Introduction
What if an eCommerce website crashes during a flash sale, or even worse, a crowdfunding app freezes in the middle of a transaction, causing panic among the users? You’ll likely miss out on several sales opportunities and lose consumer trust.
Naturally, traditional testing or quality assurance (QA) approaches would offer little to no comfort in such scenarios, as you would have to wait for technical support or assistance. These approaches are reactive by nature—they catch issues after they’ve already impacted users, damaged reputation, and cost revenue.
However, today, the integration of AI in software testing and deployment is rewriting this narrative. It is helping organizations predict these failures days, and even weeks, in advance, and automatically prevents them by fixing the issue. This blog post will explore this shift from reactive firefighting to predictive, AI-powered software testing.
Where AI Fits in Development Workflows?
Before discussing AI’s main benefits in software testing and deployment, let’s provide a broad overview of how and where it fits into development workflows.
The graph added below highlights the key use cases of AI within a software development life cycle. The most prominent AI use cases that developers are currently relying on are writing code, searching for answers, and debugging.
Similarly, many developers express their desire to utilize AI tools for software testing, code documentation, and predictive analytics.
Source: Statista
How AI is Transforming Testing and QA?
- Simulating Realistic User Behavior Patterns to Test Scalability
Many organizations are utilizing AI tools in their software testing and deployment workflows to create user personas that accurately represent realistic usage patterns, encompassing various metrics such as session duration, number of clicks, and transactional volume. These insights can reveal subtle scalability bottlenecks that may go unseen in a traditional performance and load testing process.
- Creating Test Cases in Response to Code Changes
AI-powered software testing tools can go through huge volumes of code commits in real-time, pinpointing areas or functionalities that will be affected by new code additions or changes. As a result, they can eliminate the guesswork involved in manual test planning by creating targeted test cases to see how the software functions when particular code changes are made.
- Developing Self-Healing Tests that Adapt Automatically to UI/Feature Updates
Self-healing test cases have the ability to update their scripts automatically when some UI elements change their location, specifications, or naming conventions. So, instead of lagging or breaking down if a button is moved or renamed, the script performs equally well. And AI-powered software testing tools have greatly simplified the development of such self-healing tests by accommodating UI variations.
- Automating Deployment Post-Testing: CI/CD Pipelines
AI-based software testing has not only helped in testing and fixing bugs, but it has also streamlined deployment workflows by making intelligent, autonomous decisions about release readiness. These tools can also examine test results, code coverage metrics, previous failures & crashes, and other performance benchmarks to calculate a deployment confidence score. Based on this, they can hold back risky changes and even select optimal deployment windows.
Integrating AI in Software Testing and QA: Things to Plan for
Clearly, the technical prowess of AI in software testing is commendable. However, even then, there are a few things to be cautious about, especially when AI-powered testing is implemented for solutions that handle sensitive data (such as in a banking app) or critical functionality (like in vital monitoring wearable devices).
The Edge Cases Problem
We all know that AI-powered software testing tools are perfect for pattern recognition. But they still struggle with truly novel real-world scenarios, unanticipated edge cases, and contextual business logic. For example, an AI tool can easily test basic checkout flows for an eCommerce application, but may fail to work through a rare payment timeout set by only a few specific regional regulations.
Which is why, to account for such edge cases and subtle gaps, many organizations hire software testing experts to gain from human intuition and understanding. They move forward with a humans-in-the-loop approach, where AI-powered software testing tools automate routine testing, while human experts indulge in exploratory testing, focus on user experience validation, and train AI systems on new edge cases.
Cost-Benefit Trade-Off
Implementing AI in software testing and deployment can be a costly decision. Typically, AI-powered testing tools offer SaaS-based pricing options that range from $2,000 to $5,000 per month, and the price goes higher with the project’s complexity and the set of features and offerings you avail of. While relatively cheaper, this approach has less customization scope and somewhat restricted testing coverage.
For greater flexibility and customized test coverage, many organizations (those with the financial resources to do so) also invest in developing AI-enhanced testing frameworks. This approach has significant cost implications, ranging from US$30,000 to US$80,000 and potentially higher, depending on the scale of operations.
But, there’s an easy way out of this cost vs. benefit dilemma–outsource software testing and QA to a professional service provider. They have access to leading AI-powered software testing tools, such as Selenium, Appium, and Katalon, and have dedicated QA teams to oversee the entire process.
Cultural and Process Transformation
The integration of AI in software testing is not just a technical change–it is a fundamental enhancement that changes team dynamics and QA workflows. With AI-powered software testing tools in the picture, SDETs (Software Development Engineers in Test) must evolve from manual test executors to experts who can train AI models and interpret their results. This cultural transformation requires special training programs, revised job descriptions, and new collaboration patterns between development, QA, and DevOps teams.
The Road Ahead
This AI-driven transformation in typical software testing and QA workflows is just the beginning–automating test script generation, monitoring, and debugging. However, as AI becomes more sophisticated, we are all reaching another interesting inflection point: What will the results be when AI-powered software testing tools start testing AI-generated code? Will these tools begin ratting out their own code and predict the unpredictable?
And with AI evolving each day, this isn’t the most intriguing possibility or concern. The most widely asked question remains: In a world where AI handles testing and quality assurance tasks, what uniquely can SDETs and QA specialists offer?