Automatic structural testing using genetic algorithms

Jones, B.F.; Sthamer, Harmen-Hinrich; Eyres, D.E.

doi:10.1049/sej.1996.0040

Cited by 278 publications

(154 citation statements)

References 1 publication

Supporting

Mentioning

146

Contrasting

Unclassified

Order By: Relevance

“…The genetic algorithm was first used by Xanthakis [23] to generate input data satisfying the all branch predicate criterion. Evolutionary approaches, where search algorithms, in particular genetic algorithm, are tailored to automate and support testing activities, i.e., to generate test input data [10,20,21] are often referred to as evolutionary-based software testing or simply evolutionary testing. A survey of evolutionary testing and related techniques is beyond the scope of this paper; the interested reader may refer to the survey published by McMinn [12].…”

Section: Related Workmentioning

confidence: 99%

Divide-by-Zero Exception Raising via Branch Coverage

Bhattacharya

Sakti

Antoniol

et al. 2011

Search Based Software Engineering

View full text Add to dashboard Cite

Abstract. In this paper, we discuss how a search-based branch coverage approach can be used to design an effective test data generation approach, specifically targeting divide-by-zero exceptions. We first propose a novel testability transformation combining approach level and branch distance. We then use different search strategies, i.e., hill climbing, simulated annealing, and genetic algorithm, to evaluate the performance of the novel testability transformation on a small synthetic example as well as on methods known to throw divide-by-zero exceptions, extracted from real world systems, namely Eclipse and Android. Finally, we also describe how the test data generation for divide-by-zero exceptions can be formulated as a constraint programming problem and compare the resolution of this problem with a genetic algorithm in terms of execution time. We thus report evidence that genetic algorithm using our novel testability transformation out-performs hill climbing and simulated annealing and a previous approach (in terms of numbers of fitness evaluation) but is out-performed by constraint programming (in terms of execution time).

show abstract

Section: Related Workmentioning

confidence: 99%

Divide-by-Zero Exception Raising via Branch Coverage

Bhattacharya

Sakti

Antoniol

et al. 2011

Search Based Software Engineering

View full text Add to dashboard Cite

show abstract

“…In order to define the fitness function, this research builds upon previous work dealing with branching conditions (among others [8], [5], and [9]). These are developed in [10] by introducing the idea of an approximation level.…”

Section: Overview Of Evolutionary Testing (Et)mentioning

confidence: 99%

Structural and Functional Sequence Test of Dynamic and State-Based Software with Evolutionary Algorithms

Baresel

Pohlheim

Sadeghipour³

2003

Genetic and Evolutionary Computation — GECCO 2003

View full text Add to dashboard Cite

Abstract. Evolutionary Testing (ET) has been shown to be very successful for testing real world applications [10]. The original ET approach focuses on searching for a high coverage of the test object by generating separate inputs for single function calls. We have identified a large set of real world application for which this approach does not perform well because only sequential calls of the tested function can reach a high structural coverage (white box test) or can check functional behavior (black box tests). Especially, control software which is responsible for controlling and constraining a system cannot be tested successfully with ET. Such software is characterized by storing internal data during a sequence of calls. In this paper we present the Evolutionary Sequence Testing approach for white box and black box tests. For automatic sequence testing, a fitness function for the application of ET will be introduced, which allows the optimization of input sequences that reach a high coverage of the software under test. The authors also present a new compact description for the generation of real-world input sequences for functional testing. A set of objective functions to evaluate the test output of systems under test have been developed. These approaches are currently used for the structural and safety testing of car control systems.

show abstract

“…Through instrumentation, the subject program has in effect been converted into another program that computes a function that is to be minimised to zero. This approach has been used by Korel [2], Tracey et al [7], [8], Wegener et al [9], Jones [1] and Michael [4] [3].…”

Section: // Execution Required To Enter This Branchmentioning

confidence: 99%

“…The truth value of a fuzzy disjunction is the maximum of the truth values of the operands, the value of a fuzzy conjunction is the minimum of the operand truth values. The common use of the functions min and max 1 should not obscure the fact that the intended interpretation in test data search is quite different. Fuzzy logic is concerned with formalising reasoning with vague concepts where the vagueness is formalised as a fuzzy set.…”

Section: Analytical Considerationsmentioning

confidence: 99%

Predicate Expression Cost Functions to Guide Evolutionary Search for Test Data

Bottaci

2003

Genetic and Evolutionary Computation — GECCO 2003

View full text Add to dashboard Cite

Abstract. Several researchers are using evolutionary search methods to search for test data with which to test a program. The fitness or cost function depends on the test goal but almost invariably an important component of the cost function is an estimate of the cost of satisfying a predicate expression as might occur in branches, exception conditions, etc. This paper reviews the commonly used cost functions and points out some deficiencies. Alternative cost functions are proposed to overcome these deficiencies. The evidence from an experiment is that they are more reliable.

show abstract

Automatic structural testing using genetic algorithms

Cited by 278 publications

References 1 publication

Divide-by-Zero Exception Raising via Branch Coverage

Divide-by-Zero Exception Raising via Branch Coverage

Structural and Functional Sequence Test of Dynamic and State-Based Software with Evolutionary Algorithms

Predicate Expression Cost Functions to Guide Evolutionary Search for Test Data

Contact Info

Product

Resources

About