Why top software teams are switching to AI testing

Software development has outpaced the capabilities of traditional testing methods — really.

Microservices architectures, containerized applications, and rapid deployment cycles — this is what the most terrifying nightmare of a manual QA professional sounds like. And basic automation tools simply cannot handle this effectively.

A single app update can affect hundreds of interconnected services and process millions of data points. Do we really need any more arguments that it’s time to take a shift to AI software testing?

2018. Facebook data breach. Attackers exploited vulnerabilities in the platform’s code to gain access to 50+ million user accounts. Recall how many talks there were about Facebook’s influence on the election.

This breach wasn’t due to a single flaw but a chain of issues involving the "View As" feature and video uploading functionality. Similar patterns appear across the industry. Traditional testing methods hardly can identify these compounded vulnerabilities.

Despite the 2023 State of Testing Report showing that only 14% of recipients had not automated their tests at all, not so many companies adopted AI in software test automation. At the same time, top software teams know the limitations of traditional testing and embrace software testing trends.

Let’s explore the key reasons why leading teams are switching to new-age testing and how this transformation helps them stay competitive.

The growing complexity of software development

“Software is about to stagnate”. We often hear this from futurists or those who do not work in the sphere. In fact, software is evolving, and quite fast, despite the long way it has already come. And traditional testing is becoming a roadblock. Top five of such roadblocks that manual testing introduces:

1. Insufficient test coverage

It just can’t cover the vast number of possible user interactions in modern applications. Manual testing and even scripted automation can’t feasibly test every scenario, and we end up with undetected bugs and vulnerabilities. Additionally:

• Apps now have multiple platforms and configurations

• Each feature = multiple test scenarios (often much more than expected)

• Test case maintenance becomes overwhelming (the codebase updates, so must test cases)

• Documentation can’t keep pace with changes

2. Device and browser fragmentation

General challenges turn into particularly tangible ones:

• Growing number of devices and screen sizes

• Multiple browser versions and rendering engines

• OS-specific behaviors and compatibility issues

• Need for cross-platform testing

3. Integration and data complexity increases

Modern companies rely on third-party integrations and microservices architectures to save time, money, and cover more user needs. Yet, they inevitably face a growing complexity in data and system interactions which necessitate extensive testing to ensure that all components work seamlessly together.

The decentralized nature of microservices creates hare-brained dependencies and coupled with the challenges of API versioning, they create a landscape in which you must test individual services alongside interactions between them.

Additionally, “sensitive” businesses (like medical or financial ones) must find ways to manage these complex data relationships without harming compliance with regulatory requirements.

Real-time data processing is the icing on the cake. These scenarios create additional hurdles, requiring businesses to validate data accuracy and maintain performance under varying workloads.

4. Human error

When you complete the same task bunch over and over again, chances are you will screw up at least once. Fatigue and monotony can cause testers to miss critical defects. And imagine you decided to save money by turning down additional hires and increasing the workload for current staff. Oversights will multiply, and software simply won’t work. Some engineers are sure that about 50% of defects happen due to inefficient manual testing processes.

5. Scalability issues

The chain of thoughts is simple. Roughly speaking, the project scaling = more developers + more testers + more time and effort. At the same time, widening the team size isn’t always feasible. And inability to scale testing efficiently is a bottleneck, hands down.

The need for speed and accuracy

Haystack Analytics found that 83% of software developers suffer from burnout, with 81% of respondents citing increased workload and tight deadlines as primary factors. At the same time, QA professionals entered the top 4 jobs with the highest burnout rate in the US (LinkedIn study).

Shorter release cycles (Hello, Agile! How are you, DevOps?) demand that testing keeps pace without compromising accuracy. And users expect an even smoother experience than ever before.

Integrating AI in software development is the solution. Next-gen tools automate repetitive tasks, adapt to code changes in real time, and provide rapid feedback to developers. As a result, a faster testing process, enhanced accuracy, and fewer human errors.

What is AI testing?

Let’s play an imaginary game. The traditional testing is a gardener manually watering each plant. Time-consuming a bit, if the garden is, let’s say, 100 acres. Simple automation is when we set up a sprinkler system to water the garden on a fixed schedule. Saves time but doesn’t take into account specific plant needs.

AI testing is a smart cookie that knows each plant’s needs, weather conditions, and soil content. Such a system autonomously adjusts watering schedules, analyses soil data, and optimizes water usage itself.

Artificial Intelligence testing is like a grandmaster AI that knows your every move in this chess batch:

> Learns from each game (self-learning algorithms)

> Anticipates your strategies several moves ahead (predictive analytics)

> Develops novel tactics on the fly (autonomous test case generation)

Autonomous testing is a new-age approach when we use AI and ML to automate and enhance the software testing process. Traditional one relies solely on manual efforts, while autonomous testing is named after a completely autonomous process with minimal human participation.

