Manual testing is the most fundamental approach to software testing. Simply opening a website and clicking a button to see if it works already counts as manual testing.
In this comprehensive guide, we'll explore what manual testing is, its types, benefits, and challenges, how it compares to automation testing, and how AI is reshaping, not replacing, the manual testing process in 2026.
Manual testing is a testing approach where testers manually interact with software or applications to check their quality without the help of automated testing tools or test scripts.
Manual testers interact with the system the way an end user would, aiming to identify bugs and usability issues that can create friction in the user experience.
For large-scale testing projects with thousands of test cases and features to verify, QA teams usually automate their work to boost efficiency. However, manual testing is still needed for scenarios that are difficult or impossible to automate, and according to the State of Software Quality Report 2025, 82% of testers still use manual testing in their daily work.
Any type of testing can be executed both manually and with automation tools. The decision on which approach to use depends on the nature of each specific test case.
However, there are some testing types that usually can't be automated and must be done manually. They are:
To be human is to be creative and intuitive, and in certain types of testing those 2 factors are a must-have.
Manual testing allows testers to ask questions like "What if I do it this way?", and this liberty of experimentation is something automation testing just won't be able to deliver.
Remember: automation testing only follows a predetermined set of test steps. It can only do what we script it to do. Meanwhile, manual testing allows testers to do whatever they want, including breaking the app, which usually reveals bugs nobody knows to have existed in the first place.
Let's take a look at this Venn diagram to better understand manual testing:
We start with the Unknown Known, meaning we only think a feature might be broken.
After testing, we reach the Known Known. We now know for sure whether the feature works or not. This is as far as automation can go because it only follows predefined steps.
Manual testing goes further by encouraging curiosity. A tester might ask, "What happens if I click this button 100 times?" By experimenting in unexpected ways, they can uncover Unknown Unknowns - bugs that no one knew existed until someone tried something unusual.
If you want to witness the power (and joy) of manual testing, check out the Let's Game It Out channel. The man behind the channel plays games, but he always interacts with them in the most boundary-breaking ways to uncover bugs:
Manual testing is flexible. If the requirements change, testers can quickly adjust their approach without needing to update scripts or code. This makes it easier to test new or modified features on the spot.
To do manual testing is to experience the application in the same way an end user would, and almost anyone can become an end user.
Of course, manual testers must have enough technical knowledge and experience to know how to best "break" the app, but other than that they don't need programming skills or engineering expertise to create test scripts like an automation tester. The low learning curve of this approach allows teams to start testing quickly with limited resources.
One of the biggest challenges for teams adopting automation testing is the ongoing maintenance effort it requires. When code changes, test scripts often need to be updated as well, otherwise they quickly become outdated and unreliable.
That is not an issue with manual testing, since each test execution is a new execution.
Manual testing requires testers to execute each and every step manually. It is fine for small projects, but for those with hundreds to thousands of test cases, manual testing is just counter-productive.
Moreover, humans cannot process data as fast as machines, and we are prone to fatigue and distraction. If a mistake occurs, testers must repeat steps, further extending the testing time.
Each manual test execution is independent. A new execution requires testers to repeat all steps from the beginning. This leads to:
While manual testing does not require specialized tools or scripting, the long-term cost can be high due to the need for more testers, longer testing cycles, and repeated effort for the same test cases. Companies often underestimate these costs when scaling their testing process.
📚 Read More: How To Calculate Test Automation ROI?
Manual testing takes longer to identify and report defects. Since execution is slower, bugs may be discovered late in the development cycle, increasing the effort required for debugging and fixing them. This delay can impact release schedules and software quality.
Manual testers follow the Software Testing Life Cycle (STLC) to systematically plan and execute their tests.
📚 Read More: How to execute tests?
Automation testing involves executing test cases automatically through reusable test scripts or with the help of automation testing tools. It is the essential counterpart of manual testing.
As of 2026, both manual testing and automation testing have clear strengths and weaknesses. In practice, a hybrid approach often yields the best results, with manual testing handling exploratory and usability aspects while automation testing covers repetitive and resource-intensive tasks.
You can have a look at the table below to better understand the differences between the 2 approaches:
| Aspect | Manual Testing | Automation Testing |
|---|---|---|
| TESTER INVOLVEMENT | Human testers execute test cases manually. | Automated tools execute test cases automatically. |
| TEST CASE EXECUTION | Test cases executed manually without tools. | Test cases executed by automation scripts. |
| SUITABLE FOR | Exploratory testing, usability testing, ad-hoc testing, small projects. | Regression testing, performance testing, large-scale testing, repetitive tasks. |
| HUMAN INTERVENTION | Requires human intervention for each test case. | Limited human intervention after script setup. |
| ERROR PRONE | Prone to human errors and subjectivity. | Reduces human errors, increases repeatability. |
| PROJECT SCALE | Suitable for small projects or when test cases change frequently. | Suitable for large and complex projects with consistent requirements. |
| EFFICIENCY | Time-consuming for repetitive or large-scale testing. | Efficient for repetitive tasks and regression testing. |
| INITIAL SETUP | Simpler initial setup; doesn't require scripting skills. | Initial setup requires scripting skills and may take longer. |
| ADAPTABILITY | Offers more flexibility in adapting to changes. | Less adaptable to changes in the application. |
| SPEED OF EXECUTION | Slower compared to automated testing. | Faster test execution once scripts are set up. |
📚 Read More: Manual Testing vs Automation Testing
Based on the nature of manual testing, here are the scenarios it is best suited for:
Many testers fear the rise of automation and AI, saying that it is going to replace them. However, that is far from the truth. Check out what Christiano Caetano, Katalon's Senior VP of Product Management, thinks about this topic:
The rise of AI-powered testing tools and autonomous test agents has led many to question the future of manual testing. However, the data paints a more nuanced picture.
