As we enter 2024, what automation testing trends will shape the future?
Each year, I pick up on trends gleaned from interviewing hundreds of folks on my podcast and news stories featured on my TestGuild DevSecOps News show.
Also, developments in one year tend to set up the trends for the following year. (Some trends I pointed out from 2023 also will continue in 2024)
Money invested in companies is one key that always acts as a leading indicator for a trend.
Read all the way to the end because you won’t believe #12!
With that in mind, I’ve compiled my list of the top automation testing trends for 2024 using the above criteria, just like I’ve been doing for the past 13 years.
- The use of AI artificial intelligence and machine learning in automation testing will increase, allowing for more efficient and accurate testing.
- The shift left testing trend to find bugs earlier and cheaper by executing more automated tests against code changes in the CI/CD pipeline rather than just pre-production.
- The rise of testing in production to validate features at real production scale with millions of users.
The first one is obvious, so let’s get it out of the way.
1. AI-Assisted Testing Boom Continuous
AI and machine learning will become even more integral to helping you boost your testing efficiency.
But is any tester really using AI to help with testing now?
Surprisingly, based on surveys like LambdaTest and The TestGuild webinar polls, most testers have already adopted AI into their workflow. (So if you’re not experimenting with AI, you are already behind)
A recent Lambada Test survey found AI Adoption among Software Testers at 78%.
In a TestGuild Webinar on Generative AI, 76% of testers say they already use (or plan on using) Chat-GPT AI to help them with their day-to-day testing activities.
I think this already high adoption rate will grow even more with more choices in AI test tooling and platforms proliferating across software teams in 2024.
But remember, real software testing obviously requires a deep understanding of the software under test. However, it does not rule out AI assistance with many testing activities:
- Automated Test Case Generation: AI algorithms can analyze requirements, user stories, and application changes to design optimal test cases targeting key use cases to maximize coverage.
- Intelligent Test Data Creation: Generating high-quality, realistic test data at scale remains a major bottleneck. However, AI/ML techniques like generative adversarial networks (GANs) show promise for synthesizing production-grade test datasets efficiently for greater test coverage. The capabilities of GANs are impressive for data augmentation since they can effectively learn the underlying distribution of the input data and generate very realistic samples. However, once again, you really need a tester to evaluate the quality of the generated sample.
- Predictive Defect Models: AI/ML models will be trained on historical defects and releases to predict types of bugs likely to manifest and high-risk areas of the code to guide test targeting for maximized effectiveness.
- Test Optimization & Prioritization: By assessing business risk, use cases, and development activity, ML will smartly guide test case prioritization and coverage for each release and code change to allocate testing resources to the highest value areas.
- Automated Root Cause Analysis: Once bugs surface, AI will analyze logged telemetry across the entire integrated architecture to rapidly and automatically pinpoint the likely root causes of software failures. (This is an area where Guild members say they struggle)
Once again, all these activities require a human tester to make sure the AI-generated data is correct.
Follow the money in 2023 (key trend indicator):
- Perforce released “Test Data Pro by BlazeMeter,” which leverages generative AI technology to simplify and enhance test data creation.
- Katalon announced TrueTest, an automated regression testing solution leveraging AI to generate and maintain regression tests by monitoring user activity.
- Integrated GenAI into TestResults.io to Create Test Cases
Multimodal AI Takes Automation to the Next Level
Plus, with the recent release of multimodal AI Google’s Gemini, AI in Testing will get even more interesting.
If you haven’t heard, Multimodal AI incorporates multiple data types like text, images, speech, and sensor inputs processed via different algorithms to enable more flexible automated testing capabilities.
This is cool because this should allow multimodal AI to enhance test automation with things like:
- Visual Application Validation: Test bots leverage computer vision alongside text processing to traverse and validate graphical interfaces like a human tester would for greater coverage.
- Expanded Types of Testable Applications The ability to interpret images, video, speech, and other signals allows test automation to cover a wider variety of applications beyond traditional web apps.
- Training with Diverse Sensory Inputs: Leveraging image, voice, and other multimodal inputs to train automated testing models leads to a more nuanced understanding of system behavior and defects.
- Mainstream Accessibility Integration of advanced multimodal frameworks like GPT-4 Turbo into commercial test tools makes these innovations accessible to non-specialized test teams.
