A decade ago, automated testing emerged as a fixture in the software testing landscape. The Agile methodology had become the undisputed priority for testing teams, just as CI/CD pipelines had for continuous testing and feedback.
At the time, implementing automation testing was considered a risky, complicated, and expensive decision. However, its efficacy was impossible to ignore, and it soon came to dominate QA processes across teams.
Cut to 2025, and AI has become a similarly emergent technology. Its advantages are quickly becoming apparent in every domain. However, just as with automated testing a decade ago, many CTOs are unsure about the new technology and its viability in driving business value in both the short and long term.
CTOs hold a key role in innovating and helping test teams thrive in a competitive future. Here’s why they should prioritize AI in that transformation.
Challenges in modern automation testing
The easiest way to identify the necessity of AI is to explore the gaps it can fill and the pain points it can address. Modern automation testing provides great value in terms of saving time and resources for QA teams. But as software products become more complex and user expectations become more nuanced, flaws in existing automation frameworks become apparent.
Script Maintenance
As an application UI is developed and features are added, automation scripts break. Scripts written for testing Version 1 do not cover any other versions, updates, and modifications in business logic.
Since these scripts are written in complex programming languages, modifying them requires technical expertise, time, and resources. This adds up to extra-budgetary demands for each test cycle.
Frequent false positives and flaky tests
Minor inconsistencies in test scripts often lead to false positives, which eat up excess debugging effort and delay release dates. This is also compounded with flaky tests that pass or fail inconsistently, confusing testers on the app’s true quality.
These issues disrupt CI/CD pipelines, increase costs, and delay deployments
Inconsistent Test Coverage
It is difficult for human testers to properly analyze vast swathes of historical data and identify the tests that would provide maximum coverage. Often, testers attempt to maximize coverage, which can lead to redundant tests. Sometimes, it can go the other way and result in insufficient test coverage and testing.
Of course, it is also draining for human testers to create scripts manually for a large number of tests, which further reduces the coverage ratio.
Unreliable risk coverage
Not all test cases are equally essential, and not all app features carry equal business value. For instance, the payment feature of any app needs to be tested and verified for perfect functionality. However, lesser features like non-essential visual elements don’t require the same effort. Critical paths and high-risk need more comprehensive and frequent testing.
However, human testers simply do not have the time and mental bandwidth to manually study vast records of customer behavior, past test reports, and project data. They can evaluate risk to some extent, but are constrained by time and fatigue. This leaves room for defects to reach production.
Slow execution
Often, automated pipelines are slow to execute due to several bottlenecks. For tests, these bottlenecks can vary from too many test cases to run, unoptimized test execution strategy, flaky tests, to insufficient parallel testing support.
It is difficult for human testers to create automation scripts and also refine processes for minimal obstructions. It requires a large team with technical expertise, which is expensive to hire and maintain for most organizations.
Gaps in security testing
Traditional automation testing pipelines cannot always adequately detect security vulnerabilities. Generally, these tests rely on rule-based scanning tools that aren’t updated to detect evolving threats. Static scanning tools cannot adapt to zero-day vulnerabilities, for instance. These tools also cannot analyze the app’s behavioral patterns of the application, which makes it difficult to identify manual attacks.
These security scanners also tend to produce false positives, which manual testers have to spend time verifying. These tests are resource-intensive and occur at the end of the test cycle, so any defects cause further delays in product release. On top of that, penetration tests and manual security audits are prohibitively expensive.
The AI ROI: What AI-powered testing brings to the CTO’s table
When considering the pros and cons of adopting AI-based testing, CTOs can expect these engines to deliver a range of technical and business benefits. These benefits don’t just drive long-term success; they transform the test pipeline fundamentally for efficiency, accuracy, and industry-best product quality.
Self-healing test scripts
AI-based testing tools use a combination of machine learning, self-healing capabilities, and intelligent object recognition to manage test scripts for maximum reliability.
Tools like CoTester can automatically detect changes in the UI and update test scripts to match these changes. AI can find stable attributes and dynamically adjust the scripts without manual intervention. It can refactor scripts to eliminate duplicate or redundant steps.
If the right data is available, AI engines can examine thousands of test cases to suggest structural improvements.
These tools can analyze multiple attributes to find the right UI elements. It can monitor locators and update scripts using the most stable locators.
AI can predict which test scripts are prone to breaking based on historical test failures, and proactively update these scripts before they break. This slashes down test maintenance efforts by a notable margin.
Managing false positives and flakiness
AI tools can use machine learning, historical failure analysis and smart execution protocols to reduce test flakiness and false positives.
By analyzing test logs and execution history, AI tools can accurately find the cause of test failures — even environmental causes like network delays, CPU loads, and memory leaks.
AI can identify flaky tests from execution histories, and run verification steps on all tests already marked as flaky. These engines can adjust test execution based on network conditions, UI rendering speed, and infra load to increase test stability.
Improved test coverage and prioritization
AI tools can analyze code changes, test history and defect reports to find untested features, elements and user flows. It can analyze test gaps and highlight missing test cases in high-risk areas.
In case tests are missing, AI can generate new test cases based on production logs, user behavior and defects patterns. To identify the most critical test cases, AI engines examine code changes, importance of features, historical defect data and risk histories.
AI-enabled tools can identify features most likely to have defects and prioritize test cases covering them. It will note all UI changes automatically, and adapt tests for maximum efficiency.
Improved test execution
AI-based testing tools can analyze past test results and eliminate outdated or low-impact tests. ML models can predict which test cases will likely fail recent code changes — and prioritize critical test cases that best serve to maximize code coverage and risk assessment.
AI engines can deploy tests across multiple environments and virtual machines to enable parallel testing.
Additionally, AI-powered protocols can simulate real-world user activity without having to build static load tests from scratch. This includes predicting performance bottlenecks before they emerge in production and building tests to mitigate them.
Enhanced security scanning
AI-driven testing can continuously analyze app behavior in real time to identify security gaps often missed by static tools. By accounting for historical security data and code patterns, AI engines can predict which features/modules/workflows are most likely to suffer from inadequate security — and focus increased tests there.
These tools monitor network traffic, API calls, and system behavior to detect threats as they emerge. Some tools can even analyze live app responses to flag zero-day attacks.
They can simulate real-world attacks for penetration tests, and even anonymize personally identifiable information in test environments when checking the app’s compliance with GDPR, HIPAA and other privacy laws.
Source: This blog was originally published at testgrid.io
Top comments (0)