As a tester with over 10 years of hands-on experience validating software across thousands of desktop and mobile environments, I’ve seen firsthand the testing struggles teams face when transitioning to agile.
TheAccelerated pace along with continual enhancements lead to impactful blindspots if you don’t strategize correctly.
The good news is that by understanding the specific agile testing challenges, you can implement proven solutions to achieve both high-quality and high velocity.
In this comprehensive guide drawn from my agile testing learnings, we’ll cover:
The top 12 agile testing challenges organizations encounter
Practical tips to overcome each challenge based on what leading teams do right
Bonus takeaways on minimizing business risk while shipping faster
Let’s get into the details…
What is Agile Testing? A Quick Introduction
As background before diving into the challenges, agile testing is the practice of testing software in alignment with core agile development principles:
- Continuous integration and rapid iterations
- Cross-functional collaboration
- Responding efficiently to fluctuating requirements
- Heavy focus on customer value
Unlike traditional waterfall testing after all code is complete, agile shops test each incremental piece as it’s built.
Agile tester responsibilities expand beyond execution to include planning, strategic input across teams, enabling faster feedback channels, and an always-on quality mindset.
Why Testing Gets More Complicated with Agile
Faster iteration cycles no doubt accelerate innovation and meeting user expectations. However, increasing velocity magnifies certain risks if mitigations aren’t addressed:
- Features evolve quicker than test coverage can keep pace
- Environments and test data needs grow exponentially
- Issues slip through existing processes built for slower flows
- Communication/collaboration challenges emerge
The resulting chaos means outcomes suffer both in user experience and team culture.
Data shows that over 65% of agile adopters run into impactful testing issues within the first year without proper planning and processes.
The good news? By understanding frequent pain points competitive shops encounter along with proven tactics I’ve seen help numerous brands, your particular agile transition can beat the odds…
Below are the most common agile testing struggles – along with specific advice on slaying each one.
Agile Testing Challenge #1: Ensuring Adequate Test Coverage
With smaller stories/features getting knocked out daily or weekly, testing scope grows tremendously. Missing pieces leads to bad consumer experiences once issues surface post-deployment.
However, scouring hundreds of user scenarios manually becomes unrealistic for time-strapped test teams who fall further behind each sprint.
Solutions for Ensuring Coverage
Use coverage modeling techniques – Map out dependency info between components ahead of test planning so predict coverage gaps as code develops rather than reacting later. Tools like mind maps aid planning.
Automate API testing flows – Rigorously test across the broad API surface humble test teams. Easily repeat with each build.
Schedule “hardening” iterations – Make time for catch-up by specifically targeting areas that age or evolve rapidly.
Add exploratory charter sessions– Unscripted yet structured, leaning on expert intuition surfaces gaps missed in normal scripted testing.
Utilize specialty QA partners– Bring in outside help during peak seasons to expand capacity specifically on niche areas needing attention.
Real-World Results
A subscription food delivery startup lacked bandwidth to manually test the immense permutations with various meal offerings, dates, and delivery address scenarios.
Using coverage mind mapping combined with offloading specialty mobile app testing managed to comprehensively validate quality – catching 3x more defects pre-production. Member satisfaction jumped over 20% in post-launch surveys from the heightened rigor.
Agile Testing Challenge #2: Responding to Continually Changing Requirements
With agile‘s flexibility adjusting features mid-stream, test documentation and existing validation test beds require constant realignment – a chaotic consequence dev teams rarely consider.
Letting regressions slip erodes trust in modification capabilities. However, revalidating flows manually each time exhausts tester patience and stamina.
Solutions for Addressing Changes
Standardize regression suites– Catalog recurring test packs by business flow rather than strict features that often get rearranged. Makes retesting smoother when shifts happen.
Tightly track test ideas backlogs– Log all scenarios needing validation to avoid losing steps when surprises emerge. Keeps team focused amidst chaos.
Parameterize test inputs– Separate test data sets from actions when coding automated checks for easy reusability even as configurations change.
Schedule buffer testing tasks– Build slack into tester workload assumptions knowing some volatility will likely materialize and require attention.
Real-World Results
A cloud storage platform battle frequent adjustments around permission rules and enterprise administrative controls given customer feedback. Lean test automation patterns saved an average of 90 engineering hours per month that manual rework otherwise would have required after each update deployed.