Part of what's driving that question is the sheer amount of code AI is now writing. AI-generated code carries roughly 1.7 times more issues per pull request than human-written code, according to CodeRabbit's 2025 State of AI vs. Human Code Generation report, and GitClear's 2025 analysis of over 150 million lines of code found that code churn, meaning code written and then reverted or replaced within two weeks, has risen sharply in AI-assisted codebases.
More code moving faster does not mean less testing is needed. It means the testing that happens has to be judged against something automation cannot supply on its own: whether the output actually reflects how the product is meant to work for a real user.
The World Quality Report 2025 shows that 58% of enterprises are upskilling QA teams in AI tools rather than eliminating manual testers. And according to the State of Software Quality Report 2025, 72% of QA professionals actively leverage AI for test generation and script optimization, while still relying on manual testing for daily work.
📚 Read more: No-Code Test Automation with AI: A Guide for Non-Technical Teams
The bottom line: manual testers are evolving into quality engineers who guide AI systems, validate AI-generated outputs, and focus on high-value activities like exploratory testing, risk analysis, and user experience evaluation.
That validation role runs on domain knowledge AI does not have: what a feature is actually supposed to do for a real user, which edge cases matter to your business and which do not, and whether a result that looks correct actually is. As AI writes more of the code, that judgment call does not become less necessary. It becomes the thing that is hardest to automate and most valuable to have. The skill set is shifting, and it is also changing what the job gets called. See why "manual tester" means two different jobs.
📚 Read more: How to Test AI Applications Manually
💡 For a fuller data picture on which testing tasks AI is and is not replacing, see Will AI Replace Manual Testers? Katalon's Data Says the Story Is More Complicated. And for the testing-workflow side of the AI-generated-code problem specifically, see Why AI-Generated Code Needs AI-Powered Testing.
Rather than replacing manual testers, AI is acting as a force multiplier for their work. Here are the key ways AI is augmenting manual testing workflows today:
AI-powered test case generation: Tools can now read requirements written in plain language and generate comprehensive manual test cases. Katalon's own GPT-powered test case generator integrates with JIRA, reads ticket descriptions, and outputs tailored test cases automatically, saving hours of manual preparation.
Intelligent bug detection: AI analyzes logs, UI behavior, and user interaction patterns to surface anomalies that manual testers can then investigate further. This helps testers focus on confirming and reproducing the most critical issues rather than hunting for them.
Risk-based test prioritization: AI recommends which areas to test first based on code changes, historical failure patterns, and business impact, helping manual testers allocate their limited time where it matters most.
AI-assisted test reporting: Natural language processing tools can generate human-readable reports from raw test logs, saving testers hours of documentation work. This means more time testing and less time writing reports.
Self-healing test maintenance: While primarily used in automation, self-healing capabilities also benefit manual testers by keeping automated regression suites stable, freeing up manual testers to focus on exploratory and usability work instead of re-running broken automated scripts.
💡 To get good output from any of these tools starts with how you prompt them, see our guide to prompt engineering for testers.
The global automation testing market is projected to grow from $19.97 billion in 2025 to $51.36 billion by 2031 (Research and Markets). Yet industry data confirms that only about 5% of companies are fully automated, most still use a hybrid manual-to-automation ratio of 50:50 or 75:25 (VirtuosoQA). This underscores that manual testing expertise remains a foundational part of any quality strategy.
To ensure that the manual testing process goes smoothly, the QA team should have solid test management practices in place. Google Sheets has a default template for test management that you can use for reference.
Why this sheet works:
Note: this spreadsheet is only suitable for small-scale testing projects. For large-scale testing projects, it is recommended to have a dedicated test case management system.
Popular test case management tools include:
Katalon True Platform supports the testing lifecycle across planning, test authoring, execution, and analysis. For manual workflows, teams can organize test cases and suites, create manual test runs, capture step-level results and evidence, report defects, and review run results in one platform.
Depending on plan and configuration, teams can also use AI-assisted features to generate test steps or run selected manual tests with AI.
Where configured, Jira and Azure DevOps integrations can connect requirements, defects, and results to external ALM workflows. Exact behavior varies by provider and configuration.