In the last few years, especially since the first release of ChatGPT, AI has shaken many markets, products, disciplines, jobs, and many other things. Software Testing as a discipline is no exception to this.
In fact, AI in software testing is now becoming a central conversation across QA teams worldwide.
While AI was used to some extent before large language models, after the boom of large language models, using AI for anything you do is not just a trend but almost a compulsion.
But here is a thing. When a software tester says this works, we trust his/her skills, judgment, and discretion.
When an AI is going to say this works, or when AI is going to help in making things work, will you trust it? A lot of people, libraries, and tools have already started using AI working for Software Testing. But this raises serious concerns about reliability. If AI is used at multiple places, are we making that due diligence of Reliability? This blog is more of a reality check about the usage of AI in Software Testing.
Different Types of AIs Used in Software Testing
To understand why the usage of AI raises reliability concerns, let’s first understand technically what types of AI are used and what the real concerns with them are.
### 1. Classification Models in AI-Powered Testing
Classification is one of the oldest AI techniques applied to testing. These models learn patterns from labeled data. For example, classifying whether a test case is likely to pass or fail, or whether a defect is “critical” or “minor.”
#### **Use cases:**
- Predicting defect severity or priority - Automatically classifying bug reports - Identifying duplicate issues
**Reality check:** These models are only as good as the historical data. Poorly labeled or biased bug data can make them unreliable.
2. Regression Models for Predictive QA Analytics
Regression-based AI predicts numerical values rather than categories.
#### **Use cases:**
- The probability of a test failing in the next run - Time or cost estimates for test execution - Quality or reliability metrics based on trends
**Reality check:** Regression models often give a false sense of precision. Their predictions may look confident, but they may not generalize well beyond the trained data.
### 3. Clustering and Anomaly Detection in AI Test Automation
These unsupervised techniques help when there’s no labeled data. Clustering groups similar defects, logs, or test failures, while anomaly detection highlights outliers.
#### **Use cases:**
- Grouping similar test failures - Detecting anomalies in performance metrics or CI logs
**Reality check:** These methods don’t “understand” meaning. They just find statistical outliers. They’re helpful for training, not decision-making.
### 4. Natural Language Processing (NLP) for Test Automation
Before LLMs, NLP models were used to process text - test cases, requirements, bug reports.
#### **Use cases:**
- Mapping test cases to requirements - Extracting test data or parameters from documentation - Converting natural instructions to automation
**Reality check:** Traditional NLP still struggles with context and ambiguity. Without fine-tuning, it may misinterpret domain-specific language.
### 5. Reinforcement Learning in Test Optimization
This is where systems learn from actions and feedback. In testing, it’s emerging for optimizing test execution order or test data generation.
#### **Use cases:**
- Test case prioritization based on coverage or risk - Intelligent test agent that learns which paths to explore
**Reality check:** Needs a controlled environment and reward signals. Real-world testing systems are often too noisy for clean reinforcement learning.
### 6. Large Language Models (LLMs) and Generative AI in Software Testing
The newest and most transformative class. LLMs can generate, summarize, and reason.
#### **Use cases:**
- Generating test cases, code, or user scenarios from natural language - Explaining test failures or logs - Generating locators, assertions, and mocks
**Reality check:** LLMs sound confident but can be wrong. They hallucinate facts, skip edge cases, and lack situational awareness. Without human oversight, they’re risky for automation decisions.
## Why Reliability is the Deal Breaker for AI in Software Testing
In software testing, reliability is everything. The entire discipline exists to build trust; trust that a system works as expected, that defects are caught before users find them, and that every “pass” or “fail” actually means something.
When a human tester says, “This works,” we subconsciously trust not just the observation but also the judgment - the tester’s experience, context awareness, and discretion.
When an AI says, “This works,” what exactly are we trusting?
### 1. Testing is About Confidence, Not Just Accuracy
Testing doesn’t end with finding bugs; it’s about building confidence that the system behaves correctly in real-world conditions.
