As quality assurance leaders, one question we inevitably grapple with is – how do we determine the testing metrics that accurately evaluate the software delivery pipeline? Which key indicators should we track to gain visibility into bottlenecks undermining team productivity, release quality and business KPIs?
This dilemma is fully justified, because testing metrics are mission-critical tools that empower us to:
- Quantify the ROI of quality assurance
- Diagnose exactly where and how defects decrease customer happiness
- Spot testing capacity gaps deflating feature velocity
- Guide data-informed decisions around timelines, releases and resourcing
When leveraged judiciously, metrics transform previously hidden workflow deficiencies into transparent, actionable opportunities catapulting teams closer towards excellence.
Conversely, tracking the wrong metrics breeds distractions by shining the spotlight on areas misaligned from business goals. Or worse, major bottlenecks stay concealed resulting in failures traced back to QA. Neither helps our cause as trusted advisors safeguarding customer satisfaction through rigorous validation.
So in this comprehensive guide, we’ll unpack:
- What defines a genuinely effective testing metric?
- Common categories of metrics available
- Step-by-step how to select metrics that matter
So whether you‘re an aspiring first-time QA manager or industry veteran, I promise tips that unlock rapid enhancements. Let‘s get learning!
Traits that Characterize an Impactful Testing Metric
At its core, a valuable testing metric:
#1: Ties Directly to Customer Happiness
Never lose sight that our purpose is upholding exceptional customer/user experiences through rigorous defect detection and prevention. So ideal metrics focus on interactions, journeys, flows and capabilities where bugs predominantly frustrate consumers.
Some examples? Payments failing at checkout, data syncing inaccurately across devices, laggy response times degrading productivity, notifications rarely arriving on time, etc. These reliability issues provoke complaints, returns and churn if left unchecked.
That‘s why forward-thinking teams go so far as to quantify a financial cost for the software defects associated with each metric e.g. "$2.3M annually lost to shopping cart abandonment due to errors." This urges proper prioritization by revealing P&L impact.
#2: Enables Tracking Progress Over Time
The best metrics display measurable improvements or deterioration from version to version. Comparing fresh numbers against past performance spots trends instantly without awaiting stakeholder complaints.
For instance, tracking first-call resolution rates by customer support teams across iOS releases determines if defects are rising or declining through empirical data. Do users still face the same frustrations contacting help desks?
Static one-time metrics fail to provide this crucial ongoing feedback loop. Strive for actionable comparables.
#3: Signals How Testing Strategy Should Evolve
When metrics expose underperformance around an area instrumental to consumers like uptime, it prompts constructive reflection around improving test scenarios, tools and processes to boost said metrics.
If quarterly app crash rates rise despite extensive functional testing, we must get creative. Perhaps shift left practices like canary releases allow real user monitoring? Or is more investment needed in stress testing for peak traffic resilience?
Falling metrics on customer pain points suggest QA plans require reimagination to uplift outcomes. We must become curious.
#4: Is Simple to Track Operationally
Testing leaders already face endless demands on their time. So metrics trailing must utilize lightweight automation rather than manual number crunching which quickly becomes burdensome.
Prioritize measurements seamlessly compiled by QA workflow software vs Excel sheet updates requiring tester hours. The goal is spending under 5 minutes glancing at charts vs days preparing them.
What gets measured gets managed, but not at the expense of tester capacity. Seek sustainability through smart tooling.
#5: Provides Interpretable, Actionable Insights
An effective testing metric transforms abstract quality goals into unambiguous, approachable objectives that cement baby steps towards broader aspirations.
For example, rather than a vague mandate to “reduce app defects” prod the team with “no more than one crash per 100 taps” or “90% API response times under 300 milliseconds.”
Now everyone including developers rallies around the same measurable benchmarks drilling down on specifics.
In summary, stellar testing metrics share one common attribute – enabling data-informed adjustments drawing teams closer to customer-centric excellence through heightened quality.
Now let‘s examine the testing metrics types available to leverage…
Testing Metrics Generally Fall Under 2 Buckets
Testing metrics typically come in two interdependent flavors – input-focused metrics guiding day-to-day test activities and outcome-based metrics determining if strategies yielded target results.
Leading Metrics
Leading indicators track the volume of ongoing testing work and defects being executed/detected by validation teams through:
- Test cases run
- % code coverage attained
- Unique defects identified
- Bug severity breakdown
- Automated checks completed
Think input KPIs. Tracking productivity early in the pipeline ensures adequate rigor so discovered issues get fixed before customers notice anything amiss after launch. No firefighting needed.
