An Evaluation of Random Testing

Duran, Joe W.; Ntafos, Simeon

doi:10.1109/tse.1984.5010257

Cited by 556 publications

(315 citation statements)

References 9 publications

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“…A number of studies compared advanced techniques for test generation or test selection with random testing [14,15,16,18,32], but these studies did not provide conclusive answers either way (sometimes random testing looked better and sometimes worse than more advanced techniques), and they did not focus on containers. Several recent techniques use random generation for object-oriented unit tests [1,13,21] but target shallower exploration of larger codebases and can generate complex test data inputs, while testing containers focuses on deeper exploration of smaller codebases and typically requires only simple data inputs.…”

Section: Related Workmentioning

confidence: 99%

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Testing Container Classes: Random or Systematic?

Sharma

Gligoric

Arcuri

et al. 2011

Fundamental Approaches to Software Engineering

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Abstract. Container classes such as lists, sets, or maps are elementary data structures common to many programming languages. Since they are a part of standard libraries, they are important to test, which led to research on advanced testing techniques targeting such containers and research on comparing testing techniques using such containers. However, these techniques have not been thoroughly compared to simpler techniques such as random testing. We present the results of a larger case study in which we compare random testing with shape abstraction, a systematic technique that showed the best results in a previous study. Our experiments show that random testing is about as effective as shape abstraction for testing these containers, which raises the question whether containers are well suited as a benchmark for comparing advanced testing techniques.

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Section: Related Workmentioning

confidence: 99%

“…Although RT is often considered a naive testing strategy [20], it can be very effective in many testing scenarios [9,15]. When the test cases have a variable length representation, there can be different ways to sample test cases at random [9].…”

Section: Random Testingmentioning

confidence: 99%

Testing Container Classes: Random or Systematic?

Sharma

Gligoric

Arcuri

et al. 2011

Fundamental Approaches to Software Engineering

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show abstract

“…Ntafos compared the fault detection probability of random testing with partition testing, and discovered that the differences in effectiveness were small [5]. Hamlet and Taylor corroborated the original results [8].…”

Section: Related Workmentioning

confidence: 70%

Testing and Tracing Lazy Functional Programs Using QuickCheck and Hat

Claessen

Runciman

Chitil

et al. 2003

Advanced Functional Programming

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“…Duran and S.C. Ntafos generated the software test data using 'random testing technique'. In their work, they (1) compared random testing with partition testing, and (2) evaluated the random testing process using various coverage criteria such as segment coverage, branch coverage, path coverage, etc [2].…”

Section: Related Workmentioning

confidence: 99%

“…Functional testing focuses on testing the functionality of the system using some functional test criteria such as equivalence classes [2], random testing [2], etc. Structural testing focuses on testing the structure of the system using some structural test criteria such as paths, functions, conditions, branches etc.…”

Section: Introductionmentioning

confidence: 99%

An Adequacy Based Test Data Generation Technique Using Genetic Algorithms

Malhotra¹,

Garg²

2011

Journal of Information Processing Systems

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Abstract-As the complexity of software is increasing, generating an effective test data has become a necessity. This necessity has increased the demand for techniques that can generate test data effectively. This paper proposes a test data generation technique based on adequacy based testing criteria. Adequacy based testing criteria uses the concept of mutation analysis to check the adequacy of test data. In general, mutation analysis is applied after the test data is generated. But, in this work, we propose a technique that applies mutation analysis at the time of test data generation only, rather than applying it after the test data has been generated. This saves significant amount of time (required to generate adequate test cases) as compared to the latter case as the total time in the latter case is the sum of the time to generate test data and the time to apply mutation analysis to the generated test data. We also use genetic algorithms that explore the complete domain of the program to provide near-global optimum solution. In this paper, we first define and explain the proposed technique. Then we validate the proposed technique using ten real time programs. The proposed technique is compared with path testing technique (that use reliability based testing criteria) for these ten programs. The results show that the adequacy based proposed technique is better than the reliability based path testing technique and there is a significant reduce in number of generated test cases and time taken to generate test cases.

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An Evaluation of Random Testing

Cited by 556 publications

References 9 publications

Testing Container Classes: Random or Systematic?

Testing Container Classes: Random or Systematic?

Testing and Tracing Lazy Functional Programs Using QuickCheck and Hat

An Adequacy Based Test Data Generation Technique Using Genetic Algorithms

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