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Manual Testing with the help of AI tools

With the rise of artificial intelligence (AI), its applications have become widespread across the software industry. In this blog, we’ll focus on how AI can enhance efficiency, effectiveness, and innovation in manual testing processes. It’s important to remember that AI tools are not magic solutions; they work best when combined with a solid understanding of both manual and automated testing. When used correctly, AI can help deliver higher-quality products.

Let’s explore how AI can assist in various aspects of manual testing:

 

1. Streamlining Requirement Analysis

AI tools can simplify the process of analyzing software requirements by providing valuable insights and contextual information. These tools can clarify requirements, improve understanding, and generate supplementary information.

For instance, if a QA team member encounters a new or unfamiliar functionality, AI can assist in reviewing the requirements, ensuring they are well understood and testable. This results in more accurate testing and better overall quality.

2. Assisting with Test Plan Creation

AI can support QA engineers in building comprehensive test plans in several ways:

Test Case Templates: AI tools can offer test plan templates, especially helpful when engineers are unsure about test plan components or when the company lacks standardized templates.

Clarifying Components: AI tools can provide detailed explanations on test plan elements such as purpose, scope, and objectives, helping QA engineers refine their plans.

Reviewing Test Plans: AI can also review test plans, suggesting improvements or highlighting missing components to ensure completeness before sharing them with stakeholders.

3. Enhancing Test Monitoring and Control

AI tools can assist QA engineers by drafting concise status reports for stakeholders, summarizing testing progress, current status, and any challenges faced. Additionally, AI tools can analyze testing metrics, offering insights into trends, identifying potential areas for improvement, and facilitating better decision-making.

 

4. Supporting Test Case Design

During the test design phase, AI can provide structured test cases, covering both positive and negative scenarios. QA engineers can compare AI-generated test cases with their own, ensuring no use cases are overlooked. When time is limited, AI can quickly generate checklists to cover key functionalities.

 

 

5. Assisting in Writing Bug Reports

Writing clear, concise bug reports is crucial for effective communication between QA and development teams. AI can provide guidelines and templates for creating high-quality bug reports, including sections like title, steps to reproduce, expected vs. actual results, and environment details.


AI tools can also help refine existing bug reports, suggesting improvements to ensure clarity and completeness. If language is a barrier, AI can translate reports from a QA engineer’s native language into English.

 

 

6. Simplifying API Testing


AI can support QA engineers in API testing by generating requests and JSON bodies for positive and negative test cases. When errors in JSON request bodies occur, AI tools can correct these mistakes and provide explanations, helping engineers learn from the process.

 

7. Creating Test Tables


Creating test tables to organize test cases can be a time-consuming task. AI tools can simplify this by generating structured test cases and organizing them into tables, covering both positive and negative scenarios. For instance, in testing an online ticketing system, AI can help QA teams create detailed test tables for the entire purchasing process.

 

8. Assisting with SQL Queries

AI can assist QA engineers in writing and refining SQL queries, which is especially useful in database testing and data validation. AI tools can help with complex databases, suggesting corrections and improvements to ensure accurate data extraction.

 

 

9. Generating Test Data


AI tools can generate realistic test data for various testing scenarios, such as web, mobile, API, and SQL testing. For example, while testing a registration form, AI can provide different user data sets based on region and country, allowing for thorough testing of different scenarios.

In more complex scenarios, such as testing an e-commerce website’s checkout process, AI can generate product listings, user profiles, and transaction data. This data can then be used to test end-to-end functionality, including product browsing, registration, and payment processing.

For more details about such case studies, visit us at www.corecotechnologies.com and if you would like to convert this virtual conversation into a real collaboration, please write to [email protected]

Vaibhav Mevekari

Sr. Software Engineer
CoReCo Technologies Private Limited

 

Vaibhav Mevekari
Vaibhav Mevekari

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