In software testing, monkey testing is a technique where the user tests the application or system by providing random inputs and checking the behavior, or seeing whether the application or system will crash. Monkey testing is usually implemented as random, automated unit tests.
While the source of the name "monkey" is uncertain, it is believed by some that the name has to do with the infinite monkey theorem,[1] which states that a monkey hitting keys at random on a typewriter keyboard for an infinite amount of time will almost surely type a given text, such as the complete works of William Shakespeare. Some others believe that the name comes from the classic Mac OS application "The Monkey" developed by Steve Capps prior to 1983. It used journaling hooks to feed random events into Mac programs, and was used to test for bugs in MacPaint.[2]
Monkey Testing is also included in Android Studio as part of the standard testing tools for stress testing.[3]
Monkey testing can be categorized into smart monkey tests or dumb monkey tests.
Smart monkeys are usually identified by the following characteristics:[4]
Some smart monkeys are also referred to as brilliant monkeys, which perform testing as per user's behavior and can estimate the probability of certain bugs.
Dumb monkeys, also known as "ignorant monkeys", are usually identified by the following characteristics:
Monkey testing is an effective way to identify some out-of-the-box errors. Since the scenarios tested are usually ad-hoc, monkey testing can also be a good way to perform load and stress testing. The intrinsic randomness of monkey testing also makes it a good way to find major bugs that can break the entire system. The setup of monkey testing is easy, therefore good for any application. Smart monkeys, if properly set up with an accurate state model, can be really good at finding various kinds of bugs.
The randomness of monkey testing often makes the bugs found difficult or impossible to reproduce. Unexpected bugs found by monkey testing can also be challenging and time consuming to analyze. In some systems, monkey testing can go on for a long time before finding a bug. For smart monkeys, the ability highly depends on the state model provided, and developing a good state model can be expensive.
While monkey testing is sometimes treated the same as fuzz testing[5] and the two terms are usually used together,[6] some believe they are different by arguing that monkey testing is more about random actions while fuzz testing is more about random data input.[7] Monkey testing is also different from ad-hoc testing in that ad-hoc testing is performed without planning and documentation and the objective of ad-hoc testing is to divide the system randomly into subparts and check their functionality, which is not the case in monkey testing.