AI testing has

• Self-learning algorithms: They learn from previous test data, code changes, and user interactions. Then adjust and hone testing strategies.

• Ability to predict: Models analyze patterns and trends in data to predict potential defect areas, alerting teams on high-risk components.

• Test case generation: AI generates test cases dynamically, adapting to application changes in real time (No need for manual scripting).

Now recall our smart irrigation system. It understands the context of the entire garden and the particular plant and digitalizes gardening. The same way AI transforms testing by bringing intelligence and adaptability to the process.

How AI enhances testing processes

AI-driven testing tools analyze large volumes of data. This is their main feature that sets them apart. The more data, the more they have information to identify (un)conventional patterns and optimize testing. Below is a breakdown of how this works.

1. Data ingestion and preprocessing

• Advanced ETL (Extract, Transform, Load) processes come into play. AI systems ingest diverse data sources: code repositories, test logs, user interaction data, etc.

• Natural Language Processing (NLP) algorithms parse unstructured data like bug reports and user feedback.

• Then the algorithm normalizes data and vectorizes it for efficient processing by machine learning models.

2. Pattern recognition by the ML model

• Unsupervised learning: Algorithms like clustering group similar code modules or user behaviors to identify patterns without predefined labels.

• Supervised learning: AI specialists train models on labeled data (e.g., past defects) to recognize fail-led patterns.

• Convolutional Neural Networks (CNNs): These networks analyze UI elements for inconsistencies across different devices and browsers.

• Recurrent Neural Networks (RNNs): Process sequential data to identify patterns in user journeys and system behaviors.

• Anomaly detection (general): Statistical models identify deviations from normal behavior, flagging potential issues.

3. Predictive modeling

• Defect prediction models: Through ARIMA or Prophet models and based on the previous test results and codebase changes, AI predicts where defects are likely to occur.

• Risk assessment: Machine learning evaluates the impact and likelihood of potential failures, prioritizing testing efforts.

• Trend analysis: AI keeps monitoring trends and notifies human testers if failure rates in certain modules are increasing.

4. Test case generation

• Flexible test creation: The system generates new test cases with each code change. It simulates user interactions and explores different execution paths. Multi-armed bandit algorithms balance exploration (testing new scenarios) and exploitation (focusing on known problem areas).

• Input optimization: Algorithms determine the most effective input combinations to maximize code coverage and defect detection.

• Real-time adaptation: With the development, AI adjusts test cases to reflect new features or altered user flows.

5. Optimization of testing processes

• Test suite reduction: The modern app is a living organism. It never stays frozen. So does AI. It notices even minimal changes and removes test cases that we do not need anymore

• Parallel execution planning: Parallel execution is the feature that saves you the time. Based on dependencies and resource availability, it strives to reduce overall testing time.

• Resource allocation: The model also predicts workload and adjusts resource allocation accordingly.

6. Continuous learning

• Feedback: Test results and user feedback are the most significant sources for the model to learn from. Ensure you feed the model enough with production incidents to refine its predictions. This iterative process leads to progressively better testing accuracy and efficiency.

• Reinforcement learning: Algorithms improve decision-making by receiving rewards or penalties based on the outcomes of their actions during testing.

• Model retraining: Regularly updates models with fresh data to maintain accuracy and relevance in changing environments.

7. Integration with development pipelines

• CI/CD integration: Integrates with continuous integration and deployment pipelines and triggers tests automatically.

• Real-time reporting: Provides immediate feedback to developers with actionable insights. They, in turn, can resolve issues and fix problems.

• Automated issue creation: When defects are detected, AI can automatically create detailed bug reports in tracking systems, complete with reproduction steps and impact analysis.

Why top QA teams are switching to AI testing

It is faster, it is more reliable

AI-driven testing tools significantly reduce the time needed for the entire testing process. This is not to mention faster feedback and shorter development cycles. And traditional methods imply manual scripting and execution. Let’s compare.

• Test creation time: Manual testing: hours to days to write a complex test case. AI-driven tools: generate comprehensive test suites automatically in less than an hour.

• Test execution speed: Traditional regression testing might take several days to complete. AI testing tools utilize parallel processing and intelligent test selection to execute tests much faster.

• Faster feedback: AI testing is equal to instant feedback, and developers can address issues promptly. Consequently, we have a faster dev cycle and time-to-market.

Simple and rough time comparisons

It enhances accuracy

As cynical as it sounds, no people, no mistakes. Especially, when it comes to complex context and user scenarios. AI automates repetitive and intricate tasks, taking the same steps every time, which minimizes errors and reduces the likelihood of overlooked defects.

