Let‘s address the elephant in the room – relying solely on manual test execution is no longer practical for teams striving for continuous delivery and deployment pipelines.
In this comprehensive guide, I‘ll leverage over a decade of hands-on experience to transparently break down the fundamental velocity and coverage gaps with manual testing that hinder CD success. I‘ll offer proven recommendations for intelligently incorporating automation to overcome these bottlenecks, both from an enterprise testing strategy and practitioner test tooling perspective.
Whether your team is just dipping toes into continuous integration and delivery or has hit scaling snags trying to push quality code faster, you’ll find actionable best practices here to accelerate releases confidently.
The Bottlenecks of Manual Testing Explained
Continuous delivery promises a compelling vision – developers seamlessly merging code changes into shared branches multiple times daily that get evaluated, tested, and promoted to production automatically if meeting quality gates.
This pace of constant incremental improvements hinges on test suites keeping up. Yet while integral, manual test execution alone introduces several speed bumps making it impractical to sustain.
Velocity Slowdowns
Industry testing experts contend that manual testing completion lags software development by up to 1:3 ratios on average. This significantly throttles delivery velocity and feedback cycles.
Per the recent World Quality Report 2022-23 from Capgemini, only 16% of developers feel testing keeps pace with development speed, indicating room for improvement.
Manually running full regression suites can easily take days or weeks versus a matter of hours or minutes with well-designed test automation flows. When attempting to ship features in hours rather than months, this speed difference causes clear bottlenecks.
Max Coverage Gaps
With code complexity and test matrices expanding exponentially, covering every device, OS, browser, and use case combination manually is infeasible without massive QA teams.
Industry analysts suggest minimum viable test coverage ranges from 75-90% for enterprise applications. Yet RightStar‘s latest survey found manual testing alone often peaks below 50%, leaving product quality more vulnerable.
Consistency & Accuracy Challenges
There‘s no sugarcoating it, manual testing can be mundane. After just 20 minutes, human focus often lapses as testers get fatigued.
Gartner estimates that people make mistakes in up to 20% of repetitive tasks. These testing gaps can allow defects through to customers.
Compare this inconsistency to the precision of automated scripts purpose-built to run tests without deviation 24/7.
Real Device Testing Logistics
While simulators have uses, testing code on actual user devices in real-world network conditions provides the most accurate validation pre-release.
Yet asking testers to manually install an app on dozens of unique device and browser combinations locally becomes expensive and chaotic. Cloud-based real device labs help tame this complexity.
Blending Automation Strategically
Given these data-backed constraints with manual testing, continuously delivering quality software necessitates balancing automation into the mix.
The right combination of manual exploratory testing augmented by automated unit, API, UI, visual testing, fuels dev teams to ship incremental improvements faster without instability.
Here are smart ways to evolve testing strategies.
UI Test Automation Frameworks
Automating front-end user flows provides one major opportunity to accelerate repetitive tasks. Open source and commercial test automation frameworks exist across web, Android, and iOS platforms.
TestProject, Selenium, Appium, Espresso, and XCUITest offer popular options to simulate UI actions at scale. IDE features speed building reusable test scripts.
API Testing Automation
As enterprises adopt microservices patterns, properly validating APIs becomes mandatory. API testing automation offloads sending requests and validating responses to tools instead of manual checks.
Postman, Karate, Rest Assured, ReadyAPI all help streamline this API testing at enterprise scale and speed.
Unit Test Automation
By empowering developers to test individual functions and components before integration, unit test frameworks like JUnit, NUnit, PyUnit catch issues exponentially earlier, saving costs before reaching QA.
Identifying Automation Candidates
With so many tests, where do teams start focusing automation for maximal impact?
Prime targets include repetitive test cases prone to human inconsistencies or that are tedious to perform manually. Also prioritize UX flows vital to end users with little tolerance for defects.
Conversely, exploratory tests analyzing visual design, user experience (UX), complex edge cases can still provide value manually.
Cloud Real Device Testing
While simulators have uses, testing code on actual user devices in real-world network conditions provides the most accurate validation pre-release.
Yet asking testers to manually install an app on dozens of unique device and browser combinations locally becomes expensive and chaotic. Cloud-based real device labs help tame this complexity.
Popular options like BrowserStack and LambdaTest simplify integrating this realism into pipelines with extensive device coverage plus built-in automation capabilities.
Calculating Automation ROI
What can teams expect to invest in test automation – and how long until value is realized?
According to recent research by TestRobot, test automation development tends to cost around $20 per test script hour. With frameworks abstracting complexity, that initial ramp up pays dividends. Average maintenance costs then dip below $5 hourly post-implementation.
Forrester finds top-performing companies see full test automation ROI in less than 6 months. Scaling delivery velocity 10x versus manual testing is feasible in a year.
Gaining Buy-In
Like any enterprise shift, evolving QA automation requires upfront investment and multi-department alignment.
I advise test leaders pitch automation as an accelerator for meeting business KPIs like release frequency, cost reductions, and lower defect rates. Frame the benefits in revenue and competitiveness.
For smooth adoption, plan to phase manual and automated testing responsibly, adding automation iteratively around current workflows rather than displacing overnight. Leadership support and adaptive culture are critical enablers.
Start Engineering Quality into Code
The data shows relying purely on manual testing severely restricts release velocity, stability, and quality coverage – making it an insufficient strategy for maturing continuous delivery initiatives.
Blending test automation distributes the workload for validating updates pre-release so teams can publish progress faster without instability.
Carefully assess where existing manual processes can be enhanced by injecting off-the-shelf and custom test automation across the app and API layers. This allows sustaining CD goals around incremental improvements and live site updates without defects disrupting users.
The future of quality lies in engineering it directly into code. Let‘s discuss where your pipelines may benefit from expanded test automation techniques today.
