Random testing

Random testing is a black-box software testing technique where programs are tested by generating random, independent inputs. Results of the output are compared against software specifications to verify that the test output is pass or fail.[1] In case of absence of specifications the exceptions of the language are used which means if an exception arises during test execution then it means there is a fault in the program.

History of random testing

Random testing for hardware was first examined by Melvin Breuer in 1971 and initial effort to evaluate its effectiveness was done by Pratima and Vishwani Agrawal in 1975.[2]

In software Duran and Ntafos had examined random testing in 1984.[3] Earlier Howden had termed it functional testing in 1980.

Overview

Consider the following C++ function:

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

Now the random tests for this function could be {123, 36, -35, 48, 0}. Only the value '-35' triggers the bug. If there is no reference implementation to check the result, the bug still could go unnoticed. However, an assertion could be added to check the results, like:

void testAbs(int n) {
    for (int i=0; i<n; i++) {
        int x = getRandomInput();
        int result = myAbs(x);
        assert(result>=0);
    }
}

The reference implementation is sometimes available, e.g. when implementing a simple algorithm in a much more complex way for better performance. For example, to test an implementation of the Schönhage–Strassen algorithm, the standard "*" operation on integers can be used:

int getRandomInput() {
    . . .
}

void testFastMultiplication(int n) {
    for (int i=0; i<n; i++) {
        long x = getRandomInput();
        long y = getRandomInput();
        long result = fastMultiplication(x, y);
        assert(x*y==result);
    }
}

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.[1]

Strengths and weaknesses

Random testing is typically praised for the following strengths:

The following weaknesses are typically pointed out by detractors:

Types of random testing

With respect to the input

Guided vs. unguided

Implementations

Some tools implementing random testing:

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.[1]

An 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.[4] In particular, random testing finds more bugs than manual inspections or user reports (albeit different ones).[7]

See also

References

  1. 1 2 3 Richard Hamlet (1994). "Random Testing". In John J. Marciniak. Encyclopedia of Software Engineering (PDF) (1 ed.). John Wiley and Sons. ISBN 0471540021. Retrieved 16 June 2013.
  2. ↑ Agrawal, P.; Agrawal, V.D., "Probabilistic Analysis of Random Test Generation Method for Irredundant Combinational Logic Networks," Computers, IEEE Transactions on , vol.C-24, no.7, pp.691,695, July 1975
  3. ↑ Duran, J. and S. Ntafos, An evaluation of random testing, IEEE Trans. Software Eng. SE-10 (July, 1984), 438-444
  4. 1 2 3 http://se.inf.ethz.ch/research/autotest/
  5. ↑ Pacheco, Carlos; Shuvendu K. Lahiri; Michael D. Ernst; Thomas Ball (May 2007). "Feedback-directed random test generation" (PDF). ICSE '07: Proceedings of the 29th International Conference on Software Engineering (IEEE Computer Society): 75–84. ISSN 0270-5257.
  6. ↑ Chen, T.Y.; H. Leung; I.K. Mak (2005). "Adaptive Random Testing" (PDF). Advances in Computer Science - ASIAN 2004. Higher-Level Decision Making (Lecture Notes in Computer Science Volume 3321): 320–329.
  7. ↑ Ilinca Ciupa; Alexander Pretschner; Manuel Oriol; Andreas Leitner; Bertrand Meyer (2009). "On the number and nature of faults found by random testing". Software Testing, Verification and Reliability (John Wiley and Sons) 21: 3–28. doi:10.1002/stvr.415.

External links

This article is issued from Wikipedia - version of the Wednesday, May 04, 2016. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.