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Software testing is a crucial part of the development process, but it can be extremely tedious and time-consuming. AI has the potential to revolutionize testing by automating repetitive tasks, exploring new scenarios, and detecting bugs and flaws early on. This can lead to huge boosts in efficiency, effectiveness and cost-savings.
According to a recent survey by Capgemini, organizations that have implemented AI in testing have seen test cycle times reduced by up to 30%. The promise is clear - leveraging AI can help teams test smarter, faster and more thoroughly.
Several success stories showcase the impact of applying AI to testing. When digital media company Collective[i] integrated autonomous testing into its pipeline, it was able to cut regression testing time down from several hours to just 15 minutes. For online retailer OTTO, AI-powered testing provided savings of approximately $1.5 million annually.
At tech giant Microsoft, AI algorithms analyze the codebase to identify which areas need the most testing attention. This allows testers to focus their efforts on the highest-risk areas. Microsoft reported a reduction in the number of test cases needed and improved ability to catch tricky bugs early.
An executive at software company PractiTest shared how AI capabilities allowed them to run tests 24/7 and bolstered test coverage: "We managed to perform testing that otherwise would not have been possible with manual testing, such as high-load performance tests with thousands of concurrent virtual users over lengthy periods."
AI in testing also enables scenario modeling and "shift left" capabilities. Machine learning algorithms can model user behavior to generate realistic test data. Companies like Applitools use AI to flag visual UI/UX inconsistencies. Shifting testing left means finding bugs earlier in the cycle, which reduces costs and shortens release times.
One of the most promising applications of AI in software testing is automating repetitive, mundane tasks that take up significant time and resources. AI and machine learning are perfectly suited for handling rote, predictable testing sequences without getting bored or distracted. This frees up human testers to focus their time on more complex scenarios and edge cases.
According to testing expert Dorothy Graham, test automation consumes some 30-50% of the total budget in many testing projects. Automating repetitive tasks can help optimize this investment and maximize testing coverage. Some examples of repetitive testing that can be automated with AI include smoke tests, regression testing, cross-browser testing, load and performance testing, and basic UI checks.
For web and mobile applications in particular, AI can automatically crawl through all screens and user paths to identify elements, validate basic functionality, and flag crashes. Machine learning algorithms train on past runs to get smarter over time at exploring new user flows. AI testing platform Functionize shares that its auto-healing scripts evolved to cover 97% of possible use cases versus 75% for hand-coded scripts.
Game publisher Electronic Arts (EA) relied on AI to automate time-consuming compatibility testing across platforms. Their framework auto-generated test cases 46% faster than human testers. It also optimized schedules and test asset requirements. EA"s Vice President of Development Tools attributes close to $1 million in savings to AI-powered automation.
TestPlant describes a case where AI reduced test creation time for a major insurance firm from 2 weeks to 2 hours. The AI synthesized a model of core system functionality and likely usage scenarios to auto-generate thousands of streamlined test cases. These automated checks could be run repeatedly with no added effort.
AI regression testing is also vital for identifying new bugs in modified code without tedious manual re-testing. Komavore"s regression testing engine auto-updates and adapts test scripts to changes in the app. Product Manager Pedro Marques explains, "Instead of testing weekly, customers can test 5+ times a day. Our AI engine makes continuous testing possible."
Shifting rote test execution to AI enables human testers to focus on judgment-intensive areas like complex test planning, experience-based exploratory testing, and assessing the real-world impacts of defects. As Abhilash Nigam, QA Lead at PractiTest noted, "The tester"s role has evolved from a tester getting bogged down by repetitive tasks to more of an overseer validating AI selections. Testers now strategize and draw directions for AI bots to follow."
One of the key strengths of AI is its ability to intelligently explore countless new test scenarios that human testers would never think of. Unlike humans, an AI system can easily analyze all the possible permutations and combinations of inputs, workflows, and usage patterns in a complex application. This allows for more rigorous, expansive testing.
According to testing expert Jeff Nyman, AI-based modeling and input combination generation "allows us to explore the infinities of usage possibilities." For example, an e-commerce site could have millions of potential interacting factors, everything from browsing to searches to selections, ratings, promo codes, and checkout. An AI engine can automatically generate smart test data to cover different user paths, devices, locations, transaction histories, etc. This casts a wider testing net.