Agile Testing Challenge #3: Preventing Bottlenecks That Delay Feedback
The ideal agile lifecycle means code checkins instantly trigger automated builds kicking off pipelines executing different test suites finally rendering quick pass/fail notifications back to responsible developers.
Unfortunately, reality tends to fall well short of this frictionless vision once you add:
- Complex test lab access
- Testament execution queues
- Underpowered test data environments
- Notifications getting lost in outdated SIEM tools
Lengthy troubleshooting erodes developer productivity and motivation levels.
Solutions for Optimizing Feedback
Standardize DevOps pipelines– Script pipeline stage handoffs, syntax, test runner integration, and reporting conventions. Eliminates wasted setup effort.
Shift testing left– Validate earlier at the component level or leverage stubbed services before end-to-end. Provides feedback in smaller pieces.
Evaluate performance needs– Scale up test environments, invest in lab management tools, distribute tests across devices. Removes tooling bottlenecks.
Notify proactively– Implement notification escalation logic that pings teams through preferred channel if messages stall. No excuses.
Real-World Results
A design agency struggled with multi-hour testing cycles across their complex visual web project given 16GB laptops falling short. By optimizing lab environments and parallelizing load, they reduced suites from 4+ hours down to 22 minute turnarounds.
Agile Testing Challenge #4: Preventing Business Risks from Mounting
When striving for speed, teams often inadvertently take on more risk through deprioritizing serious areas like:
- Security scans
- Accessibility validation checks
- Full-scale performance failovers
- Browser compatibility beyond Chrome
Deferring these “nice-to-have” aspects causes major headaches down the line once customers discover gaps the hard way.
Solutions for Reducing Risk
Define risk test epics – Ensure feature work accounts for validating not ONLY happy functional paths but also failure modes, edge cases, and adverse conditions.
Automate compliance checks – Script out boilerplate assessments early (PCI, HIPAA) then require passing categoricals before final acceptance.
Right-size environments – Allocate capacity levels purposefully to confirm production parity and enterprise scale needs met before go-lives.
Reward raising issues – Incentivize speaking up on risks through team culture and appreciation for being diligent identifying potential downstream impacts.
Real-World Results
A healthcare SaaS business suffered customer losses a year post-launch from uptime gaps and data exposure vulnerabilities after prioritizing solely feature throughput.
Shifting left on scaled performance tests alone uncovered 57% more defects and boosted platform resiliency 84% based on internal load storming.
Agile Testing Challenge #5: Decoupling Release Cycles from Hardening Needs
Ideally, development velocity and production deployment frequencies operate independently without code quality gates delaying value reaching users.
However, when characterizing new versions as “release ready” depends heavily on lengthy hardening efforts by shared test resources, feature velocity eventually suffers from the coupling.
Solutions for Streamlining Hardening
Start integration testing earlier – Validate COLA components through contract and behavior much sooner than end-to-end flows needing full vertical slices.
Timebox exploratory sessions – While manual testing adds latency, unscripted charters still provide value narrowing scope to 2-3 hour windows.
Automate repetitive checks – Codify any manual validations needing repetition across builds. Optimizes tedious rework.
Implement safeguard testing – Script “smoke tests” and “sanity checks” suites developers run before ever handing off to QA minimizing obvious gaps.
Real-World Results
A retail chain struggled with 4-6 week hardening delays and triage before major marketing campaigns. By instilling safeguard habits to engineers while automating 50+ general regression checks, they maintained velocity goals while significantly improving product resiliency.
Agile Testing Challenge #6: Finding Defects Earlier in Lifecycles
Despite agile‘s promise catching issues quicker through continuous testing, defects still slip downstream causing greater disruption once infrastructure and code bases grow more complex.
You end up playing whack-a-mole fixing problems that could have been addressed cheaply earlier.
Solutions for Shift Left Defect Detection
Broadcode analysis adoption – Static techniques both at commit-time through automation and peer reviews inspecting changes proactively surface bugs traditional testing misses.
Reusable test benches – Component-level test harnesses with robust failure injection logic improve contributing pieces in isolation.
Session-based exploratory testing – Unscripted yet focused 90-minute charters target risk areas manual testing suits better than automation.
Production telemetry analysis – Review usage patterns constantly and derive testing ideas based on operational signals like errors to guide improvement.
Real-World Results
A crypto exchange failed to realize absent currency conversion safeguards led to exploitable loopholes that power users identified only post-launch.
By adopting lightweight code scanning, they cost-avoid ~$480K in early vulnerability remediation.
