AI tester’s helper: 9 practical applications that really save time

Artificial intelligence has already entered the IT world for good. While a few years ago it was mainly associated with chatbots and technological experiments, today it is becoming an everyday tool for programmers, analysts, and testers.

Does this mean that AI will replace testers? Definitely not. However, it can significantly increase their efficiency and allow them to focus on tasks that require experience and critical thinking.

Let’s take a look at the areas in which AI can realistically support the work of a software tester.

Generating test cases

Creating test scenarios often takes many hours, especially in the case of complex business requirements. AI can analyze User Stories, functional documentation, or acceptance criteria and generate test artifacts based on them. All you have to do is choose the right prompt for what you want to achieve:

• standard test cases,
• positive and negative scenarios,
• test checklists,
• exploratory tests (ideas, suggestions, other approaches, list of possible tests),
• edge cases.

For example, for a password reset requirement, AI can automatically suggest tests for a valid email address, a non-existent user, empty input, or an expired reset link.

Detection of gaps and ambiguities in requirements

One of the most valuable applications of AI is to support requirements analysis.

Using the corresponding prompt, AI can indicate to us:

• missing acceptance criteria,
• inconsistencies between requirements,
• ambiguous provisions,
• unhandled exception scenarios.

In practice, this allows us to come up with ready-made questions during planning or grooming meetings, so that a Business Analyst or Story Owner can describe the requirement more accurately, thus reducing the time needed for analysis and subsequent explanations.

Test automation support

Automation writing is one area where AI tools show a lot of potential.

Based on a simple description, you can generate code for the most popular test frameworks. A very convenient option here is GitHub Copilot, where we can specify specific classes that the AI can analyze to improve our code.

Thanks to this, instead of creating the test’s basic structure from scratch, the tester can focus on business logic and on verifying the solution’s correctness.

As a result, even testers who are not familiar with automation can automate at least some regression or other cyclical tests.

Logs analysis and error diagnosis

Every tester knows the situation when an application crash requires tedious reproduction of the problem or reviewing thousands of lines of logs.

AI can:

• identify exceptions and indicate false negatives,
• group related errors,
• indicate the potential cause of the problem,
• analyze dependencies between events.

As a result, the time needed to find the cause of the failure can be reduced from hours to several minutes. In this case, a tester who does not know the code has a tool in his hands that makes it easier for him to carry out such a task, or he can go to the developer with specific suspicions or RCA proposals.

Analysis of regression results

In large projects, a single regression execution can involve hundreds or thousands of tests.

AI can automatically:

• group similar failures,
• indicate the most common problems,
• analyze quality trends,
• identify the riskiest areas of the system,
• indicate the areas where the most errors have appeared so far,
• where the highest level of complexity occurs,
• prioritize the scope of regression and the size of regression tests.

But what does it actually give us? It allows us to perform a selective regression, focus on areas with high error density, and, most importantly, identify areas we might overlook during the analysis.

API and database testing

Modern applications rely on communication between services, which is why API and database testing have become key elements of the quality process. AI can help us during integration tests and data migration tests between environments.

AI supports us in:

• creating queries,
• generating JSON payloads,
• designing negative API tests (e.g., as a post script in Postman),
• identification of edge cases (by analyzing query and response).
• generating SQL queries,
• analysis of results,
• comparing data between environments,
• identification of inconsistencies and anomalies.

It can also help you prepare a set of tests that include missing required fields, incorrect data types, thresholds, or attempts to execute SQL Injection and XSS attacks.

But that’s not all, thanks to AI, we can quickly prepare test data sets, pseudo-random customer data, address database, etc., tailored to specific test requirements.

Generating test data

Creating realistic test data can be time-consuming, especially in business projects.

AI allows you to quickly generate:

• files with data in the desired formats,
• files of specific, desired sizes,
• data for test objects,
• custom data,
• regional data,
• addresses.

As a result, it takes significantly less time to prepare the test environment.

In addition, we can always create a prompt that searches for all the data we have created and deletes it after the tests are completed.

Creating test documentation

As we well know, the favorite part of a tester’s job is creating documentation…

Here, AI helps us create standard documents. Of course, it won’t do everything for us, but it can prepare the framework of the document, which we can adapt to specific needs. And so AI also generates various types of charts; you just need to describe them well in the proposal.

AI can support the creation of documents such as:

• test plan/test strategy,
• test summary reports,
• defect reports,
• documents describing RACI.

All you have to do is provide basic information, and the tool will prepare a clear document, which can then be detailed with other promotions, finally merged into a whole (also thanks to AI) and exported to an external file.

Thanks to this, even detailed documents can be created quite quickly, and the time spent on the basics can be used for additional tests…

Support exploratory testing

A good tester constantly asks himself when exploring an application:

What happens if a user does something unusual?

AI can act as an additional brainstorming partner, proposing unusual scenarios. It can indicate edge cases or specific approaches based on the analysis of user behavior.

This often leads to the discovery of errors that would be difficult to find with a traditional approach, or with testers focusing mainly on a business approach without analyzing end-user behavior.

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Will AI replace testers?

This question comes up very often. In my opinion, the answer is no.

AI is great at analyzing data, generating content, and automating repetitive tasks. However, it still lacks a complete understanding of the business context, domain experience, or intuition that often determines the detection of critical errors.

The greatest value today is achieved by testers who can use AI as an additional team member – a colleague who helps to analyze requirements faster, generate tests, diagnose problems, and prepare reports.