Becoming a Smart Manual Tester: Harnessing AI Tools for Enhanced Testing

Now a days software development landscape is moving forward in pace that we couldn’t even have imagined few years ago. Main reason for this fast movement is introduction of AI in various aspect of development. In testing paradigm also AI has been impacted that, AI tools are capable of automating many aspects of testing. But it is still a fact that manual testing will continue to play a crucial role in ensuring quality of software under test. At the same time as a manual tester we should not be left out with old fashioned testing methodologies we should be embracing Artificial Intelligence (AI) tools to augment our skills, improve testing efficiency, and deliver high-quality software products. AI can be incorporated in different aspects of testing, this article is mainly focused on manual testing point of view. 

Understanding the Role of AI in Manual Testing

We should not try to bring AI in each and every steps of testing we should first understand where all can AI help a manual tester. Using AI driven tools testers can create test cases, prioritize the test cases efficiently and improve the test coverage, so that the actual testing effort can be concentrated on critical part of application. Testers can also use AI tools for usability testing, log analysis and to perfor root cause identification. If we understand the integration of AI in manual testing, it will help us to streamline processes, raise productivity levels and enable developers to design software that is both reliable and user-centered.

Learn how to be a smart tester using AI tools through these steps:

Get to know AI concepts:

First of all start by gaining an understanding of the concepts, methodologies and tools for AI which are used in manual testing to become a smart tester. Here’s what you can do

• Look up tutorials, articles, or any available documentations about relevant AI principles with respect to testing online.
• Take an online course or certification training that covers Basics of Artificial Intelligence (AI), fundamentals of machine learning (ML) and AI applications in software testing.
• Read books on artificial intelligence in manual testing ,machine learning algorithms and ai-driven test tools
• Sign up for newsletters or subscribe on social media platforms such as LinkedIn where there are many discussions about the impacts of ai on testing
• Join groups at LinkedIn and find experts from the field who really work with ai-registration based companies
• Visit webinars which deal with ai as applied to manual testing

Identify Testing Challenges:

Second steps is to Identify common testing challenges and pain points in your projects that can be addressed using AI tools. Usually they are categorized under test case prioritization, test data generation, defect prediction, and usability testing.

• Test Case Prioritization:
  • For effective prioritization of test cases, utilize AI tools. A good AI tool can help you to identify critical tests by analyzing different factors like code changes, impact analysis and risk assessment erc. Ofcourse we need to provide these factors to get the prioritization.  They can also prioritize testing efforts and resources by selecting test cases that cover high-risk functional areas, frequently used features and critical user journeys. All this can be done in a quick turn around time which would have taken hours for us to do manually.
• Test Data Generation:
  • One challenge a manual tester faces in this real world is to get the client specific realistic data. How many of the times we had come down to root cause of a bug as data issue, many of you can relate it. Using AI tool we can generate realistic test data set. Automated test data generation should be carried out based on predefined scenarios, boundary conditions as well as data dependencies to ensure full coverage of tests. Find out the AI-powered testing platforms that offer some capabilities like data anonymization, masking and synthetic data generation for efficient secure test data management.
• Defect Prediction:
  • We can also use AI for defect prediction. Using ML algorithms for anomaly deletection and risk assessment, we would be able to predict the possibility of defect. If we get to know there would be a possibility of defect in particular area we could provide more focus and avoid missing issue and proactively improve the quality of releases.
• Usability Testing:
  • Manual tester can utilize AI tools to improve the usability testing by automating different aspects of the process. We can analyze user interfaces, navigation flows, and design elements more efficiently and effectively using AI/ML algorithms. A standard example for this is  – computer vision algorithms can automatically analyze and evaluate visual elements such as buttons, menus, and layouts in the application UI and let us know if there is any usability issues. It is just matter of getting used to these tools once you are over it and the results would be quicker than when you perform it manually.

Explore AI-Driven Testing Tools:

Explore and evaluate AI-driven testing tools and platforms that align with your testing requirements. Earlier we mentioned we can use AI tool to prioritize the test cases, we can even use AI tools to write test cases, to prepare test documentation etc.

• Test case creation
  • Consider a case where you need to write test case for simple requirement like – drinking water cup. Just type this requirement in the chatGPT or other AI tool and look what you get. Within a few second you would be able to create 20 tests that too in different category which we might not even thought off. It will give minimum 3 to 4 test cases under different category like Basic Functionality, Material and Durability, Design and Ergonomics, Safety and Hygiene, Capacity and Measurement, Usability and Accessibility, Packaging and Labeling, Regulatory Compliance etc

Learn AI Integration Techniques:

Now that you have understood AI concept, identified testing challenges where AI can help and explored the tools, next step is to learn to integrate AI techniques. Learn how to integrate the explored AI tools into your manual testing processes and workflows. You may need to upskill your scripting capability, which will add value to your profile, to integrate the tools. 

Develop AI-Enhanced Testing Strategies:

So now you have learned concepts of AI, challenges that can be addressed by AI, explored the tools and integrated it with testing, you need to develop AI enhanced testing strategies. Actually this should be done as first step, since this is the first time it can be done after performing above activities. This is same as we develop normal test strategy but only thing is we are considering the usage of AI tools which will augment our testing. 

It should have clear testing objective, goals, success criteria as usual. Additionally, we need to provide the areas where we can utilize the AI tools for example, test case creation, prioritization, or usability testing etc. Mention the tools that has to be explored and added to add value to areas mentioned above. Develop test plans that incorporate AI-enhanced testing strategies, methodologies, and techniques.

Once above all steps are performed no one can stop you from becoming a smart tester who embraces the AI tools to complete the work efficiently and effectively and more importantly in a quick turn around time. In this process you would also enhance your technical skills, domain knowledge, and strategic thinking. One thing to remember is AI workspace is changing dramatically each and everyday so don’t stop once you had finalized the strategy. Keep exploring, experimenting, and learning to unlock the new potential of AI in manual testing and elevate your testing career to new heights.