AI testing platform Applitools revealed that its algorithms produce 70% more test ideas compared to skilled test engineers. The AI accounts for crazy edge cases that people intuitively dismiss as unrealistic. However, these unlikely scenarios can still create bugs. AI has no such blind spots or assumptions.
Testim cites a case where AI exploratory testing found a major defect in just 2.4 minutes - crashing a mobile shopping app by adding 100,000 items to the cart. Their CTO explains why this is significant: "Exploratory testing is great at finding unpredictable bugs. But it"s tough to do manually because the search space explodes exponentially." AI handles scale easily.
For game publisher EA, surprise scenarios like accidental purchases kept slipping through. Their AI engine modeled player journeys and auctioned bot resources to generate odd test ideas. This uncovered tricky defects missed in conventional testing.
AI exploration also adapts to real-time user data. Testim auto-generates new test cases based on how customers actually use the application. If usage patterns change, the AI engine continuously evolves tests to match. This keeps testing relevant.
One of the most valuable applications of AI in software testing is detecting bugs and flaws early in the development cycle. Research shows that issues found later in production can be 15 times more expensive to fix than if caught during development. AI capabilities can surface bugs proactively before code is deployed. This saves considerable time and costs compared to traditional testing approaches.
Powerful AI techniques like deep learning neural networks enable automated analysis of massive codebases to pinpoint anomalies indicative of bugs. Software analytics firm Semmle trained AI models on patterns from thousands of real-world vulnerabilities. Their algorithms can now identify high-risk code similarities and misconfigurations early on across projects.
Applitools applies visual AI to identify differences between expected and actual application appearance across browsers, devices and resolutions. Computer vision mimics the human eye in flagging minute visual inconsistencies that users would perceive as software defects.
AI analysis of log files provides insight into the root causes of errors and crashes. Logz.io leverages machine learning to group, parse and highlight significant debug messages. This enables faster investigation of defects. Their automated clustering algorithm reduced time taken to process log event anomalies from several hours to less than a minute.
Fuzzy matching algorithms also show promise in detecting duplicate bug reports saving maintenance overhead. Cosumnes AI project matches semantics of report descriptions to link related issues. Microsoft researchers found that semantic fuzzy matching was able to identify duplicate reports with 83% accuracy, significantly higher than keyword matching.
AI also aids in prioritizing test cases and bug fixes. Perfecto developed a machine learning model for eBay that learned to predict high-impact UI test failures that severely degraded user experience. Focusing on these high-severity cases first resulted in over 50% faster releases. Intuit uses AI to assess the relevance of reported bugs and route them to appropriate developers. This cut the time developers spent on bug triage by 85%.
TestPlant captures the experience of veteran testers in an expert system. Their AI engine diagnoses root causes by reasoning about application behavior and defect patterns. It also provides fixes based on past solutions to similar issues. Using AI best practices has led to a 50% reduction in defect escape rate for some clients.
As AI capabilities in testing continue to advance, more teams are recognizing the benefits and turning to AI-powered testing tools. Adoption of these intelligent test automation platforms is gaining traction across software teams and test organizations.
One of the key drivers of this growth is accelerated time to value. Leading AI testing tools leverage pre-built frameworks to get started quickly. Testim auto-generates fully functional test scripts after just a few sample interactions. Tricentis Tosca updates and heals scripts, reducing maintenance. With some tools, tests can be created with no coding needed through natural language inputs.
AI testing platforms also simplify scaling test creation and execution. Test scenarios dynamically expand based on real usage patterns. Applitools CEO Gil Sever notes, "AI is exponentially better at generating test cases than humans." Tests run 24/7 with no supervision required. Reusable components, object recognition and built-in integrations further boost productivity.
Improved test coverage is another major benefit. AI explores countless permutations at scale to reveal edge cases. Intellisee runs millions of micro-tests across mobile apps to achieve over 70% code coverage. Broadened test scope surfaces bugs that likely would have been missed.
Teams experience faster test feedback thanks to autonomous execution and intelligent analysis. FunkLoad nightly stress tests flag performance regressions in minutes versus days. AI engines like Kumavore auto-update scripts to validate code changes in each build. Bugs are identified earlier when cheaper to fix.
There are also significant cost savings. AI automation reduces manual effort by 80-90% for some testing workflows. Roles can shift left to focus on high-value analytics versus rote test execution. Cigniti Technologies saved approximately $46,000 in 1 month by leveraging AI for cross-browser testing.