By combining multiple data types processed via specialized algorithms tailored to each input, multimodal AI will deliver more comprehensive test automation capabilities, helping to mirror real-world complexity.
Is AI perfect? NO! Is it a silver bullet? NO!
But it cannot be ignored and is very beneficial when used correctly, and it is only getting better and better.
We have a bunch of sessions at this year's online Automation Guild design specifically to help automation engineers with AI like:
- A Practical Guide To AI: How to Improve Quality In Software Development
- From Code to Cognition: The Journey Of AI And Automation
2. Testing For Low-Code/No-Code Apps
By 2025, surveys have shown that 70% of newly developed applications will use low-code or no-code technology, up from less than 25% in 2020.
Because of this, testing needs will grow exponentially as the adoption of low-code/no-code app development spikes across companies. Demand will surge for optimized, automated testing solutions tailored for low-code platforms.
Low-code/no-code application development platforms enable faster delivery of apps with minimal hand-coding. As more companies adopt these to empower business users and accelerate digital initiatives, testing needs grow exponentially.
Key aspects to testing low-code apps:
- E2E Testing: Low-code apps often support web, mobile, and other channels that require automated testing across surfaces.
- Integration Testing Challenges: Connecting low-code apps to existing systems requires API and integration testing for end-to-end flows.
- Enabling Non-Technical Users: Allowing business teams owning low-code apps to execute tests without deep technical expertise is crucial for adoption.
- Optimized CI/CD Pipeline Integration: Testing solutions must be embedded efficiently into continuous delivery pipelines favored by low-code platforms.
- Scalability Across App Portfolios: Testing capabilities must economically scale across large portfolios of low-code apps as adoption spreads enterprise-wide.
Follow the money in 2023 (key trend indicator):
- UIPath’s business automation platform recently added integration of new generative artificial intelligence, specialized AI and automation capabilities that will make it simple to automate almost any business task using natural language.
- Applitools announced its acquisition of Preflight, a leading low-code test automation platform.
3. End-to-end API Testing Automation
As you know, APIs serve as the connectivity fabric across systems in modern microservices architectures.
With API usage multiplying, automated API testing capabilities must expand in 2024 to validate functionality, performance, and security pre-production.
Key aspects of robust API testing automation needed in 2024 include:
- Functional Validation Verify APIs correctly implement required operations and business logic through extensive parameterized test cases.
- Load & Performance Testing Execute high-volume test loads against APIs to uncover latency, bottlenecks, or errors under load using realistic scenarios.
- Security Testing Validate APIs resist OWASP top threats like injection attacks, broken authentication, rate limiting, and input filtering using negative test cases.
- Contract Validation
Compare API responses against OpenAPI or Swagger schemas to ensure compliance with defined contracts.
- Service Virtualization Simulate APIs not yet developed or mock external facing APIs to enable earlier testing before availability.
Leading API testing platforms and tools like Karate, Postman, Parasoft, and Tricentis provide test automation capabilities like auto-generated test cases and tools like Cypress and Playwright, which will see increased adoption for API Testing.
Some sessions at AG24 to help you learn more about API Testing for 2024:
- API Automation Mastery: From Novice To Ninja
- Accelerating API Testing: A Practical Guide to Automation with Cypress
- Mocking The Unmockable: Elevating Test Automation Stability Through Advanced API Simulation
- Safeguarding Digital Assets: Uncovering Security Risks In APIs
4. Shift Left Goes Mainstream
To find bugs quicker and cheaper, in 2024, DevOps teams will continue to shift testing left in the lifecycle to run earlier against code changes in the CI/CD pipeline. Unit testing, integration testing, performance testing, and more will shift left.
Shift left testing involves executing automated tests earlier against code changes in the software delivery lifecycle rather than just pre-production. Enabled by CI/CD pipeline integration, shift left enables finding defects quicker and cheaper.
Types of testing shifting left include:
- Unit Testing Developers often neglect writing unit tests covering code modules. Shift left embeds unit testing into commits and PRs.
- Integration Testing Validate new code impacts with real dependencies rather than mocks after merge by automated integration tests through pipeline execution.
- Contract Testing Run API contract tests against pull requests to instantly assess consumer compatibility for API definitions.
