Businesses across industries are increasingly migrating to the cloud. According to Gartner, 85% of enterprises will have a cloud-first principle by 2025, driving their digital transformation efforts.
This shift to the cloud is crucial for building a robust application ecosystem, enabling companies to innovate rapidly, scale efficiently, and respond to market demands with agility.
By leveraging the cloud, businesses are unlocking new performance levels, enhancing customer experiences, and staying competitive.
However, despite the clear advantages, underlying processes can hinder digital acceleration. One such critical process is software testing, especially when done manually.
Issues Involved in Manual Software Testing
Manual software testing, while foundational in ensuring software quality, poses several challenges that can slow down cloud transformation:
- Human Errors: Manual testing’s labor-intensive and repetitive nature can lead to burnout and mistakes, especially in tasks like regression testing.
- Limited Coverage: Manual software testing often misses certain scenarios, particularly under tight deadlines, allowing bugs to reach production.
- Scalability Issues: As businesses grow, the demand for comprehensive testing increases. Manual testing struggles to scale efficiently to meet these demands.
- High Costs: Maintaining a large team of testers and the necessary infrastructure is costly.
- Simulation Limitations: Manually simulating various scenarios is challenging, limiting the effectiveness of the testing process.
AI’s Potential in Addressing These Challenges
The National Institute of Standards and Technology (NIST) reports that software bugs impose an annual cost of $59.5 billion on the U.S. economy. AI-assisted test execution offers a solution to this issue.
Here’s how it addresses the challenges of manual testing with an outcome-focused lens:
Generate Test Cases
AI can help QA testers generate test scripts quickly and intelligently. Normally, testers with engineering expertise must develop and maintain these scripts using frameworks like Selenium.
Now, AI can create a test for a specific scenario in seconds from a plain language prompt. By clearly defining the test’s focus, scope, and boundaries, AI learns from user behavior and adapts its test generation to fit specific business needs, reviewing requirements and existing test cases to suggest relevant ones.
Data-Driven Testing
Comprehensive testing often requires a large volume of data or numerous data combinations. For example, testing an e-commerce site’s search functionality with thousands of queries.
Instead of manually creating or finding these data points, AI can quickly generate a customized dataset, preparing mock test data for almost any scenario.
Prioritize Test Cases
AI can analyze the risk factors associated with different software parts and prioritize test cases accordingly. This ensures that critical functionalities are thoroughly tested, even with limited resources.
Effortless Test Maintenance
Frequent software updates can cause test scripts to fail, requiring time-consuming maintenance. AI can simplify this by regenerating test scripts each time code changes occur. If an object’s locator changes, the AI will attempt to use another locator and continue the test, a feature known as self-healing.
Predict Defects with Ease
AI algorithms can analyze past defect data and code changes to predict potential areas of the software that are likely to contain defects. This allows testers to focus on high-risk areas, improving testing effectiveness.
Manage Test Environment
AI can dynamically manage and provision test environments, scaling resources as needed and reducing the time and effort required for test setup and configuration.
Log Analysis
AI techniques such as natural language processing (NLP) can analyze logs generated during testing to identify patterns, anomalies, and potential issues, helping testers diagnose and troubleshoot problems more efficiently.
Automated Bug Triage
AI can assist in triaging bugs by automatically categorizing and prioritizing them based on various factors such as severity, impact, and frequency. This helps streamline the bug-fixing process and reduce turnaround time.
Self-Healing
AI-powered testing frameworks can detect flaky or unstable tests and automatically rerun them or suggest fixes to improve their reliability, reducing false positives and negatives.
AI-Powered Software Testing and CloudTestr
While traditional testing often uses a hybrid approach of manual and automated methods, AI takes test automation to the next level. AI-powered test automation tools can assist testers in complex tasks requiring human reasoning, allowing them to focus on more strategic activities and improving scalability.
It uses techniques like machine learning, NLP, and computer vision to process vast amounts of data and make intelligent decisions. AI automates repetitive tasks, freeing human testers to tackle more creative and intricate aspects of testing.
Sutherland CloudTestr is a next-generation test automation platform – that enables simplified test automation, execution, and management.
It is equipped with pre-built test libraries and streamlines testing for enterprise, packaged, and custom-built applications. The auto-scaling, no-code approach reduces testing cycles from weeks to hours.
For example, Activision, a leading video game publisher, leveraged CloudTestr to cut test validation time by half and boost testing ROI by 50%. This transformation enabled them to accelerate their digital transformation efforts and deliver superior user experiences. Read the full case study here.
Final Words
AI-powered test automation is a game-changer that derives more value from cloud investments and accelerates digital transformation.
By addressing the limitations of manual testing and providing robust, scalable, and efficient solutions, AI empowers businesses to stay ahead in a competitive landscape.
Harness the power of AI for your software testing with CloudTestr to deliver high-quality applications and achieve your digital transformation goals faster.
