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Manual testing
Manual testing
Manual testing
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Compare with Test automation.

Manual testing is the process of manually testing software for defects. It requires a tester to play the role of an end user where by they use most of the application's features to ensure correct behaviour. To guarantee completeness of testing, the tester often follows a written test plan that leads them through a set of important test cases.

Overview

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A key step in the process is testing the software for correct behavior prior to release to end users.

For small scale engineering efforts (including prototypes), ad hoc testing may be sufficient. With this informal approach, the tester does not follow any rigorous testing procedure and simply performs testing without planning or documentation. Conversely, exploratory testing, which involves simultaneous learning, test design and test execution, explores the user interface of the application using as many of its features as possible, using information gained in prior tests to intuitively derive additional tests. The success of exploratory manual testing relies heavily on the domain expertise of the tester, because a lack of knowledge will lead to incompleteness in testing. One of the key advantages of an informal approach is to gain an intuitive insight to how it feels to use the application.

Large scale engineering projects that rely on manual software testing follow a more rigorous methodology in order to maximize the number of defects that can be found. A systematic approach focuses on predetermined test cases and generally involves the following steps.[1]

  1. Choose a high level test plan where a general methodology is chosen, and resources such as people, computers, and software licenses are identified and acquired.
  2. Write detailed test cases, identifying clear and concise steps to be taken by the tester, with expected outcomes.
  3. Assign the test cases to testers, who manually follow the steps and record the results.
  4. Author a test report, detailing the findings of the testers. The report is used by managers to determine whether the software can be released, and if not, it is used by engineers to identify and correct the problems.

A rigorous test case based approach is often traditional for large software engineering projects that follow a Waterfall model.[2] However, at least one recent study did not show a dramatic difference in defect detection efficiency between exploratory testing and test case based testing.[3]

Testing can be through black-, white- or grey-box testing. In white-box testing the tester is concerned with the execution of the statements through the source code. In black-box testing the software is run to check for the defects and is less concerned with how the processing of the input is done. Black-box testers do not have access to the source code. Grey-box testing is concerned with running the software while having an understanding of the source code and algorithms.[4]

Static and dynamic testing approach may also be used. Dynamic testing involves running the software. Static testing includes verifying requirements, syntax of code and any other activities that do not include actually running the code of the program.

Testing can be further divided into functional and non-functional testing. In functional testing the tester would check the calculations, any link on the page, or any other field which on given input, output may be expected. Non-functional testing includes testing performance, compatibility and fitness of the system under test, its security and usability among other things.

Stages

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There are several stages. They are:

Unit testing
This initial stage in testing normally carried out by the developer who wrote the code and sometimes by a peer using the white box testing technique.
Integration testing
This stage is carried out in two modes, as a complete package or as an increment to the earlier package. Most of the time black box testing technique is used. However, sometimes a combination of Black and White box testing is also used in this stage.
System testing
In this stage the software is tested from all possible dimensions for all intended purposes and platforms. In this stage Black box testing technique is normally used.
User acceptance testing
This testing stage carried out in order to get customer sign-off of finished product. A 'pass' in this stage also ensures that the customer has accepted the software and is ready for their use.
Release or deployment testing
Onsite team will go to customer site to install the system in customer configured environment and will check for the following points:
  1. Whether SetUp.exe is running or not.
  2. There are easy screens during installation
  3. How much space is occupied by system on HDD
  4. Is the system completely uninstalled when opted to uninstall from the system.

Advantages

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  • Low-cost operation as no software tools are used
  • Most bugs are caught by manual testing
  • Humans observe and judge better than the automated tools

Comparison to automated testing

[edit]

Test automation may be able to reduce or eliminate the cost of actual testing.[5] A computer can follow a rote sequence of steps more quickly than a person, and it can run the tests overnight to present the results in the morning. However, the labor that is saved in actual testing must be spent instead authoring the test program. Depending on the type of application to be tested, and the automation tools that are chosen, this may require more labor than a manual approach. In addition, some testing tools present a very large amount of data, potentially creating a time-consuming task of interpreting the results.

Things such as device drivers and software libraries must be tested using test programs. In addition, testing of large numbers of users (performance testing and load testing) is typically simulated in software rather than performed in practice.