- Security Scanning Static and dynamic application security testing scans assess code early for vulnerabilities like SQL injection upon each commit.
- Performance Testing Detect performance regressions instantly rather than right before going live by pipeline-integrated load tests against major code changes judging impact.
Shift left testing improves developer productivity, reduces escape defects, and accelerates delivery velocity. By flagging issues earlier at code commit rather than staging, shift left allows cheaper remediation. Testing shifts from being a release gate to an integrated enabler for rapid, high-quality software delivery.
By executing more automated tests pre-merge complementing end-stage full regression testing, issues resolve faster with lower cost, avoiding late-stage surprises.
Testing transforms from release gate to developer partner through pipeline embedment.
Automation Guild Sessions to help:
- Automate Quality Assurance Reporting In Your CI Pipeline
- Shift Left Performance Testing
5. Containers Enable Test Environment Consistency
Using containers to provision test environments provides consistency across stages, improves collaboration across teams, and helps enable automated environment provisioning.
As applications become complex, consistently configuring integrated test environments across stages poses challenges, leading to “works on my machine” issues. Containers transform test environment reliability.
Benefits containers bring test environments:
- Isolated Dependency Replication Containers package and standardized all libraries, data, and configurations needed for code execution, simplifying test bed creation.
- Consistent Environment Provisioning
Container registries streamline spinning up consistent test environments on demand without manual installation frustration across stages.
- Infrastructure Cost Savings Containers enable developers and QA to replicate diverse test configurations cost efficiently without wasting VMs.
- Automated Scaling & Teardown Programmatically spins up containerized test environments in pipeline stages and automatically scales them up or destroys them on demand.
Leading platforms like Docker and Kubernetes make creating reusable micro-service-style test environments easier. Cloud vendors offer container services, simplifying test orchestration scalability. As codebases grow more complex in modern architectures, containers provide testing consistency.
6. Automating Compliance Testing Acceleration
Applications must comply with expanding regulations covering security, data privacy, industry standards, and more. Automating compliance control validation will accelerate to keep pace.
Many developers find manually validating controls required by regulations to be cumbersome, busy work. Whereas automation can execute and validate these control checks more frequently.
Validating that applications meet expanding regulations remains challenging.
These span regional data privacy laws, industry standards, and corporate governance policies. Keeping pace manually proves untenable.
Automating compliance testing delivers:
- Controls Integration Into CI/CD Pipelines: Embed validation of security, privacy, and standards controls into code commits and test suites for instant feedback on violations early.
- Policy Enforcement: As Code Define compliance policies and controls as executable code for easy testing without manual script creation overhead.
- Automated Controls Audit Reporting Certify application policy conformance with automated reports detailing tested controls for auditors.
- Regression Testing At Scale Rerun regression test suites against code changes to identify compliance impacts with shift left benefits.
- Risk Analysis Integration Incorporate risk analysis from tools like RSA Archer into test case generation to target high-risk use cases.
Example: Traceable AI combats API abuse with digital fraud prevention capabilities
Example: CrowdStrike debuts generative AI cybersecurity chatbot and new AWS integrations
Neglecting compliance testing automation makes achieving continuous delivery with governance impossible at scale. So that’s why I think this area will expand in 2024.
7. Self-Healing Drives Higher Resiliency
Automated remediation capabilities will kick in based on policy to auto-rollback deployments or auto-scale resources when health checks fail – minimizing downtime.
Even with rigorous testing, production issues inevitably occur, requiring rapid response. Manual processes slow reaction times. Automated self-healing mechanisms will minimize application downtime.
Self-healing capabilities include:
- Automated Rollback: Auto-revert code versions or infrastructure changes automatically on detected failures according to policy to restore the working state.
- Live Container Restarts: Dynamically restart unhealthy containers or replace them with the latest images to resolve app crashes faster.
- Intelligent Request Routing: Auto-route traffic away from unstable services and scale up healthy replicas, improving uptime.
- Predictive Auto-Scaling: Forecast load spikes based on metrics to provision additional capacity in advance, optimizing responsiveness automatically.
- Graceful Degradation: Automatically disable non-critical features selectively during incidents so core app functions operate normally per defined policies.
