int myAbs(int x) {

    if (x>0) {         return x;    }    else {        return x; // bug: should be '-x'    }

}

void testAbs(int n) {

    for (int i=0; i=0);    }

}

int getRandomInput() {

}

void testFastMultiplication(int n) {

    for (int i=0; i
}
While this example is limited to simple types (for which a simple random generator can be used), tools targeting object-oriented languages typically explore the program to test and find generators (constructors or methods returning objects of that type) and call them using random inputs (either themselves generated the same way or generated using a pseudo-random generator if possible). Such approaches then maintain a pool of randomly generated objects and use a probability for either reusing a generated object or creating a new one.[4]
On randomness
According to the seminal paper on random testing by D. Hamlet
[..] the technical, mathematical meaning of "random testing" refers to an explicit lack of "system" in the choice of test data, so that there is no correlation among different tests.[3]
Strengths and weaknesses
{{Unreferenced section|date=August 2014}}
Random testing is typically praised for the following strengths:
It is cheap to use: it does not need to be smart about the program under test.
It does not have any bias: unlike manual testing, it does not overlook bugs because there is misplaced trust in some code.
It is quick to find bug candidates: it typically takes a couple of minutes to perform a testing session.
If software is properly specified: it finds real bugs.
The following weaknesses are typically pointed out by detractors:
It only finds basic bugs (f.ex. null pointer dereferencing).
It is only as precise as the specification and specifications are typically imprecise.
It compares poorly with other techniques to find bugs (f.ex. static program analysis).
If different inputs are randomly selected on each test run, this can create problems for continuous integration because the same tests will pass or fail randomly.[4]
Some argue that it would be better to thoughtfully cover all relevant cases with manually constructed tests in a white-box fashion, than to rely on randomness.[4]
Types of random testing
With respect to the input
Random input sequence generation (i.e. a sequence of method calls)
Random sequence of data inputs (sometimes called stochastic testing) - f.ex. a random sequence of method calls
Random data selection from existing database
Guided vs. unguided
undirected random test generation - with no heuristics to guide its search
directed random test generation - f.ex. "feedback-directed random test generation"[5] or "adaptive random testing" [6]
 Implementations 
Some tools implementing random testing:
 QuickCheck - a famous test tool, originally developed for Haskell but ported to many other languages, that generates random sequences of API calls based on a model and verifies system properties that should hold true after each run.
 Randoop - generates sequences of methods and constructor invocations for the classes under test and creates JUnit tests from these
 Simulant - a Clojure tool that runs simulations of various agents (f.ex. users with different behavioral profiles) based on a statistical model of their behavior, recording all the actions and results into a database for later exploration and verification
 AutoTest - a tool integrated to EiffelStudio testing automatically Eiffel code with contracts based on the eponymous research prototype.[4]·
 York Extensible Testing Infrastructure (YETI) - a language agnostic tool which targets various programming languages (Java, JML, CoFoJa, .NET, C, Kermeta).
 GramTest - a grammar based random testing tool written in Java, it uses BNF notation to specify input grammars.
Critique
Random testing has only a specialized niche in practice, mostly because an effective oracle is seldom available, but also because of difficulties with the operational profile and with generation of pseudorandom input values.[3]
A test oracle is an instrument for verifying whether the outcomes match the program specification or not. An operation profile is knowledge about usage patterns of the program and thus which parts are more important.
For programming languages and platforms which have contracts (for example Eiffel. .NET or various extensions of Java like JML, CoFoJa...) contracts act as natural oracles and the approach has been applied successfully.[7] In particular, random testing finds more bugs than manual inspections or user reports (albeit different ones).[8]
See also
Fuzz testing - a kind of random testing which provides invalid input to the tested program
Lazy systematic unit testing#Systematic Testing - a systematic way of exploring "all" method calls, as implemented e.g. by NASA's Java Path Finder (which blends testing with model checking by limiting the state space to a reasonable size by various means)
Constrained random generation in SystemVerilog
Corner case
Edge case
Concolic testing
References
1. ^{{cite journal|url=http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1672882&tag=1|title=Probabilistic Analysis of Random Test Generation Method for Irredundant Combinational Logic Networks|first1=P.|last1=Agrawal|first2=V. D.|last2=Agrawal|date=1 July 1975|journal=IEEE Transactions on Computers|volume=C-24|issue=7|pages=691–695|accessdate=15 November 2017|via=IEEE Xplore|doi=10.1109/T-C.1975.224289}}
2. ^{{cite journal|url=http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5010257|title=An Evaluation of Random Testing|first1=J. W.|last1=Duran|first2=S. C.|last2=Ntafos|date=1 July 1984|journal=IEEE Transactions on Software Engineering|volume=SE-10|issue=4|pages=438–444|accessdate=15 November 2017|via=IEEE Xplore|doi=10.1109/TSE.1984.5010257}}
3. ^1 2 {{cite book|title=Encyclopedia of Software Engineering|year=1994|publisher=John Wiley and Sons|isbn=978-0471540021|author=Richard Hamlet|edition=1st|editor=John J. Marciniak|chapter=Random Testing}}
4. ^1 {{cite web|url=http://stackoverflow.com/questions/636353/is-it-a-bad-practice-to-randomly-generate-test-data|title=Is it a bad practice to randomly-generate test data?|website=stackoverflow.com|accessdate=15 November 2017}}
5. ^{{cite journal|last=Pacheco|first=Carlos|author2=Shuvendu K. Lahiri |author3=Michael D. Ernst |author4=Thomas Ball |title=Feedback-directed random test generation|journal=ICSE '07: Proceedings of the 29th International Conference on Software Engineering|date=May 2007|pages=75–84|url=http://people.csail.mit.edu/cpacheco/publications/feedback-random.pdf|issn=0270-5257}}
6. ^{{cite journal|last=Chen|first=T.Y.|author2=H. Leung |author3=I.K. Mak | title=Adaptive Random Testing |journal=Advances in Computer Science - ASIAN 2004. Higher-Level Decision Making |date= 2005|pages=320–329|url=http://www.utdallas.edu/~ewong/SYSM-6310/03-Lecture/02-ART-paper-01.pdf}}
7. ^1 2 {{cite web|url=http://se.inf.ethz.ch/research/autotest/|title=AutoTest - Chair of Software Engineering|website=se.inf.ethz.ch|accessdate=15 November 2017}}
8. ^{{cite journal|title=On the number and nature of faults found by random testing|year=2009|author=Ilinca Ciupa|author2=Alexander Pretschner|author3=Manuel Oriol|author4=Andreas Leitner|author5=Bertrand Meyer|journal=Software Testing, Verification and Reliability|doi=10.1002/stvr.415|volume=21|pages=3–28}}
External links
Random testing by Andrea Arcuri.
Random testing by Richard Hamlet, professor emeritus at Portland State University; a valuable list of resources at the end of the paper
Random Testing wiki at Cunningham & Cunningham, Inc.
 1 : Software testing