizations
Agile adoption drives immense improvements in software quality and delivery speed
The 5 Main Phases of Agile Testing
While agile testing is integrated throughout delivery, most teams organize test activities into these 5 high-level phases:
1. Sprint Planning
Before development work begins, the team comes together to decide what user stories or features they’ll deliver in the upcoming sprint.
As part of planning, the team defines “Ready” criteria including:
- Detailed acceptance criteria – conditions to satisfy the story
- Test scenarios & test cases – examples to validate required behavior
- UX designs finalized
- Environments, test data available
Sample User Story
As a user I want to update my password so that I can enhance my account security
Acceptance Criteria:
- Password meets length, character complexity rules
- User receives confirmation notice after change
- User remains logged in after change
- New password allows user to access account
- Invalid passwords show proper error message
Test Cases:
| # | Steps | Expected Result |
|---|---|---|
| 1 | Enter valid existing password & new password meeting rules | New password saves & user stays logged in |
| 2 | Enter invalid existing password | Error message displayed |
| 3 | Enter valid current but invalid new password | Validation error message about password rules displayed |
Aligning on clear definitions of done ensures all aspects covered for feature delivery and sets up traceability for subsequent testing.
2. Continuous Testing
Next developers begin building the features defined during sprint planning. To validate code quality, teams rely heavily on continuous testing practices including:
Test Driven Development (TDD)
- Developers write unit tests validating small units of code before writing production code
- Fast red-green-refactor loop builds comprehensive validation
Unit Testing
- Systematically tests code modules in isolation
- Validates proper logic and outputs
Integration Testing
- Tests new code interacts properly with existing system
- Catches gaps not identified writing separate unit tests
Regression Testing
- Existing test suites evaluating critical or complex flows automatically executed with code changes to catch any regressions
- Keeps technical debt and defects from accumulating
Automating these developer testing practices provides rapid feedback on daily work.
Recommended test types for continuous integration:
| Test Type | Execution | Maintenance |
|---|---|---|
| Unit Testing | Automated | Developer responsibility |
| Integration & Regression Testing | Automated | Shared team responsibility |
| UI Testing | Manual or Automated | Automation Engineer(s) implement & execute |
3. UAT and Sprint Demo
At the close of each sprint, the team demonstrates completed functionality.
Stakeholders including product managers and end users systematically test scenarios and validate against defined acceptance criteria. Exploratory testing is also valuable for users to see how the flow feels end-to-end.
Bugs identified are either fixed immediately or logged as defects ranked by priority to address in a future iteration based on priority.
4. Release Testing
Prior to releasing to production, agile teams formally test features from multiple iterations at once.
A sample high-level testing strategy
Common focus areas may include:
- User workflows spanning multiple features
- End-to-end business process flows
- Integration points with external systems
- Security, performance & compliance checks
- Disaster recovery validation
Dedicated release testing sprints provide a final safety net checking for issues that may not surface testing single user stories.
5. Production Monitoring
Mature teams know testing doesn’t end when code moves to production. Instead they actively monitor:
- Application health metrics (response time, traffic, uptime)
- User behavior patterns (funnels, adoption metrics)
- Production logs (errors, warnings, exceptions)
Proactive production monitoring allows the team to experiment faster while ensuring excellent live site quality.
Agile Testing Roles & Responsibilities
While everyone contributes to agile delivery, test activities involve core participation across:
Product Manager – Works with stakeholders and end users to define stories and acceptance criteria for the team. Validates implemented functionality against criteria.
Quality Assurance – Analyze features under test, identify test scenarios/cases, executes test passes. Reports on test coverage and defects.
Test Automation Engineers – Create and maintain test automation suites at all test levels. Implement frameworks enabling continuous testing. Provide automation expertise and mentoring.
Developers – Active participants in all testing activities including unit test creation, test execution, analyzing defects uncovered by testing, and fixing issues. Follow TDD practices.
Business Analysts – Help define acceptance criteria. Support data and usage analysis for test case creation. Evaluate work delivered against success metrics.
DevOps Engineers – Provide environments for testing use. Automate and monitor release deployments. Support production monitoring.
Involving all team perspectives improves software quality while enabling faster delivery speed.
Best Practices for Agile Testing Success
While agile testing comes with plenty of benefits, it’s not without challenges. By adopting some of these industry proven practices, teams can further optimize their agile test processes:
Invest in Test Environments
Stable test environments are essential, yet often neglected on agile teams. Virtualization solutions allow replicating production environment configurations needed to support test parallelization. Identify infrastructure roadblocks upfront.