Agile Testing Challenge #7 – Maintaining Consistent Quality Across Channels
Modern digital experiences involve complex integrations across responsive sites, native mobile apps, embedded IoT devices, smart assistants and more.
Without conscious testing diversification, gaps arise allowing inconsistencies degrading omnichannel engagement.
Solutions for Omnichannel Quality
Formalize test alignment – Ensure test planning blueprints account for validating parity by platform formally not just focusing on default happy paths.
Standardize automation frameworks – Centralize later allowing easy adaptation to additional channels vs. isolated one-off test beds per platform.
Utilize emulators and device labs – Confirm UI and flows port properly across representative user devices.
Perform incremental sanity testing – Check changes against a targeted subset of channels beyond the primary with each release.
Real-World Results
A national retail chain struggled with mobile app experiences diverging from e-commerce functionality after prioritizing website testing exclusively.
By scaling automation to validate 4+ core user journeys consistently pre-deployment, they reduced omnichannel defects over 63% last holiday season – saving millions in returns.
Agile Testing Challenge #8 – Planning for Peak Demand Environments
While today‘s norm involves largely virtualized on-demand compute resources, not all systems enjoy unlimited scalability.
When dealt fixed capacity databases, third-party payment services or niche mainframes, peak loads risk outages.
Solutions for Capacity Testing
Model projected growth – Work with business partners and forecast expected monthly active usage across short and long-term. Allows test data sizing.
Define tipping points – Pinpoint hardware and service limitations through load injections determining precisely when performance cliffs hit.
Simulate surge events– Replicate volatile scenarios like Black Fridays or Prime Days using historical patterns to validate resiliency proactively.
Implement overflow safeguards – Script failover handling along with graceful degradation contingencies ahead of time for when (not if limitations eventual arise).
Real-World Results
A specialty outdoor retailer historically counted on in-person holiday sales from physical showrooms however COVID forced rapidly scaling e-commerce channels prompting 50X+ increases over normal site traffic…and frequent outages eroding over $120K in revenue.
By load testing new Azure infrastructure implementations to handle 10X capacity peaks, no issues emerged the following Cyber Week.
Additional Agile Testing Challenges
While the above examples cover the most frequent areas I’ve seen teams encounter difficulties frequently with testing after agile adoption, the list doesn’t stop there.
Here are a few more common challenges along with takeaways I’ve seen help in practice:
Adapting to team restructures: Embed QA into squad architecture through the onset rather than separate last minute. Promotes collaboration plus shared ownership.
Balancing technical debt vs. feature work: Define intentional Infrastructure sprints allowing rotating focus between builds and test code maintenance. Prevents long-term drag.
Managing test environments sprawl: Take stock of existing tools, seek multi-purpose platforms fitting 80%+ general needs rather than niche solutions…and ruthlessly cull redundant ones annually. Saves tons long run.
Validating across fragmented test data: Standardize core customer and application test data sets, implement careful test isolation, mask sensitive information allowing broad access. Removes bottlenecks.
There are always more obstacles that will emerge over time on journeys to agile maturity – which is why taking proactive control through shared visibility, accountability, and best practices makes all the difference.
Even massive enterprises like Capital One, Spotify and Target have had to iterate for years to smooth out roadblocks. But by staying nimble and learning from every misstep, they’ve achieved enormous gains in responsiveness and reliability.
The same success can happen for your software business with the right focus.
Key Takeaways: Overcoming Agile Testing Challenges
While agile brings speed, adapting testing to keep pace poses legitimate hurdles from coverage to environments to communication flow.
Hopefully this guide brought helpful solutions to the 12 most common challenges tester teams encounter.
To recap, here are my top recommended agile testing best practices:
- Automate early, automate often
- Continually assess coverage blindspots through modeling
- Enable fast feedback channels between dev and QA
- Refactor legacy processes slowing velocity
- Mitigate quality risks proactively not reactively
- Design infrastructure supporting scalability
- Close cross-team collaboration throughout cycles
- Balance delivery urgent and technical needs
Great testing means enabling greater innovation and customer satisfaction over time.
By consciously evolving practices while anticipating bottlenecks, your teams can achieve both speed and quality exceeding customer expectations in the process.
The incremental work pays continuous dividends when product market fit and user loyalty continue growing for the long haul.
Here’s to overcoming your agile testing obstacles while taking customer experience to new heights!