Lagging Metrics
Whereas lagging metrics help assess downstream impacts of testing on end outcomes by monitoring metrics like:
- Live site uptime/reliability
- Peak traffic supported without failures
- Customer support ticket volumes
- Returns/refund rates
- Churn or abandonment rates
Consider these rearview mirror perspectives after releases. Lagging KPIs confirm whether commercial goals set by leadership are being sustainably met over time through adequate testing safeguards.
For balanced insights, QA managers should own indicator types in harmony. Leading metrics ensure tester output stays problems aren’t creeping in while lagging data verifies if outcomes actually fulfilled targets to delight users.
You can never fix downstream what you don’t first measure upstream. Now for the step-by-steps on picking what specifically to track…
How to Determine Testing Metrics Aligned to Business Success
Through years helping diverse clients shape testing metrics strategies, I‘ve compiled a 3 step formula for workshops which uncovers the right measurements:
Step #1: Map Metrics to Core Company Objectives
Every organization cares about a handful of key results more than anything else as bellwethers of commercial success. These usually involve some blend of new customers acquired, market share gained, revenue attained, profit secured and brand loyalty earned.
So in executive interviews understand the 1-2 quarterly or annual business KPIs deemed most pivotal to track and reward against.
Then explore what key software user interactions or components disproportionately influence said KPIs for better or worse. This pinpoints areas where new features directly trigger upgrades or flaws detract from renewals.
Finally determine testing metrics providing empirical signals into progress or setbacks towards goals through those customer journeys.
For example, let’s say a SAAS CEO shares her net revenue retention target aims for 120% annually as more subscribers purchase added product seats. So long as expansion outweighs churn amongst existing accounts, that goal stays feasible.
The customer success team then highlights how a shoddy new reporting module release frustrated power users – delaying upsells. So tests around analytics reliability become crucial retention health metrics.
See how we bridged the gap between commercial aspirations to product testing measurement areas? The data points gained focus testing capacity on what matters most to sustained leadership confidence.
Step #2: Draft Metrics Addressing Team Member Pain Points
Next, circulate a subset of potential metrics tied to leadership goals amongst testers, developers and customer support agents. Encourage spirited debate on whether selected KPIs accurately reflect release readiness experiences seen on the frontlines by domain experts.
The goal is soliciting peer feedback on what measurements provide the most meaningful, actionable insights into release quality from their vantage point. Numbers should validate (or invalidate) on-ground sentiment.
Listen intently for undercurrents around what frustrates agents dealing with customer rage or developers addressing past post-prod bug backlogs. Then devise metrics that quantify if such experiences repeat release on release due to gaps.
Collaborative metrics setting builds staff buy in, while benefiting from their specialized expertise knowing where product shortcuts undermine users.
Step #3: Continuously Align Metrics to Evolving Business Objectives
As companies grow and pivot strategies responding to market shifts, metrics trailing must also stay dynamically relevant. Quarterly reviews should determine whether current KPIs still monitor customer playbook areas that leadership now prioritizes for growth investments – or have become dated.
Special care must be taken to notice emerging competitive gaps that demand measurements. For instance, are rivals leveraging AI-assisted testing or synthetic monitoring to accelerate release velocities previously unfathomable in the category? The pressures raised may require revisiting scope.
Essentially, treat measurements as living frameworks necessitating care, feeding and occasional restructuring to stay meaningful amidst ever-changing business landscapes.
Now that approach is clear, let‘s view industry examples…
Examples of Meaningful Testing Metrics By Industry
While the overarching goal of metrics is furthering business objectives, tactics differ across software domains based on:
Ecommerce Platform Metrics
For online stores supporting household names, website performance and availability metrics take centerstage. According to Akamai, a one second delay in page loads degrades conversions by 7%. So continuous monitoring here prevents lost transactions around:
- Peak traffic supported without downtime
- Page load speed distributions across geo regions
- Third party app stability (payment gateways, shipping integrations, etc)
- Inventory accuracy checks
- Abandoned cart rates by category
With margins tightly linked to converting site visitors, conversion funnel friction checks qualify as supreme KPIs.
Mobile Gaming App Metrics
For creators behind the next viral gaming craze, maximizing player stickiness and monetization potential make or break studios relying on unpredictable hits. So QA requires awareness of:
- DAUs and MAUs
- Session length distributions
- App stability rates
- Store rating sentiment
- Tutorial completion percentages
Catching gameplay addiction early and often makes user retention feasible.