8. AIOps Powers Software Delivery Efficiency
Using big data and machine learning, AIOps platforms analyze volumes of telemetry info and help teams quickly troubleshoot, predict, and resolve operations issues.
AIOps platforms apply big data analytics, machine learning, and AI to IT operations data streams for smarter detection, troubleshooting, and prediction of infrastructure, availability, and performance issues. Many use generative AI to help interact with all the collected data to glean insights.
Why AIOps is important in 2024:
- Anomaly Detection: Analyze metrics to automatically recognize abnormalities from normal activity indicative of emerging issues.
- Log Aggregation & Analytics Collect and correlate massive log data volumes across systems to identify related events and rapidly pinpoint root causes.
- Incident Prediction Spot trends predict future incidents like disk space exhaustion, allowing proactive prevention.
- Automated Remediation: Apply pre-defined playbooks to resolve common infrastructure issues automatically, like freeing up disk space.
- Optimized Cloud Cost: Continuously tune infrastructure sizing based on utilization to optimize cloud costs.
Leading examples include Dynatrace (Davis AI with Davis CoPilot), Splunk (Splunk AI), and Datadog (Bits AI). With infrastructures growing exponentially in scale and complexity, AIOps becomes essential for IT teams to manage reliability and costs efficiently.
For a deep dive into this area, check out our AutoOps session at Automation Guild:
- Session on AutoOps: Harnessing The Power Of AI-Augmented Testing With Generative AI
9. Increased automated mobile app functional and security testing.
The global surge of mobile applications requires more automated testing capability specifically for real-world end-user scenarios and mobile apps with validation against OWASP Mobile's Top 10 threats.
Key mobile testing focus growth areas:
- Real Device Testing: Simulating mobile gestures, rotations, and inputs requires testing mobile apps on real iOS and Android devices rather than emulators. Mobile device cloud solutions enable affordable scale.
- End-User Workflow Testing: Design automated test scenarios based on key mobile user journeys rather than technical interfaces to align with customer experience.
- Cross-Browser and OS Validation: Execute test suites across all browser and OS permutations mobile apps support for broad device and version compatibility assurance.
- Native Performance Testing: Identify resource contention, battery drain, or network latency issues through automated mobile performance tests reflecting real-world mobile constraints.
- OWASP Mobile Security Validation: Verify mobile app resilience against prevalent mobile app threats like insecure data storage, lack of binary protections, and insufficient transport layer defenses through security automation.
Addressing the unique testing needs of mobile requires a test automation approach tuned specifically for mobile idiosyncrasies.
In 2024, Testing solutions must keep pace with faster mobile release cycles. Neglecting mobile test automation threatens customer experience and security.
Follow The Money
- Tricentis Acquires Waldo. Waldo is a SaaS-based, no-code, zero-footprint mobile test automation platform.
- Mabl Mobile testing: Currently in private beta, this feature offers comprehensive and reliable automated test coverage for Android and iOS apps, enabling faster test creation and execution.
- GameDriver and Kobiton Announce Partnership to Elevate Automated Mobile Game Development and Testing
Automation Guild Sessions to help:
- Remove The Pain From Mobile Test Automation
- Mastering XCUItest: Native IOS Test Automation (90 Minute Workshop)
10. Rise of Testing in Production and non-deterministic testing:
As user volumes balloon to millions, accurately mimicking production scale in test labs proves impossible. This leads teams to conduct the final validation of new features safely through well-instrumented production testing. Sophisticated observability tooling allows deep inspection of behavior and performance.
Gone are the days of test environments with full control and deterministic order of operations. Multi-cloud, microservices, and external APIs introduce unpredictability. Test automation must evolve to handle non-deterministic systems where the sequence of module execution varies. AI techniques show early promise for smart test generation and assertion guidance for non-deterministic pathways.
Between astronomical user volumes and unstable systems, the days of fully modeled test environments are ending. Innovations in production testing observability and test automation intelligence accommodate this new world where pre-deployment validation has limitations.
Example: Grafana Labs announced that it has Asserts.ai, a technology that promises to revolutionize how users understand and interact with observability data.
Testing transforms from deterministic laboratories to conducting controlled experiments in live environments will grow in 2024.
This trend will lead to…
11. Developing for Observability
Just like you must bake testability in an app to be testable or automatable, 2024 will see the same need for Observability.
