A complete guide to monkey testing

Monkey testing is a software testing technique where the tester applies random inputs and actions to a software application without any specific expectations or predefined test cases to uncover critical bugs and vulnerabilities. 

This software testing method is inspired by the Infinite Monkey Theorem, which suggests that if a monkey sat at a computer for an infinite amount of time, it would eventually reproduce great works of literature — even Shakespeare. In QA and engineering, that monkey is the software tester, and instead of Shakespeare, they’re finding bugs and vulnerabilities.

Your team can use the randomness and freedom monkey testing offers to stress test your application and find errors that may be overlooked with more structured testing methods.

Table of contents

• The goals of monkey testing
• Who performs monkey testing
• When to perform monkey testing
• Types of monkey testing
• Advantages and disadvantages of monkey testing
• How to implement monkey testing

The goals of monkey testing

While the goals of both structured and monkey testing are similar, monkey testing allows you to reach these goals in different ways, ensuring your application is more robust, stable, and secure.

Uncover unpredictable bugs

Even the best software testers make assumptions about the types of vulnerabilities or bugs that may exist within an application. This guesswork, no matter how educated, will inevitably guide their testing, which may result in some areas getting more attention than others. Monkey testing helps testers move past these assumptions to discover bugs that may not be found through more structured testing methods. By inputting random data, monkey testing can expose flaws and weaknesses in the software that only appear under unusual or unexpected conditions.

Improve stress tolerance 

Since monkey testing involves random, often intense and unexpected user behaviors, it serves as an effective stress test, pushing the software to its limits and assessing how well an application can sustain random and erratic behaviors. This helps identify issues like memory leaks, buffer overflows, and performance bottlenecks to create a more stable application.

Improve error handling 

Monkey testing can reveal how the application deals with errors resulting from random inputs. This is crucial for improving error handling mechanisms, ensuring that the software fails gracefully and provides useful feedback to the user under erroneous conditions.

Ensure security 

By subjecting the application to unpredictable inputs, monkey testing can also help in uncovering security vulnerabilities that might be exploited through abnormal use or unexpected sequences of actions.

Enhance user experience 

Random testing can simulate a less experienced user who interacts with the application in unforeseen ways. In addition to improving stress tolerance, the issues discovered through monkey testing can lead to a more robust interface, ultimately enhancing the overall user experience as well.

Save time and resources 

Automated monkey testing can be run continuously without much intervention, identifying potential issues early in the development cycle. This early detection saves time and resources that would otherwise be spent on fixing problems discovered later.

Who performs monkey testing 

Monkey testing is usually implemented as random and automated unit testing and can be conducted by various members of a software development team, but it is typically within the realm of quality assurance teams. There are various approaches to monkey testing, so despite the randomness of this application testing method, set up is still required. QA teams design these test scenarios to guarantee focus and thoroughness.

At times, especially in smaller teams or agile environments, software developers may also conduct monkey testing. This approach allows developers to directly observe the resilience of their code against random and unexpected inputs.

In cases where monkey testing is automated, automation engineers play a key role. They write scripts that generate random inputs and actions, and set up the automation framework to execute these tests.

Qualifications for monkey testing

Unlike other testing methods, monkey testing does not necessarily require a deep understanding of the application itself. In fact, some types of monkey testing benefit from little to no application knowledge.

Other qualifications, however, are essential for effective monkey testing:

1. A good grasp of general testing principles
2. Automation and scripting skills (if you are automating monkey testing)

When to perform monkey testing

The timing of monkey testing in the software development lifecycle (SDLC) is crucial. It should be employed when the software is stable enough to yield meaningful results but early enough to fix any critical issues it uncovers.

• After initial stability is established: Monkey testing is most effective when the basic functionality of the application has been developed and some level of stability is established. Running it too early in the development process, when the software is still in a highly unstable or incomplete state, may result in an overwhelming number of failures that offer little insight.
• Before user acceptance testing (UAT): It is beneficial to conduct monkey testing before UAT begins. This helps ensure that the application is robust enough to handle unpredictable user behavior during the final stages of testing.
• As part of continuous testing in agile development: In agile development environments, monkey testing can be integrated into the continuous testing process. Automated monkey tests can run regularly, ensuring ongoing robustness and stability as new features are added or changes are made to the application.
• During regression testing: Including monkey testing during regression testing cycles can help ensure that new code changes have not introduced instability in previously stable areas of the application.

Types of monkey testing

Monkey testing can be further broken down into three subcategories based on the complexity of the testing, the awareness of the tester, and the overall objective.

Brilliant monkey testing

Brilliant monkey testing is the most advanced form of monkey testing. Testers are familiar with the application and aware of its current state. They perform complex, purposeful sequences of actions that closely mimic the behavior of advanced users.

Characteristics:

1. Targeted actions with specific goals, such as completing a transaction or navigating through a series of steps
2. Capable of recognizing and responding to the application's output
3. Often used for fine-tuning usability and testing complex scenarios
4. Requires significant setup and understanding of the application

Brilliant monkey testing is ideal for mature applications where nuanced interactions and complex workflows need to be tested thoroughly.

Smart monkey testing

In smart monkey testing, testers have some knowledge of the application they are testing. These tests are not completely random; they are designed to navigate the application in a way an average user might by using semi-random inputs aligned with the behavior of an everyday or new but educated user.