Start Testing Early
Don’t wait for development work to finish before involving testing. Collaborate up front to build test automation frameworks even before feature code complete. Enable test execution as soon as code checkins begin.
Right-Tool the Job
Choose test tools strategically aligned to your technology stack and testing needs rather than adopting every shiny new tool. Evaluate open source and commercial solutions for UI, load, integration and unit test automation capabilities.
Standardize Frameworks
Reuse approved test templates, utilities, code standards and automated frameworks across your delivery teams rather than reinventing and maintaining one-off solutions.
Prioritize Test Pyramids
Rather than only UI tests, use test automation pyramids with majority automated unit tests at the lowest level, less complex integration tests, minimal UI tests at the top. Aligned with risk patterns and maintenance costs.
Inspect & Adapt
Continuously improve upon your test processes each sprint, learning lessons from past efforts. Find the agile testing rhythm fitting your team’s unique context.
Overcoming Agile Testing Challenges
While agile testing practices lead to tremendous quality and delivery improvements long-term, teams may still run into common pain points including:
Not Budgeting Enough Time for Testing
Packing too much scope work into sprints leaves little room for testing activities. Have leadership enforce reasonable workload expectations to enable test creation & execution.
Lack of Effective Test Automation Strategies
Starting automation without assessing test scenarios, proper frameworks, or maintenance costs slows teams down. Take time to establish automation game plans.
Low Test Coverage
Aim beyond executing happy path tests only. Analyze risk patterns and build test data sets to handle various use combinations. Expand test scope incrementally each sprint.
Environment Instability
Shared unstable test environments create false test failures and slow down teams. Implement virtualization solutions and resource controls to support test reliability and parallelization.
Information Silos
Details on the latest requirements and past defects slip through the cracks between test cycles. Maintain living test case documentation accessible across the team.
Technical Debt Accumulation
Legacy automation scripts and frameworks require significant rewrite to keep working driving maintenance costs up over time. Schedule periodic test refactoring.
Expert Tips: Elevating Agile Testing with AI
Over my last 10+ years working as an AI practitioner and test automation architect, I’ve witnessed incredible advances in leveraging artificial intelligence, machine learning and data analytics to accelerate test practices. Here are a few keys way AI is upgrading agile testing:
Smart Test Case Recommendations
AI algorithms can automatically analyze user stories then provide a prioritized list of recommended test scenarios/cases to review and execute each sprint based on risk analysis. Reduces planning effort significantly.
No-Code Test Creation
Testers spend copious hours scripting automation suites which quickly fall out of sync with the latest UI. AI-driven no-code approaches allow dragging-and-dropping test steps and assertions without needing to worry about maintaining underlying script complexities.
Self-Healing Test Assets
Realizing tests fail because your test environment data sets shifted and not actual application bugs sucks. AI solutions can auto-detect tests looking for non-existent data values, automatically mapping to available data to self-correct scripts.
Automated Test Reporting
Machine learning takes raw test execution logs as input then structures interactive reports detailing scope executed, test status breakdown, historical pass rates and beyond to replace manual reporting needs.
Continuous Test Optimization
Sophisticated analytics measure test suite performance then prescribe which test cases aren’t pulling their weight and should be removed or which implicit gaps in coverage remain constantly evaluating scope efficiency.
While agile delivery continues to evolve, instrumenting artificial intelligence unlocks game-changing testing productivity, efficiency and innovation gains.
Key Takeaways
Here are the core principles to take away when adopting an agile test strategy:
- Shift testing left – Begin validating functionality early and often with test first approaches
- Automate relentlessly – Continuous testing ispowered by automation
- Validate frequently – Demo working software against success metrics every iteration
- Fail fast – Learn from finding issues early when less expensive to fix
- Continuously improve – Constantly inspect and adapt your agile testing process
- Leverage AI – Innovate testing with the latest technology breakthroughs
The key is involving the entire cross-functional team to build quality into agile delivery rhythms, not just testers working in silos. Adopt these agile testing life cycle leading practices as a starting point then continually experiment and tailor an approach fitting your unique needs.
By taking an iterative mindset to evolving test processes, teams achieve the outcomes they likely set out for when embarking on agile transformations – delighting users with better software delivered faster through enhanced quality built-in right from the start.
I hope this comprehensive beginner’s guide provides a helpful understanding of agile testing concepts while equipping you with expert recommendations to optimize your team’s quality and delivery practices. What resonated most or surprised you? What aspects are you looking to adopt or improve on your teams? I welcome any additional questions in the comments!