Conversely, graphical user interfaces whose layout changes frequently are very difficult to test automatically. There are test frameworks that can be used for regression testing of user interfaces. They rely on recording of sequences of keystrokes and mouse gestures, then playing them back and observing that the user interface responds in the same way every time. Unfortunately, these recordings may not work properly when a button is moved or relabeled in a subsequent release. An automatic regression test may also be fooled if the program output varies significantly.

See also

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References

[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

Manual testing

View on Grokipedia
from Grokipedia
Manual testing is a fundamental software testing technique in which testers manually execute test cases without relying on tools to verify that a software application functions as intended, identify defects, and ensure compliance with specified requirements. This approach involves testers simulating end-user interactions, such as navigating user interfaces, submitting data, or attempting to exploit vulnerabilities, to evaluate the software's behavior across various scenarios. Unlike automated testing, which uses scripts and tools for repetitive execution, manual testing leverages judgment to explore unpredictable paths and uncover issues that might overlook. In the software development lifecycle, manual testing typically occurs during phases like unit testing, integration testing, system testing, and acceptance testing, where it can be applied in black-box (focusing on inputs and outputs without internal knowledge) or white-box (examining internal structures) formats to achieve comprehensive coverage. Testers create test cases based on requirements, design documents, or exploratory techniques, then document results, log defects, and collaborate with developers for resolution, often representing a significant portion of overall efforts—up to 40% of the development budget in some projects. Key activities include ad-hoc testing for quick issue detection, to investigate unscripted behaviors, and to assess from a perspective. One of the primary advantages of manual testing is its ability to incorporate and creativity, making it particularly effective for complex, subjective areas like , , and where nuanced human observation is essential. It requires no initial investment in scripting tools, allowing for rapid setup in early development stages or for one-off validations. However, manual testing is labor-intensive, time-consuming, and prone to , leading to inconsistencies in execution and scalability challenges for large-scale or . Despite these limitations, it remains indispensable in modern practices as of 2025, often complementing and AI-driven tools to provide a balanced testing strategy that enhances overall and reduces deployment risks.

Fundamentals

Definition and Scope

Manual testing is the process of executing test cases manually by human testers without the use of tools or scripts, primarily to verify that software applications function as intended, meet user requirements, and adhere to specified standards of and compliance. In this approach, testers simulate end-user interactions with the software, observing behaviors, inputs, and outputs to identify defects, inconsistencies, or deviations from expected results. This method relies on human observation and decision-making to assess qualitative aspects that automated processes might overlook, such as intuitive user interfaces or contextual error handling. The scope of manual testing encompasses a range of activities focused on dynamic execution rather than static analysis, including to confirm that individual features operate correctly, where testers dynamically design and adapt tests based on real-time discoveries, and visual checks to ensure aesthetic and layout consistency across interfaces. It explicitly excludes non-testing tasks like code reviews or static inspections, which do not involve running the software. Manual testing boundaries are defined by the need for human intervention in scenarios requiring subjective evaluation, such as ad-hoc scenarios or one-off validations, but it integrates within the broader lifecycle as a foundational verification step. Central to manual testing are key concepts like test cases, which consist of predefined sequences of steps, preconditions, inputs, expected outcomes, and postconditions to guide systematic verification. Human judgment plays a pivotal role, enabling testers to detect subtle defects—such as edge cases or usability issues—that rigid scripts cannot capture, thereby enhancing overall software quality through intuitive and adaptive assessment. Historically, manual testing emerged as the dominant method in software engineering during the 1950s through the 1970s, when testing equated to manual debugging and demonstration of functionality, before the advent of automation tools in the 1980s introduced scripted execution options.