As teams increasingly test directly in production environments at scale, substantially improved observability of application and infrastructure performance becomes mandatory.
Challenges with testing in production without strong observability:
- Limited production test data visibility
- Inability to assess broader customer impact
- Slow detection of failures from new features
- Lack of production telemetry to validate fixes
By developing for observability upfront, teams enable safer production testing and release capabilities:
- Distributed tracing illuminates end-user journeys
- Fine-grained metrics quantify performance impacts
- Logs provide rich context around error conditions
- Dashboards quickly highlight regressions
Using approaches like chaos and canary testing methodologies, resilient applications designed for transparency can confidently and safely validate changes directly against real production traffic at scale.
Observability capabilities like those offered by leading vendors such as Datadog and Splunk become critical testing tools rather than operational aids. Testing in production and developing for observability evolve hand in hand to support modern release velocity and risk management.
Automation Guild Sessions to help:
- DevOps VS QA: An Interactive Story Around Observability And Synthetic Testing
- Playwright Superpowers – Load Testing & E2E
Monitoring W/ Playwright
- Beyond Testing: Integrating DevOps And BizOps For Enhanced Automation Agility
- Distributed System Observability Using OpenTelemetry
12. Playwright wins Automation Testing Crown
And so, for the most controversial trend I have picked out for 2024, number 12 is the most popular automation tool. At the end of 2024, it will be Playwright by a landslide.
I speak to a lot of testing experts on my podcast. I also predicted in 2023 that I saw Microsoft Playwright as possibly being the reigning champion of web-based automation testing tools. And I was proven correct.
And I will say upfront that 2024 will be the year of Playwright as the dominant automation testing tool for the new year.
If you use a tool like star-history.com to plot the history of likes or stars on GitHub, you can see how Playwright has drastically overtaken both Selenium and Cypress as the most widely used test automation tools.
While not a scientific or optimal way to gauge tool adoption or which one's the best, we're not saying which one is the best is just gauging interest in popularity; I do find GitHub stars to be a trackable indicator that you can least use to get a pulse for what people are trending towards.
So, for example, at the time, Playwright's recording's growing popularity was evident by its 57,000 stars on GitHub, far surpassing Selenium's 25,000 stars and Cypress's 45,000 stars. As Playwright continues its rapid ascent, multiple testing thought leaders have voiced optimism about its future dominance.
Also, many tooling vendors have started adopting Playwright into their solutions.
As Playwright continues its rapid ascent, multiple testing thought leaders I've spoken to have voiced optimism about its future dominance, for instance. Many folks have told me that Playwrights solves many pain points they've experienced with previous tools.
They rave about how the API design is top-notch in multi-browser support and has been a game changer for their test automation strategy.
Some also predict that as more teams struggle with the limitations of older tools, Playwright will become a go-to recommendation for test automation for modern web app testing. Many vendors I speak to, and many people on my show, have started thinking of Playwright first when they take it of new features to add to their solution.
If experts' predictions prove accurate, I expect Playwright to claim the test automation throne in 2024 based on its impressive momentum and ability to address common test automation challenges immediately. So, if you have yet to try Playwright, give it a whirl. Let me know your thoughts, as it will be a tool you must learn as an SDET tester in 2024.
What To Do With These Trends
One of the burning questions I always ask when discussing a subject like this is, “Will AI replace me?” Or, “Will the current recession scare looming on the horizon jeopardize my job?”
No one can truly predict how things will turn out because that’s beyond our control.
But in my 25+ years of field experience and humble opinion…
What you CAN do is make sure you’re always armed and ready with the latest info on current tech.
What you CAN do is stay up-to-date with industry know-how and relevant software testing trends.
That’s one of the reasons I create this post each year.
What you CAN do is always strive to improve your skillset and build a solid network.
That way, you’ll always be in demand regardless of how things turn out.
Because that’s EXACTLY how I survived (and even thrived) over the decades, no matter the “scare” or its consequences.
So, if you seriously want to separate yourself from the pack and gain an elusive edge in your career in 2023:
You might want to look closely at Automation Guild 2023 since I designed it to address many of the trends covered in this post.
Consider celebrating this New Year by investing in yourself to supercharge your E2E automation testing skills and get a leg up in your career, Joe.