Characteristics:

1. Able to handle and navigate relatively complex interfaces and workflows
2. Requires basic knowledge like logging in, filling out forms, and maintaining sessions
3. Less likely to cause crashes, but more effective in finding functional bugs

Smart monkey testing is useful for more sophisticated applications where understanding the context and workflow is essential for effective testing.

Dumb monkey testing

Dumb monkey testing is the most basic form of monkey testing. It involves random inputs with little to no knowledge of how the application should behave. The tests are completely random and do not require any understanding of the application, aiming to mimic the behavior of a person — or monkey — who is entirely unfamiliar with the application.

Characteristics:

1. Totally random inputs and interactions
2. Little to no awareness of the application’s state or context
3. High potential for causing crashes and uncovering major bugs
4. Easy to set up as it requires no specific knowledge of the application

Dumb monkey testing is particularly useful in the early stages of development to quickly find crash-causing bugs or major issues in the application. It's also useful for load testing and ensuring basic robustness.

Monkey testing vs other testing methods

The terms monkey testing, gorilla testing, and adhoc testing are often used interchangeably. However, there are subtle differences in intent.

Advantages and disadvantages of monkey testing

As is the case with any application testing method, there are advantages and disadvantages to monkey testing. Understanding these can help teams effectively integrate this method into their testing strategies.

The advantages of monkey testing

We’ve already mentioned how monkey testing helps QA teams detect unexpected bugs, thoroughly stress test applications, and automate part of the software testing process. However, the advantages don’t end there.

Requires minimal planning

Monkey testing — especially smart and brilliant monkey testing — does require some planning. However, the planning required is less detailed than structured testing, making it easier and faster to set up and execute. 

Saves time

The reduced planning time, in addition to the ability to automate, means monkey testing is a great way to cover a lot of ground with minimal effort.

Complements other testing methods

A main benefit of monkey testing is that it fills in the gaps left by structured testing methods. Folding monkey testing into your QA creates a more comprehensive testing strategy overall.

The disadvantages of monkey testing

Lack of focus

The random nature of monkey testing means it lacks the focus of targeted testing approaches. It might miss specific, complex scenarios that structured performance testing would catch.

Difficulty in reproducing and analyzing results

Most research and testing processes follow the scientific method; a requirement of this method is that the results are replicable. Monkey testing, by design, does not follow this method. Bugs found through monkey testing can sometimes be hard to reproduce due to the randomness of the actions that led to them, complicating the debugging process. The results of monkey testing can also be challenging to analyze due to the random nature of the tests, requiring more effort to sift through data to find actionable insights.

Potential for inefficiency

Monkey testing can save your team time — when implemented correctly. But without strategic implementation, monkey testing is time-consuming and can waste a lot of resources without yielding proportionately significant results.

Limited scope

Monkey testing is less effective in identifying bugs related to business logic or complex user scenarios since it operates randomly without understanding the application's purpose.

How to implement monkey testing

The goal when implementing monkey testing into your process is to ensure your process is both effective and efficient, providing valuable insights without overwhelming the development team with irrelevant data. 

Step 1: Choose the right testing tool or framework

Select a tool or framework that can generate random inputs and interact with your application. There are many testing tools available specifically for monkey testing, such as the UI/Application Exerciser Monkey for Android apps.

In some cases, you might need to write custom scripts to create more controlled random testing scenarios, especially for web applications or desktop applications.

Step 2: Define the scope and parameters

Decide which parts of the application will be subjected to monkey testing. It could be the entire application or specific modules where randomness could potentially reveal hidden issues.

Set parameters for these tests, such as the duration of each test session, the intensity of input, and the types of random actions (clicks, swipes, data input, etc.).

Step 3: Integrate with the development lifecycle

Integrate monkey testing early in the software development lifecycle and make it a part of your continuous testing strategy. This helps in identifying and fixing issues early on.

Consider running monkey tests even after deployment, especially for web and mobile applications, to continually ensure robustness.

Step 4: Monitor and analyze results

Ensure that the tool or script you use effectively logs all actions and outcomes. This data is crucial for analyzing what the monkey test did and what issues it may have uncovered.

Regularly review the results to identify patterns or recurring issues. This analysis can reveal insights into the areas of the application that are more prone to errors.

Step 5: Reproduce and address issues

When a potential issue is identified, try to reproduce it with a more structured approach to understand it fully. While reproducing results perfectly isn’t always possible with random testing, getting close is more helpful than not attempting to reproduce at all.

Prioritize the issues based on their severity and impact. Some issues may be critical and need immediate attention, while others can be scheduled for later.

Step 6: Refine the testing process

Use insights gained from each round of testing to refine your monkey testing process. Adjust parameters, focus areas, and even the tools as needed to make the tests more effective.

Lastly, establish a feedback loop with the development team to continually improve both the application and the testing process.

Improve software stability with monkey testing

Monkey testing is a powerful tool in the software testing arsenal, particularly for enhancing application robustness and finding unexpected bugs. However, it is not effective as a standalone solution. The best way to navigate the cons (and take advantage of the pros) is to fold monkey testing into your QA process alongside more structured testing methods. The key is to strike a balance, leveraging the strengths of monkey testing while mitigating its weaknesses through a well-rounded testing strategy.