Role in Software Testing

Manual testing plays a pivotal role in the Software Development Life Cycle (SDLC) by verifying software functionality and after requirements gathering and phases, ensuring alignment with specified needs before deployment. In the , manual testing follows a linear sequence post-development, involving comprehensive execution to validate built features against predefined test cases. In contrast, agile methodologies integrate manual testing iteratively within sprints, allowing testers to collaborate closely with developers for ongoing validation and rapid feedback loops. This positioning enables early defect detection, reducing rework costs later in the process. Prerequisites for effective manual testing include well-defined , which serve as the foundation for deriving test cases, and detailed test plans outlining objectives, scope, and execution strategies. Additionally, a stable test environment must be established, replicating production conditions to simulate real-world usage without introducing external variables. These elements assume testers have foundational knowledge of the application's requirements, enabling focused validation rather than exploratory guesswork. As a complement to automated testing, manual testing addresses inherent blind spots in scripted , such as dynamic changes, subjective assessments, and rare edge cases that demand human intuition and adaptability. For instance, while automated tests excel at repetitive regression checks, manual efforts uncover intuitive issues like intuitiveness or unexpected interactions in evolving features. This enhances overall test coverage, with manual testing often serving as the initial exploratory layer to inform subsequent automation priorities. In terms of involvement, manual testing accounts for a significant portion of total testing effort in early-stage projects, where exploratory and ad-hoc validation predominate, but this proportion evolves downward with project maturity as handles routine verifications. Such metrics highlight manual testing's foundational contribution to , particularly in contexts with high variability or limited prior data.

Methods and Techniques

Types of Manual Testing

Manual testing encompasses several distinct variants, each tailored to specific objectives in . These types differ in their approach, level of structure, and focus, allowing testers to address various aspects of software behavior and user interaction without relying on automation tools. The primary categories include , (in its manual form), , , and ad-hoc testing, each applied based on project needs such as functional validation, structural review, or rapid defect detection. Black-box testing treats the software as an opaque entity, focusing solely on inputs and expected outputs without any knowledge of the internal or details. This approach verifies whether the software meets specified requirements by simulating user interactions and checking results against predefined criteria. It is particularly useful for validating functional specifications from an end-user perspective. Key techniques within black-box testing include , which divides input data into classes expected to exhibit similar behavior, thereby reducing the number of test cases while maintaining coverage, and boundary value analysis, which targets the edges of input ranges where errors are most likely to occur, such as minimum and maximum values. These methods enhance efficiency in testing large input domains without exhaustive enumeration. White-box testing, when performed manually, involves examining the internal logic and structure of the software to ensure comprehensive path coverage, though it lacks the automation typically associated with code execution analysis. Testers manually trace code paths, decisions, and data flows to identify potential issues like unreachable branches or logical errors, often using techniques such as decision tables to map combinations of conditions and actions. This manual variant is limited to inspection-based checks rather than dynamic execution, making it suitable for early-stage reviews where developers and testers collaborate to verify structural integrity without tools. It is applied when understanding code flow is essential but automation resources are unavailable. Exploratory testing is an unscripted, improvisational approach where testers dynamically design and execute tests in real-time, leveraging their experience to uncover defects that scripted methods might miss. It emphasizes learning about the software while testing, adapting to new findings to probe deeper into potential risks. Sessions are typically time-boxed, lasting 30 to , to maintain focus and productivity, often structured under session-based with a outlining objectives. This type is ideal for complex or evolving applications where requirements are unclear or changing rapidly. Usability testing evaluates the intuitiveness and user-friendliness of the software interface through direct observation of users performing realistic tasks, focusing on how effectively and efficiently they interact with the system. Testers observe participants as they attempt to complete scenarios, measuring metrics like task success rates and completion times to identify friction points in or . This manual process aligns with standards defining as the extent to which a product can be used by specified users to achieve goals with , , and satisfaction in a given . It is essential for consumer-facing applications to ensure positive user experiences. Ad-hoc testing involves informal, unstructured exploration of the software to quickly spot obvious issues, without following test plans or cases, relying instead on the tester's intuition and familiarity. It serves as a rapid , often used for smoke tests to confirm basic functionality before deeper verification. While not systematic, this approach is valuable in time-constrained environments for initial defect detection and can reveal unexpected problems that overlook.

