As an application testing expert with over a decade of experience, I‘ve seen firsthand the software delivery benefits realized by development teams who embrace continuous testing across their toolchains. However, implementing comprehensive test automation to shift quality left isn‘t always straightforward.
In this detailed guide designed specifically for you, I‘ll share proven strategies to help your team:
- Instill a culture of quality ownership across all roles
- Scale test automation for maximum coverage
- Choose and configure test tools purpose-built for DevOps
- Simulate real-world conditions to uncover defects early
- Analyze test data to optimize validation criteria
- Overcome common testing bottlenecks
These actionable best practices stem from successes (and pitfalls) I’ve experienced consulting many software teams on expediting release cycles through continuous testing over my career.
Let‘s start by examining what continuous testing entails.
What is Continuous Testing? An Overview
I define continuous testing as the practice of executing automated checks on source code changes frequently and early within the software delivery lifecycle. It expands on traditional testing approaches in several key ways:
- Testing is shifted left to validate work products earlier at code commit and build/integration process stages
- Validation criteria expands beyond functional testing to include other types like performance, security, and localization
- Test execution is automatic whenever new changes are introduced
- Results and coverage data are shared across teams to instill shared quality goals
- Simulating real user conditions on actual devices and browsers provides order of magnitude greater test accuracy
The overarching theme is providing rapid feedback on code changes so that teams can identify and remediate issues before they reach customers. Industry research shows that continuous testing reduces production defects by as much as 50%, enabling reliable, rapid releases.
So what does a comprehensive continuous testing toolchain actually include? Here are key components:
Component #1: Automated Unit Tests
Developers design and run unit tests on individual modules using frameworks like JUnit to validate input/output logic and prevent unknowingly introducing defects within code. Covering code complexity with automated unit testing is proven to reduce production issues down the line. Industry best practice dictates >= 70% unit test coverage for new code changes.
Component #2: Automated Integration + Regression Testing
Continuous integration servers like Jenkins run automated test suites on integrated code multiple times per day to verify module interactions and confirm existing feature functionality per regression testing standards. These validate code merges from all developers adhere to requirements without side effects.
Component #3: Manual Exploratory Testing
While test automation should be maximized, manual real-world testing remains invaluable for validating assumptions of how software will be used by replicating user environments and motives. Exploratory testing on actual devices frequently uncovers edge cases automation scripts cannot anticipate.
Component #4: Performance + Load Testing
Specialized testing evaluates system behaviors under expected real-world load including identifying bottlenecks, establishing baseline metrics, assessing scalability, and optimizing configurations for production readiness. This focuses specifically on non-functional attributes.
Now that we‘ve defined continuous testing, let‘s examine why it‘s so critical for DevOps-centered teams.
Importance of Continuous Testing for DevOps
Delivering high quality software fast requires optimizing quality control measures across development toolchains. Continuous testing apartheid provides the rapid feedback loops needed for agile teams to meet compressed delivery timelines while preventing defects from escaping into production.
Look at these statistics that showcase the positive outcomes realized by teams leveraging continuous testing:
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Organizations who test earlier and more often release products 125% faster than competitors as per recent reports by McKinsey and Capgemini
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Teams running over 1000 automated checks daily see 33% fewer high severity production issues than those running less than 500 (data via Perfecto 2021 State of DevOps)
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Banking apps utilizing continuous testing deploy 2x more frequently with lead times under an hour (per AWS 2021 research study)
The data confirms implementing preventative quality measures through automated testing directly fuels release velocity increases and reliability gains.
From firsthand experience, here are the real-world advantages teams stand to gain:
Faster Time to Market
By integrating testing earlier into commit stage workflows, defects surface instantly so developers can rectify issues without waiting days or weeks for downstream QA validation feedback. This prevents accumulative work pileups.
Reduced Costs
Shifting testing left significantly reduces the costs that come with identifying and fixing issues further down the delivery lifecycle. Industry data shows fixing bugs post-production costs 15x more on average.
Improved Customer Experiences
Higher quality experiences increase satisfaction over time. In an age where users will quickly abandon apps and services after one bad experience, preventing faults through testing protects retention and loyalty.
Greater Release Velocity + Scale
Comprehensive test automation allows organizations to seamlessly scale delivery volumes across expanding team sizes without compromising on quality.