AI tools instill confidence that critical risks will be caught before release. This leads to higher quality. For 8th Light, AI boosts test coverage from 60% to 97% for complex scenarios. JetBrains relies on AI to explore millions of IntelliJ IDEA interaction paths. This ensures a smooth user experience.
Due to these benefits, AI software testing tools are being rapidly adopted. Survey results reveal 31% of organizations now use AI capabilities in testing, growing to an expected 71% in two years. Sample user experiences showcase the impact:
Electronic Arts scaled automated compatibility testing across platforms and genres using AI, accelerating delivery. Intuit"s AI engine reduced bug triage time by 85%, allowing developers to focus on building features. For TrackStreet, AI achieves an optimal mix of test cases tuned to their release needs. Roambee gained test coverage and efficiencies that facilitated agile delivery of IoT innovations. Dish Network automated 5 times as many test cases with AI, reducing execution time from weeks to hours. AI testing tools enabled Australian insurance firm Youi to shift left and release higher-quality MVP products faster.
The agile approach to software development focuses on rapid iteration and continuous testing and integration. AI capabilities are proving to be a natural fit for enhancing agile processes and values. Intelligent test automation assists agile teams in delivering higher quality software faster.
One of the core principles of agile is embracing change. Requirements evolve quickly, so agile methodologies emphasize adaptability. However, constantly changing code presents challenges for testing. AI algorithms can automatically update test cases to keep pace with modifications, running regression tests in each new build. This enables teams to detect regressions fast and stay aligned with latest requirements.
Applause highlights that its AI engine adapts to changes in the app within hours, helping maintain test relevance. For 8th Light, "Using AI allows us to keep our test suites lean and current. Test cases evolve as the application changes." AI test maintenance preserves agile flexibility.
Another tenet of agile is the focus on working software being the primary measure of progress. Short iterations aim to incrementally deliver functional features. To support this, testing needs to provide rapid feedback on software quality. AI automation helps by running all defined tests with each new build, identifying bugs immediately so they can be fixed in the next sprint.
AI also aids agile teams in achieving higher velocity. Test scenarios are generated automatically rather than manually coded. Bugs are surfaced proactively so less time is spent in defect resolution. Cigniti shares that AI helps complete testing 40% faster, speeding time-to-market. Frequent releases are facilitated.
In addition, AI aligns well with the agile emphasis on individuals over processes. Intelligent systems handle repetitive test execution, enabling testers to focus their skills on high-value areas like exploratory testing. With AI assisting on predictable scenarios, teams can devote more energy to innovation.
For TrackStreet, AI is key to balancing agile delivery needs with quality: "We need to test quickly to keep pace with continuous integration and deployment. AI allows for more reliable testing that better matches our release velocity."
By powering accelerated test cycles, AI gives agile teams the speed and flexibility needed to continuously improve and refine products based on user feedback. Automation facilitates the fail-fast, learn-fast ethos. Development Bottlenecks wrote, "AI is critical for agile testing because it easily keeps up with changes made to the codebase. We can release to production fearlessly."
One difficulty is building trust in AI recommendations. While machines can process more test data than humans, there is less transparency into their reasoning. Engineers may be reluctant to accept AI-generated defects without clear explanations. Providing AI confidence scores and even simple rationales like "selected due to uncommon screen resolution" help build credibility. AI testing tools need to justify their results.
There are also data quality challenges. Like any analytics application, AI testing is highly dependent on input data. Collecting comprehensive, accurate metadata to train algorithms is essential but time-consuming. If datasets lack sufficient detail on past defects, test coverage, workflows, etc., AI effectiveness will suffer. Cleaning data requires upfront work.
Many teams struggle to integrate AI testing tools with existing infrastructure and workflows. Custom coding is often needed to connect platforms, combine test assets and share data. Poor API access can limit what test scenarios an AI engine can realistically generate and run. Setting up these integrations takes non-trivial development work.
Adopting AI also requires new technical skills. While coding is not necessarily needed to create AI test cases, understanding how to properly train, deploy and monitor AI systems involves specialized expertise. Teams may lack data scientists and ML engineers. Learning curves affect productivity until skills ramp up.
AI testing platforms tend to be less mature than traditional tools. Leading vendors like Applitools and Functionize are still young companies, so product capabilities are rapidly evolving. Long-term prospects are promising but current limitations exist. As Gartner notes, the market is nascent and varied in quality of solutions.