Digital Banking Metrics
For fintech disruptors seeking to win trust through radical transparency and reliability, site security metrics outweigh all else. Locking down customer assets is table stakes, by tracking:
- Attempted fraud patterns
- Vulnerability scans across web assets
- Backup restoration testing
- Biometric authentication failure rates
- New endpoint security built/tested
When money changes hands, QA sets the confidence bar for user peace of mind.
We‘ve covered extensive ground from selecting indicator types to industry examples. Now let‘s discuss the special role real devices play in accurate data compilation…
Why Real Devices Are Key to Trustworthy Metrics
The web and mobile landscape encompasses an exponentially complex matrix of smartphones, tablets, browsers, operating systems, networks and form factors. Testing metrics crafted without traversing genuine device diversity carries minimal meaning in mirroring actual usage conditions.
Yet shockingly I still encounter teams relying exclusively on emulators and simulators for metrics generation despite fundamental limitations like:
Synthetic User Representation
Emulators mimic constrained combinations of hardware and software capabilities without accounting for real-world user behaviors, environments and personalized device settings.
But the mobile universe spans thousands of phone models subject to calls, commutes, app choreography, notification habits and more creating unique contexts. Defects triggered by such factors go undetected in emulated test runs undermining metric reliability when apps later crash or freeze for consumers amidst complex device activities.
Bottomline: Metrics without realism provide misleading confidence.
Incomplete Performance Profiling
Vital user experience metrics around app stability, loading times, rendering smoothness, scrolling fluidity, video streaming, and button responsiveness vary widely across true devices based on differentiated processor powers, graphics capacities and network adapters.
Attempting performance profiling through emulators unable to mimic this hardware and carrier diversity at scale blinds teams to optimization opportunities discovered only in real mobile landscapes. Defining metrics using such narrow synthetic data leaves gaping blindspots.
Sparse Cross-Browser Testing
While mobile dominates usage, hundreds of millions still rely on legacy browsers across desktop operating systems for tasks like banking, coding and design. Emulators restrict browser-OS combinations to recent cookie cutter variants.
However real usage patterns paint a different picture still dependent on dated setups like Windows 7 machines leveraging IE11 or Linux running Firefox ESR releases. Catching web compatibility issues here requires metrics gathering across authentic test beds.
In summary, blissful ignorance through metrics fabricated without real devices poses terrible risks once applications launch for a digitally connected world spanning thousands of phone models and browsers utilized simultaneously.
So the key takeaway is…
Embrace diverse real device cloud testing early and often to create measurable quality benchmarks rooted in genuine end-user attributes for credible ongoing visibility.
Now let‘s drive home why getting metrics spot on matters tremendously…
The Secret Superpowers Unlocked by Precision Metrics
Instilling a metrics focused testing culture ushers in incredible organizational advantages like:
Superior Defect Prevention Strategies
When numbers consistently confirm lagging KPIs like support ticket volumes remain low while productivity and platform reliability stay high, it validates existing testing methods working beautifully. Terms like “if it ain’t broke, don’t fix it” come to mind.
This insight permits doubling down on what already works well while innovating only where measured weakness exists. Defect prevention becomes surgical and precise.
Predictive Resourcing for Scale
Trendline analysis of key input metrics around test cases executed monthly or automation script maintenance effort gives management data-backed inputs into headcount or tooling needs required sustaining growth without quality erosion.
Businesses scale faster by futureproofing budgets as metrics depict how much workload expands over time based on history.
Unified Focus on What Matters
Testing teams stay aligned on priorities through shared visibility into metrics displaying opportunities around improving reliability, performance efficiency or platform adoption most in need of attention based on measured evidence.
This rallies everyone around measurable outcomes verbally stated by leadership as necessary for commercial success. Progress stays transparent.
And those are just a few examples of how markers tracking the health of our testing pipelines unlock incredible potential for teams embracing them. The benefits multiply over time.
So in closing, my biggest lesson learned over a decade in quality assurance is…
Whatever business goals ultimately decide software testing headcount, tooling and focus – make sure your metrics strategy provides clarity into how reliably those expectations get met release upon release.
Because when testing KPIs guide executive decisions, they stay rooted in empirical insights rather than fluctuating opinions or emotions. And metrics grounded in reality never fail us.
I welcome all your wonderful questions, feedback and stories below on what helped shape effective metrics within your teams over the years!