The data shows that when it comes to achieving DevOps success, continuous testing is no longer optional. It‘s essential. Now let‘s explore proven frameworks for implementation.
Best Practices for Implementing Continuous Testing
Transitioning an entire organization to continuous testing can seem daunting. Through hundreds of consulting engagements, my team has developed this step-by-step framework that development teams can follow:
Step 1: Foster Cross-Team Collaboration
Siloed workstreams result in disjointed quality measures. Establish routine touchpoints between developers, test automation engineers, product analysts, and other stakeholders to strategize on preventative validation options. Clarify responsibilities around owning testing needs across toolchain phases.
Step 2: Increase Test Automation Starting Day 1
Work closely with engineers from the start of new feature builds to create automated unit test suites that can run on code check-ins to verify correctness early. Set minimum coverage thresholds teams must meet before downstream advancement.
Step 3: Validate Real-World Conditions Early
While unit tests focuses on logic flows, leveraging real mobile devices and browsers for integration testing uncovers faults triggered by fragmented production environments missed in simulated QA sandboxes. Prioritize automation for customer-impacting flows.
Step 4: Shift Security Testing Left Too
Expand beyond functional validation to assess security vulnerabilities during build phases. Identify authorization gaps, unsafe inputs, SQL injections, and other exposures through static + dynamic application security testing integrated into commit workflows.
Step 5: Embed Testing into CI/CD Pipelines
Streamline execution through native integrations with popular CI/CD tools to trigger relevant test suites automatically on code changes rather than relying on manual kickoffs. Customize orchestration as needed.
Step 6: Analyze Results to Optimize Further
Collect execution data like pass % rates, test durations, failures by type, flaky tests, etc to pinpoint problematic areas needing expanded test cases or framework improvements. Analytics fuel optimization.
While this provides a starting point, let‘s dig deeper into recommendations for the two most critical continuous testing pillars:
- Maximizing test automation
- Simulating real-world conditions
Scaling Test Automation Strategies
The key to efficient continuous testing is relying on consistent automated validation versus repetitive manual testing. Here are proven scale test automation best practices:
Leverage Existing Test Assets
Reuse any manual test cases or UI automation framework foundation available to quickly build initial test suites for pipeline integration and establish viability.
Structure Tests Using BDD Principles
Well-structured tests aligning to "Given, When, Then" behavior-driven development (BDD) principles not only improve comprehension, but provide consistent templates for easier test creation and maintenance at scale.
Abstract Test Logic from Underlying Objects
Designing test building blocks decoupled from specific UI elements and interactions allows tests to withstand frontend changes without breaking scripts. Create resilient, dynamic logic flows instead of brittle recorded steps.
Utilize Page Object Model Architectures
Further increasing test resilience involves centralizing all UI element selectors into well-organized page object classes that can be easily updated once by developers rather than constantly tweaked in dispersed automation code.
Execute Tests in Parallel Across Devices
Running validation on multiple real devices concurrently powers through test backlogs quicker. Cloud testing platforms designed for CI/CD pipelines launch tests in parallel by default to prevent bottlenecks.
Automate Replay Using Real Test Data
Rather than manually creating new test data permutations, smart test tools can automatically feed historical records into parameterized datasets for ongoing scenario replay across various production variables with no added effort.
These automation-focused techniques will help maximize test coverage as your continuous testing strategy matures. Next let‘s examine best practices for accurately simulating real user conditions.
Utilizing Real Devices for Testing
Generating actionable continuous testing feedback requires accurately replicating target customer environments where your digital offerings will ultimately operate. Cloud testing platforms now provide access to thousands of real consumer mobile devices and browsers in a highly scalable model that fits CI/CD velocity demands.
Consider these best practices for leveraging real devices and browsers for testing:
Validate All Supported Configurations
Publically document all supported device models, iOS and Android versions, browser editions, and responsive viewports. Design test suites to comprehensively cover these documented product compatibilities.
Uncover Context-Specific Defects
Flaws triggered by device sensors, geolocations, abusive network conditions, or hardware faults will be missed in emulators. Test real-world use cases on real devices.
Assess Performance Consistently
Run benchmark web vitals and resource usage metrics across diverse platforms to isolate optimization needs by device and browser types instead of averages. Pinpoint exactly where tuning is required.