💡IBM and autonomous testing

The tech giant has integrated AI-driven testing tools and enhanced its software products’ quality. About a 30% reduction in critical defects in production environments and a 20% improvement in overall test coverage. Without variability (which is a common characteristic of human testers), the company ensured more consistent and reliable test results in critical and central areas.

It’s flexible and easily scalable

Traditional testing is like a sheet-music-bound classical ensemble. Contrarily, AI testing is a jazz band:

• It improvises monitoring the audience’s current reaction

• It adapts instantly to changing “musical landscapes” (e.g., the hall orders)

• It integrates new instruments (even if they belong to another music style)

• Performs complex compositions with minimal rehearsal

Similarly to such a jazz band, AI testing tools scale effortlessly and cover all app changes: new features, new platforms, and new user behavioral patterns.

It’s compatible with your workflow

AI testing is another part of the continuous integration and delivery (CI/CD) pipeline. Software evolves, so do testing strategies. The next big thing in testing involves:

• Real-time insights: It analyzes code changes immediately and assesses the impact of these changes on the app. This way, the team can shift their attention to more strategic moves.

• Adaptive testing: “The next big thing” holds a risk assessment and focuses on areas that are in the high-risk zone.

• Seamless integration: Next-gen testing tools like OwlityAI integrate with popular CI/CD tools (Jenkins, Travis CI, GitHub Actions, etc.). They synchronize testing efforts with the development ones.

The top two benefits of AI in QA

1. Reduced deployment risks: Continuous testing ensures that code changes are validated promptly, reducing the likelihood of defects reaching production.

2. Accelerated release cycles: With auto testing, teams can deploy more frequently (for example, the neo bank Monzo deploys 100+ times a day).

It costs less but is more effective

AI testing reduces costs. That’s it. Of course, initial investments are higher, sometimes significantly. But for long-lasting projects, it is definitely worth implementation. Size up: the same tasks are totally automated; no room for extensive manual testing and debugging, and we have:

• Labor cost reduction: In some projects, the decrease in manual efforts reaches 98+%. No longer a need for large manual QA teams. Op-ti-mi-za-tion.

• Reduced maintenance costs: Self-healing capabilities require less test script maintenance. Time savings as they are.

• Decreased defect resolution costs: Early defect detection reduces the cost associated with fixing bugs in post-release.

💡Airbnb and other property software

About 70% of property owners on Booking, Airbnb, and other platforms use AI to answer user questions. But how about AI in software test automation?

The most popular platform uses. The results:

• Savings: Estimated annual savings of over USD 1 million in testing costs due to reduced manual testing efforts and faster release cycles. This is why they plan to keep using AI in software development. Money talks.

• Efficiency gains: Reduced the time for regression testing from days to hours.

• Quality: Enhanced test coverage led to a reduction in post-release defects.

How to make the switch to AI testing

AI testing tool: Choose to your needs

Here, the essential thing is to align the tool’s capabilities with your team’s needs. It’s all about your project requirements and existing workflows. While there are many frameworks to choose from, let’s stop at the top three and their components.

1/ Technical compatibility matrix

What to check:

• Integration with the existing tech stack (is it possible and how easy it is)

• Multiple programming languages (a future-proof feature in some senses)

• Cloud and on-premise deployment options

• API and SDK flexibility

A new tool must “match” your current development stack, including CI/CD pipelines, version control systems, and a project management tool. For instance, if your projects are primarily in Python and use React for the front end, the tool should have robust support for these.

2/ Functional assessment criteria

What to check:

• AI algorithm sophistication

• Machine learning model accuracy

• Test case generation capabilities and time required for test suit generation

• Predictive analytics performance

• Cross-platform support

We suppose you are looking for tools that can reduce maintenance overhead and keep your tests relevant as the application evolves. So check the predictive insight capabilities of the new instrument to identify potential flaws before a full-fledged implementation.

3/ Organizational alignment scorecard

What to check:

• Scalability to team size

• How difficult is it to learn a new skill required to manage the new tool

• Cost-effectiveness

• Vendor support quality (consider previous clients’ recommendations and personal meetings with the vendor)

• Continuous improvement mechanisms

Recommended selection metrics:

• AI Model Accuracy: >85%

• Test Coverage: 90-95%

• False Positive Rate: <5%

• Low Integration Complexity

• ROI Potential: Positive number within 6 months

Integrating AI testing into current processes

The shortest way to maximize benefits and minimize disruptions of the new tool implementation is to ensure smooth integration. Five how-to steps:

1. Map your existing workflow

• Document everything: Outline your current QA processes, then detail. Include test planning, execution, reporting, and your defect management practices.