Execution Stages

The execution of manual testing follows a structured process to ensure systematic validation of software functionality without automation tools. This process, aligned with established standards like the ISTQB test process model, typically encompasses planning, preparation, execution, and reporting and closure phases, allowing testers to methodically identify defects and verify requirements. In the planning phase, testers define testing objectives based on project requirements and select test cases prioritized by to focus efforts on high-impact areas. A key artifact created here is the , which links requirements to corresponding test cases, ensuring comprehensive coverage and facilitating impact analysis if changes occur. This phase typically accounts for about 20% of the total testing effort, emphasizing upfront strategy to guide subsequent activities. Preparation involves developing detailed test scripts that outline steps, expected outcomes, and preconditions for each test case, alongside setting up test data, environments, and allocating roles among testers to simulate real-world conditions. Tools and resources are configured to support manual execution, such as preparing checklists or spreadsheets for tracking progress. This stage, combined with , often represents around 30-35% of the effort, building a solid foundation for reliable testing. During execution, testers manually perform the test cases, observing actual results against expected ones and logging any defects encountered, including details on severity (impact on system functionality) and priority (urgency of resolution). Defects are reported using bug tracking tools like Jira, where manual entry captures screenshots, steps to reproduce, and environmental details for developer . This core phase consumes approximately 50% of the testing effort, as it directly uncovers issues through hands-on interaction, including ad-hoc exploratory techniques where applicable to probe unscripted scenarios. Finally, reporting and closure entail analyzing execution results to generate defect reports, metrics on coverage and pass/fail rates, and overall test summaries for stakeholders. Retrospectives are conducted to capture lessons learned, such as process improvements or recurring defect patterns, leading to test closure activities like archiving artifacts and releasing resources. This phase, roughly 15-20% of the effort, ensures accountability and informs future testing cycles.

Evaluation

Advantages

Manual testing leverages human intuition to detect subtle issues that automated scripts often overlook, such as visual inconsistencies, flaws, and unexpected user behaviors in complex interfaces. This exploratory approach allows testers to apply creativity and judgment, uncovering defects through ad-hoc paths and contextual insights that rigid might miss, thereby reducing false negatives in intricate user interfaces. For instance, testers can identify aesthetic discrepancies or intuitive navigation problems by simulating real-world interactions, ensuring a more holistic of . A key strength of manual testing lies in its flexibility, particularly in agile environments where requirements evolve rapidly. Unlike scripted , which requires reprogramming for changes, manual methods enable testers to adapt test scenarios on the fly without additional infrastructure, supporting iterative development cycles and quick feedback loops. This adaptability is especially valuable for handling ambiguous or shifting specifications, allowing immediate incorporation of new features or modifications into the testing routine. For small-scale projects, prototypes, or one-off tests, manual testing offers cost-effectiveness by eliminating the need for expensive tools and setups. With lower initial and short-term costs, it suits resource-constrained teams, providing rapid results and straightforward execution without the overhead of scripting or maintenance. This makes it ideal for early-stage validation where thorough human oversight can be achieved economically. Manual testing ensures comprehensive coverage by enabling exploration of unplanned execution paths, which enhances defect detection in dynamic applications. Testers can deviate from predefined scripts to probe edge cases or interdependencies in complex UIs, achieving broader test scope and minimizing overlooked vulnerabilities. By mimicking end-user behaviors, manual testing simulates real-world usage scenarios, uncovering defects early in the development process. This human-centered approach replicates how actual users interact with the software, revealing practical issues like barriers or inefficiencies that scripted tests cannot fully capture. As a result, it contributes to more user-friendly products by addressing experiential flaws proactively.

Limitations

Manual testing is inherently time-intensive, as executing repetitive test cases can take hours or even days per testing cycle, particularly for in large-scale applications. This process scales poorly for extensive software regressions, where the volume of tests grows exponentially with project complexity, leading to prolonged development timelines. The approach is also prone to human error due to its subjective nature, where testers' interpretations and judgments can introduce inconsistencies in test execution and results. Fatigue from prolonged sessions further diminishes accuracy, as sustained manual effort over extended periods increases the likelihood of overlooking defects or applying uneven scrutiny across test cases. Scalability presents significant challenges, making manual testing unsuitable for high-volume scenarios such as load or parallel testing across numerous environments, which require specialized tools to handle efficiently without human intervention. In growing projects, the manual execution of thousands of test cases becomes unsustainable, limiting the ability to keep pace with rapid development iterations. Over time, the ongoing labor expenses associated with manual testing often surpass the initial setup costs of , especially for frequent test runs in iterative development cycles. Skilled testers must be continually engaged for each execution, accumulating high personnel costs without the one-time investment yielding reusable benefits. Finally, manual testing offers limited reusability, as test cases must be re-executed from scratch for every cycle or software update, unlike automated scripts that can be run repeatedly with minimal adaptation. This necessitates rewriting or redeveloping cases for new versions, further exacerbating time and resource demands.