Confirm Responsiveness Flaws Early
Display rendering and touch latency defects impacting UX may only show up on actual phones and tablets. Validate cross-device consistency before launch.
Compare Vendor Analysis for Insights
DevOps platforms providing device and browser family test results comparisons help teams isolate differences to guide targeted test case expansion.
While maximizing test coverage through scale automation and real device testing removes the bulk of defects, let‘s discuss how to course correct strategies using data.
Using Test Analytics to Guide Strategies
Testing everything equally is impossible as user requirements continually evolve. Intelligent analytics is key for knowing where to focus test writing and execution efforts next or what techniques need tweaking.
Here are ways for using metrics to guide strategic decisions:
Identify Problematic Areas
Leverage historical test execution trends to pinpoint browser and device combinations causing frequent failures or lengthy test runs degrading productivity. Address these pain points.
Highlight Test Gaps
Compare test coverage relative to product usage analytics from current deployment monitoring to identify flows with inadequate validation criteria leaving risk exposure. Fill these priority gaps.
Optimize Flaky Tests
Tests that fail spuriously require maintenance priority otherwise they erode team trust in test reliability and mask real defects. Optimizing the top flaky tests delivers outsized impact.
Influence Where to Test Next
If particular device models used by a majority of customers lack test coverage presently but business importance is accelerating, EST data quantifies exactly where to expand test efforts next.
Promote Accountability
Publish quality benchmark results like lead times, release frequency, time to restore service by team to instill friendly competition towards improving practices. Transparent metrics and progresses catalyzes culture shifts.
Now that we‘ve established practices to scale continuous testing, let‘s discuss common scenario-specific troubleshooting recommendations.
Overcoming Continuous Testing Challenges
As with any technology transformation, moving testing left surfaces challenges – but none are insurmountable. Here is guidance for addressing common pain points:
Bottlenecks From Testing Volume
Solution: Distribute test runs across dynamically scalable nodes in the cloud to maximize parallelism and achieve 10x+ hourly execution capacity minimizing queue wait times.
Flaky Tests Slowing Progress
Solution: Enable auto-healing capabilities on underlying test platforms so infrastructure dynamically troubleshoots and prevents flaky test failures without any manual triage overhead.
Test Environments Delaying Creation
Solution: Consolidate access to thousands of real mobile devices and browsers via cloud so that testing can start immediately without procurement delays.
Reporting Fragmentation Across Tools
Solution: Choose solutions with turnkey centralized reporting dashboards across automated, manual, web, mobile, perf tests to eliminate data silos and manual roll-ups.
Minimal Historical Test Assets
Solution: Record test scripts directly on provider platform UI to instantly build initial test suites and establish viability before custom framework investment.
In my experience helping enterprise companies implement continuous testing, these proven recommendations address a wide range of potential barriers.
Now let‘s summarize key takeaways from our continuous testing journey so far.
Takeaways for Continuous Testing Success
Here are the 6 critical success factors for continuous testing in summarization:
🔷 Make testing responsibilities shared ones across entire product teams rather than just QA roles. Foster collaboration between dev, ops, infosec, and analysis roles.
🔷 Start testing as early as possible in committing code changes – don‘t wait for downstream acceptance milestones. The earlier issues can be isolated, the less quality escapes.
🔷 While unit tests focus on logic validation, run UI automation on thousands of real devices and browsers via cloud to uncover real-world defects on popular platforms.
🔷 Structure tests using BDD principles and page object models to maximize resilience and reuse accelerating authoring at scale.
🔷 Embed related test suites directly into CI/CD pipeline events through native triggers to remove manual execution friction and delays.
🔷 Analyze quality benchmark results, test outcomes, and usage trends to continuously evaluate and guide where testing efforts should shift towards driving material risk reduction.
The bottom line is that continuous testing sets up teams for current and future cycles of innovation by enabling reliable, high velocity releases that meet user demands – without sacrificing experience quality.
Just remember that engraining a culture dedicated to preventative validation through automation and real device testing collectively ensures product reliability keeps pace with your release cadence.
Now over to you! Feel free to bookmark this guide and reference the practical recommendations as you spearhead continuous testing expansions across your development workflows in the days ahead. Wishing you quality and delivery velocity success.
