App testing has entered a new era: the age of AI-driven testing solutions. For CTOs, QA leaders, and development teams, this shift brings both opportunities and challenges. As companies strive to release high-quality applications faster than ever, the pressure to implement cutting-edge testing tools is mounting.
Most industry leaders are left with these two crucial questions that are changing the testing landscape for the better.
- What exactly constitutes “AI-driven app testing”?
- Should enterprises build their own AI testing solution or invest in existing tools?
My goal is to address these questions and provide a practical framework for evaluating and implementing an AI-driven testing strategy to enhance productivity and quality assurance.
AI Driven Testing: A Brief Look
There are multiple types of AI-driven testing solutions, but the most significant potential lies in emerging tools that can automate complex, multi-step, outcome-based testing workflows. Sound familiar? Enter ‘Agentic AI’.
‘Agentic AI Testing Systems’ are the new age AI systems that enable autonomous execution of comprehensive test suites across various scenarios, offering exponentially greater productivity compared to traditional automation tools and AI-assisted test generation. These systems require little to no human intervention, enhancing the efficiency and effectiveness of QA in releasing quality updates quicker. However, these advanced systems introduce significant technical and operational complexities for enterprises.
Developing them requires expertise across the entire AI and testing stack, and there will be fierce competition to hire the rare AI testing specialists. Moreover, the cost to build these tools in-house can be prohibitively high, with estimates reaching $100M+ for end-to-end solutions. These factors essentially require enterprises to become cutting-edge AI product companies, and most will benefit more from adopting off-the-shelf solutions rather than attempting to build these capabilities internally.
What is AI-Driven App Testing?
With a ton of testing tools claiming to be AI-Driven and AI-Powered, it is difficult to gauge the level of AI/ML implementation in the testing solutions/tools. We’re here to clear the confusion and borrow this simple yet powerful framework that helps us find the needle in the haystack.
Most AI Driven Testing Tools are used for these functionalities –
- Basic Input/Output Operations (Such as providing app specifications, user stories, or existing test cases, etc.)
- Makes Decisions (Uses algorithms like machine learning models or large language models.)
- Perform Actions Autonomously (Performing complete testing life cycle of generating test cases, executing them, analyzing results, making changes and running the test again.)
Using this framework, we can identify three distinct types of AI-driven testing solutions with varying applications for enterprise productivity.
Testing Solution Type #1: Automated Test Execution for increasing coverage and reducing costs
The first type of AI-driven testing solution focuses on automated test execution. These tools, which include popular frameworks like Selenium and Appium, work by automating the execution of predefined test scripts across web and mobile interfaces.
Automated test execution gained traction in the 2010s, growing to an estimated $20B market. Common use cases include regression testing, cross-browser testing, and simulating user interactions across various devices and platforms. By automating these repetitive tests, companies can significantly increase test coverage while reducing the time and resources required for manual testing.
While powerful, these tools are limited by their reliance on predefined scripts and struggle with handling dynamic or unpredictable scenarios. They excel at executing known test cases but lack the intelligence to adapt to changes in the application or identify new test scenarios autonomously.
Testing Solution Type #2: AI-Assisted Test Generation for improving test design and coverage
The second type of AI-driven testing solution is AI-assisted test generation. These tools mostly use machine learning and natural language processing to automatically generate test cases based on application specifications, user stories, or existing manual tests. These AI-assisted test generation tools have gained popularity in recent years. While this market is still new it is expected to reach $30B by 2025.
These tools are excellent at creating comprehensive test suites, identifying edge cases, and adapting to changes in application functionality. Like Pcloudy’s Alfy (Interactive Test Assistant) which provides information about list of available devices, produces appium capabilities, etc. However, these solutions still require human oversight to ensure that the generated tests align with business requirements and handle complex scenarios that may not be easily inferred from certain conditions or inputs alone.
Testing Solution Type #3: Agentic AI Testing Systems for autonomous, outcome-based testing
The third and most advanced type of AI-driven testing solution is what we’ll call Agentic AI Testing Systems. These systems can autonomously plan, execute, and analyze comprehensive test strategies focused on achieving specific quality outcomes rather than simply running predefined test cases. This is quite similar to Pcloudy’s Copilot which is set to release soon. The new copilot will be able to provide selenium test scripts, find available browsers to test on and execute the test cases once they are ready. Building these types of AI systems takes a lot of time and effort. It is very resource intensive as the computing power needed to run these systems is a lot.
To illustrate the potential of Agentic AI Testing Systems, let’s consider an example of testing a new e-commerce mobile app. Traditional automated testing might execute a set of predefined test cases for common user flows. AI-assisted test generation might create a more comprehensive set of tests based on the app’s specifications. However, an Agentic AI Testing System would:
- Analyze the app’s purpose and target audience
- Devise a testing strategy that covers functional, performance, security, and user experience aspects
- Generate and execute test cases across multiple devices and network conditions
- Simulate various user behaviors and edge cases
- Analyze results to identify potential issues and their root causes
- Provide actionable insights and recommendations for improvement
The productivity potential of Agentic AI Testing Systems is immense. While traditional automation and AI-assisted generation might improve testing efficiency by 30-50%, Agentic AI Testing Systems could potentially reduce overall testing time and effort by 80-90% while simultaneously improving test coverage and quality insights. However, to every silver lining, there is a cloud of challenges that must be borne.
Challenges with Agentic AI Testing Systems
Agentic AI systems, while promising revolutionary advancements in automation and decision-making, face significant challenges. These include the complexity of developing robust planning and reasoning capabilities, ensuring reliable coordination between multiple AI agents, and maintaining consistency across diverse tasks and environments. There are also concerns about transparency and explainability, as the decision-making processes of these systems can be opaque.
Ethical considerations, such as ensuring the AI’s goals align with human values and preventing unintended consequences, present another layer of complexity. Additionally, the high computational requirements and data needs for training and operating these systems pose practical hurdles.
Technical Challenges
- Machine Learning Models: Multiple specialized models for test generation, execution, and analysis, or any other function.
2. Natural Language Processing: Understanding and generating human-readable test cases and reports.
3. Computer Vision: Analyzing UI elements and identifying visual defects.
4. Reinforcement Learning: Optimizing test strategies based on historical results and improving further.
5. Test Execution Engines: Interfacing with various platforms and devices across an app testing platform.
6. Data Processing: Handling large volumes of test data and results.
7. Explainable AI: Providing clear reasoning for test decisions and results.
Operational Challenges
- Expertise: Requires a multidisciplinary team of AI specialists, testing experts, and domain-specific knowledge.
- Time-to-Value: Development and implementation could take 18-24 months, assuming the right talent can be acquired.
- Cost: Initial investment in a comprehensive Agentic AI Testing System could range from $50M to $100M+.
4. Risk: Most enterprises lack the experience and resources to become cutting-edge AI product companies.
Overcoming these challenges requires interdisciplinary efforts spanning AI research, ethics, and engineering, making the development of truly effective Agentic AI a formidable but potentially transformative endeavor.
Conclusion
The potential benefits of Agentic AI Testing Systems are too substantial to ignore. With the promise of 80-90% reduction in testing time and effort, coupled with improved coverage. Agentic AI will revolutionize how we approach app testing for both mobile and web applications. While the challenges are significant, the benefits outweigh the promise of better efficiency. As the field of AI-driven testing continues to evolve, staying informed about the latest advancements and carefully evaluating potential solutions will be crucial for maintaining a competitive edge in the app quality assurance industry.
August 30, 2024
August 23, 2024