Comparison with Automated Testing

Key Differences

Manual testing and automated testing represent two distinct paradigms in , differing fundamentally in their execution mechanisms and applicability. Manual testing relies on human testers to execute test cases through direct interaction with the software, leveraging , , and contextual judgment to explore and validate functionality. In contrast, automated testing employs scripts and specialized tools, such as or , to perform predefined actions with minimal human intervention, emphasizing repeatability and precision in test execution. Regarding speed and efficiency, manual testing is inherently slower, particularly for repetitive tasks like , where human execution can take significantly longer—often 70% more time than automated counterparts—making it less suitable for large-scale or frequent validations. Automated testing, however, excels in efficiency for high-volume scenarios, enabling rapid execution of extensive test suites and integration into / (CI/CD) pipelines for immediate feedback. While manual testing shines in ad-hoc and exploratory scenarios requiring on-the-fly adaptations, automated testing's rigidity limits its flexibility in dynamic, unscripted environments. The cost models of these approaches also diverge notably. Manual testing involves low upfront costs, as it requires no specialized tools or scripting, but incurs high ongoing expenses due to the need for skilled over extended periods, especially in projects demanding repeated testing cycles. Automated testing demands substantial initial in tool development, script creation, and maintenance, yet it proves more economical in the long term for mature projects by reducing labor-intensive repetitions and enabling scalable operations. For small-scale or one-off tests, manual methods remain cost-effective, whereas automation's grows with project complexity and duration. In terms of coverage types, manual testing is particularly strong for exploratory, , and assessments, where human perception can uncover intuitive issues like interface appeal or that scripted tests might overlook. Automated testing, conversely, is superior for functional and regression coverage, systematically verifying vast arrays of inputs and outputs across multiple iterations to ensure consistency in core behaviors. This complementary coverage profile means manual efforts often address nuanced, context-dependent areas, while automation handles exhaustive, rule-based validations. Error detection capabilities further highlight these contrasts. Manual testing excels at identifying contextual defects, such as subtle flaws or inconsistencies that require human interpretation, though it is susceptible to tester fatigue and oversight. Automated testing reliably flags exact matches against expected outcomes, providing consistent and detailed , but it may miss nuanced or unanticipated issues beyond its scripted parameters, such as visual inconsistencies or adaptive behaviors. Overall, manual detection prioritizes qualitative depth, while automated focuses on quantitative reliability.
AspectManual TestingAutomated Testing
ApproachHuman-driven execution with judgment and exploration.Scripted execution using tools for repeatability.
Speed/EfficiencySlower for regressions; ideal for ad-hoc testing.Faster for volume; less adaptable to changes.
Cost ModelLow initial; high ongoing due to labor.High initial scripting; low maintenance long-term.
Coverage TypesStrong in exploratory/usability.Excels in functional/regression.
Error DetectionContextual defects via human insight; prone to errors.Exact matches; misses nuances.

Complementary Use

In modern , hybrid testing strategies effectively combine automated and manual approaches by leveraging for repetitive tasks like smoke and regression tests, while employing manual testing for exploratory and phases that require human intuition and adaptability. This integration optimizes resource use in fast-paced environments, such as pipelines, where automated tests provide quick validation of core functionality, and manual efforts address nuanced user interactions and edge cases not easily scripted. Best practices for hybrid implementation include allocating 70-85% of efforts to to ensure stability and efficiency in repetitive scenarios, reserving 15-30% for manual testing to foster and handle complex, context-dependent validations. In / () pipelines, manual gates are strategically placed at critical release points to incorporate human judgment, preventing automated-only processes from overlooking subtle risks in production deployments. Within agile frameworks, case studies demonstrate effective integration through sequenced workflows: automated unit and integration tests run early in sprints for baseline verification, followed by manual end-to-end testing to simulate real-world usage and validate overall system coherence. This hybrid model has become standard since the 2010s rise of , enabling teams to accelerate delivery while maintaining quality through balanced automation and human oversight. A practical decision framework guides the choice between methods based on project maturity: manual testing is preferred for new features characterized by high and frequent iterations, allowing testers to explore ambiguities, whereas automated testing suits mature codebases for reliable, repeatable regression coverage. Emerging trends further enhance this synergy with AI-assisted tools, such as session recorders that capture exploratory sessions in real-time and generate or test suggestions, reducing manual effort without fully automating the process.

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