• Understand where to integrate: Determine where the AI testing tool will fit within your workflow, whether it’ll be after code commits, during build processes, or as part of regression testing cycles.

2. Move step by step

• Pilot-specific areas: Apply the AI tool to a particular module or component rather than the entire application. This will allow your team to learn and adjust without overwhelming changes.

• Scale once succeed: As confidence and proficiency grow, gradually expand the tool’s usage to other areas of the application.

3. APIs and plugins

• Utilize available integrations: For example, OwlityAI offers APIs and plugins for popular development tools: Jenkins for CI/CD, Jira for issue tracking, and others.

• Custom scripting: If necessary, develop custom scripts to bridge any gaps between the AI tool and your existing systems.

4. Maintain open comms

• Team collaboration: Involve all stakeholders, including developers, testers, and operations. It may seem like this move takes more time, but it actually saves time due to a comprehensive view of the process. Additionally, it fosters buy-in.

• Feedback mechanisms: Set up channels for feedback on the tool’s performance and integration experience.

5. Adjust and keep tracking

• Set clear metrics: Define key performance indicators (KPIs) such as test coverage, execution time, and defect detection rates to measure the tool’s impact.

• Continuous improvement: Regularly review these metrics and adjust configurations or processes as needed to optimize performance.

Training and support

A lifelong learning culture future-proves your company. Here’s how to build a well-prepared QA team.

Use all training opportunities

• Vendor-supported training: Ask the tool provider for training opportunities. OwlityAI, for example, provides structured onboarding sessions and tutorials tailored to different user roles.

• Hands-on workshops: Organize practical workshops where team members can experiment with the tool in a controlled environment.

Internal knowledge sharing

• Study hour and center of excellence: Create a dedicated group within your team responsible for mastering the AI tool and disseminating best practices on a particular day of the week or month.

• Regularity: Schedule periodic training updates to keep the team informed about new features or changes.

The future of software testing with AI

AI software testing is set to evolve far beyond our current expectations. Sam Altman, CEO of OpenAI, said we’ve only witnessed about one-fifth of AI’s true capabilities. The AI visionary also expects AGI, a revolutionary development in the next 5 years. So, are you ready to stay ahead with these advancements in your testing cycle?

AI testing’s transformative trajectory

Current predictive analytics:

• Limited predictive accuracy

• Human intervention is still required

Future capabilities:

• Hyper-accurate predictive modeling

• Self-improving algorithmic intelligence

• Anticipatory testing frameworks

Breakthrough indicators:

• Quantum machine learning integration

• Neuromorphic computing approaches

• Advanced probabilistic reasoning models

Natural Language Processing path

Probably, the most common problem users encounter when working with any AI model is that it doesn’t understand or misunderstand input. That’s why thousands are working on NLP advancements. In the future, AI tools will better understand requirement documents, user stories, and customer feedback.

• Accurate test case generation: Clear understanding translates into precise test cases.

• Enhanced user experience validation: Adequate comprehension of customer feedback can identify pain points and areas for improvement. Consequently, we’ll get user-centric software instead of the window, questioning our choice and offering two identical options as an answer.

• Automated compliance checks: AI will cross-reference requirements with regulatory standards, and compliance will become much easier.

AI + IoT + Edge Computing ecosystem

We are seeing a rapid development of the Internet of Things (IoT). So, AI-driven testing will further extend to a wider array of devices and platforms, adapting to test not just software applications but also the complex interactions between interconnected devices.

Key integration dimensions:

• Autonomous device interaction testing

• Complex system interdependency validation

• Predictive maintenance scenarios

• Security vulnerability assessment

Keep in mind to stay ahead

Currently, AI forced thousands of companies to lay off their staff. However, AI also stimulated rapid innovation. And advanced AI adoption is a key differentiator between industry leaders and those who merely keep pace.

While the initial investment in AI testing may seem significant, the long-term cost savings from reduced manual labor and optimized resource utilization are substantial. These savings can be redirected toward further innovation. Try to keep a proactive position, preventing risks from becoming the next headline for a costly data breach.

Bottom line

Surprisingly, the main software testing trend is not leveraging Machine Learning and even not personal model development. It is the flexibility and nonstop learning to get the most out of the benefits of AI in QA.

The future of AI software testing holds transformative potential that will redefine Quality Assurance. To catch up, you must embrace new technologies and always keep your fingers on the pulse of the industry. No one knows what breakthrough advancement comes next. But an open-minded approach to software testing (and business in general) sets your company to lead.

OwlityAI offers a bit of this evolution alongside advanced features that align with these emerging trends. Preparing now ensures that you’re equipped to harness AI’s full capabilities, keeping you competitive.