Even if an AI in software testing achieves 90% accuracy in classifying or predicting, that 10% gap could mean:
- A critical defect slipping into production - A false alarm wasting hours of investigation
AI predictions without confidence intervals or interpretability can erode trust faster than they add efficiency.
### 2. False Sense of Objectivity
AI outputs often come wrapped in mathematical precision, probabilities, confidence scores, and rankings, which makes them look objective. But the reliability of those numbers depends on data quality, assumptions, and context.
If the training data missed rare but high-impact scenarios, AI will repeatedly fail to catch them.
In testing, rare events are exactly what we care about most.
### 3. Accountability Gap
When a human tester misses a defect, we can retrain, learn, and improve. When an AI model misses a defect, who’s accountable?
- The developer who trained it? - The tester who trusted it? - The vendor who sold the “AI-powered testing platform”?
This accountability vacuum makes reliability not just a technical issue, it’s an ethical one.
### 4. Cascading Failures Across the Pipeline
Modern CI/CD pipelines are interconnected. If one unreliable AI component (say, a flaky locator generator or a buggy test prioritizer) produces wrong output, that error propagates downstream:
- Wrong test cases get executed - Incorrect failures trigger rollbacks - Misleading dashboards drive wrong decisions
AI unreliability doesn’t just cause one bad result. It can corrupt the entire testing signal.
### 5. Trust Once Lost, Is Hard to Regain
Testing is built on repeatable evidence. Once a team experiences an AI-driven false positive or a missed defect, it becomes hard to trust that system again. Unlike humans, AI doesn’t “earn trust” through accountability or growth. It has to prove reliability through consistent outcomes.
### The Core Truth
In testing, reliability isn’t optional.
AI can assist, accelerate, and even inspire, but if it can’t be trusted to make or support critical quality decisions consistently, it’s just another flaky test script, only smarter-looking.
## Building Sedstart with AI - But Never at the Cost of Reliability
My journey as a developer on the job actually started with Sedstart. Before this, I had spent more than 18 years as a software tester, earning all my scars from real-world experiences.
Although I had developed a lot of things in the past and written code for the same, but that was out of pure personal interest, being a nerd in this area.. So when we began building a no-code automation tool, my first reaction, coming from a hardcore programming background was, “This is going to be useless.”
But as we progressed, Sedstart kept proving me wrong. The tool became more and more powerful, and not even once did I feel that I was missing the benefits of programming inside our no-code platform.
Sedstart is my baby and like every program manager, I often get a lot of pressure and requests to add fancy AI features. Yet, my tester’s morality keeps haunting me with the same question:
>“If I add this feature, will it bring more pain or more benefit to testers in the end?”
My testing principles have stopped me multiple times during the evaluation phase of new AI ideas, purely because of reliability concerns.
Being the decision-maker, at least it’s in my hands to ensure that we add only those features that are genuinely reliable.
And here’s our guiding line:
>“If our team is able to make this feature reliable, we will add it.”
Today, Sedstart already includes several AI capabilities, not just limited to below
- Natural English prompts for driving test automation, - Failure analysis powered by contextual understanding, and - Smart naming of different building blocks in test flows.
But every one of these features was crafted with engineering excellence, added only after figuring out the most reliable way to implement it.
Because in Sedstart, we believe innovation means nothing if it can’t be trusted by the people who depend on it.
## Conclusion - The Future of AI in Software Testing Must Be Reliable
AI has already changed the way we think about software testing. From generating scripts and analyzing failures to predicting risks and understanding intent. But the real question isn’t how much AI we can add, it’s how much we can trust.
Testing has always been about earning confidence through evidence. As AI enters this space, it must live by the same rule. A feature that looks impressive in a demo but fails in production doesn’t help testers, it hurts them.
At Sedstart, our philosophy is simple:
>AI should make testers more powerful, not more doubtful.
That means every “AI-powered” capability we build has to go through the same scrutiny a tester would apply to a system under test viz validation, repeatability, and reliability.
The future of AI in testing will not be shaped by who adds the most AI, but by who adds it most responsibly.
Because in the end, testing is about trust and trust is earned through reliability, not hype.