Test input generation for java containers using state matching

Visser, Willem; Păsăreanu, Corina S.; Pelánek, Radek

doi:10.1145/1146238.1146243

Cited by 137 publications

(171 citation statements)

References 28 publications

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“…While several of these studies evaluate effectiveness of random testing in various scenarios [5,6,7,10,14,30,33], only the VPP study [30] represented. The only exceptions are predicate coverage used in the VPP study [30], statement coverage [5], MCDC [26], and an unspecified "structural coverage" [22,23]. We use not only predicate coverage, which subsumes branch coverage, but also mutation score.…”

Section: Related Workmentioning

confidence: 99%

“…In general there are different ways to represent and encode a test case. Because we focus on container classes, we use a simple representation that is common in the literature (e.g., [30]): A test case is a sequence of operations such as add and remove on a container instance created with its default constructor. For the input data, we only consider integer values bounded in [1,R], where R is a fixed constant.…”

Section: Test Generation For Container Classesmentioning

confidence: 99%

“…Unlike RT that produces random sequences of method calls, SA attempts to find that certain sequences are equivalent and hence need not be generated. The original exposition of SA [30] was based on explicit-state model checking, and SA was one of six techniques in the same general framework. We provide a new exposition that directly describes the exploration, focuses solely on SA, and allowed us to obtain a faster implementation of SA without relying on a model checker.…”

Section: Shape Abstractionmentioning

confidence: 99%

“…The empirical evidence on research in this area often focuses on container classes [4,5,6,7,10,12,14,17,19,22,23,26,27,29,30,31,33,34]-containers are an important part of many standard libraries, and bugs in these containers could significantly affect applications, so directing testing efforts to these containers is worthwhile. Testing containers is not only important but also challenging to achieve with some advanced testing technique such as those based on symbolic execution [30].…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the literature offers little evidence on how more advanced techniques compare to random testing. In fact, excluding comparisons with search algorithms, we are aware of only one study that compared random testing to systematic techniques, by Visser, Pasareanu, and Pelanek [30]; we will refer to this study as the VPP study.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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%

Section: Test Generation For Container Classesmentioning

confidence: 99%

Section: Shape Abstractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Testing Container Classes: Random or Systematic?

Sharma

Gligoric

Arcuri

et al. 2011

Fundamental Approaches to Software Engineering

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Practical JFSL verification using TACO

Chicote¹,

Ciolek²,

Galeotti

2013

Softw Pract Exp

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SUMMARYTranslation of Annotated COde (TACO) is a SAT‐based tool for bounded verification of Java programs. One challenge many formal tools share is to provide a practical interface for a non‐proficient user. In this article, we present an Eclipse plug‐in for the static verifier TACO. This plug‐in allows a user to walk a counterexample trace mimicking a debugging session. TacoPlug (our plug‐in) uses and extends TACO to provide a better debugging experience. TacoPlug interface allows the user to verify an annotated software using the TACO verifier. If TACO finds a violation to the specification, TacoPlug presents it in terms of the annotated source code. TacoPlug features several views of the error trace to facilitate fault understanding. It resembles any software debugger, but the debugging occurs statically without executing the program. Furthermore, should a dynamic analysis be required, TacoPlug presents the user with a unit test case generated by TACO based on the detected violation. We show the usability of our tool by means of a motivational example taken from a real‐life software error. Copyright © 2013 John Wiley & Sons, Ltd.

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Search‐based testing using constraint‐based mutation

Malburg

2013

Software Testing Verif & Rel

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SUMMARYMany modern automated test generators are based on either metaheuristic search techniques or use constraint solvers. Both approaches have their advantages, but they also have specific drawbacks: Search‐based methods may get stuck in local optima and degrade when the search landscape offers no guidance; constraint‐based approaches, on the other hand, can only handle certain domains efficiently. This paper describes a method that integrates both techniques and delivers the best of both worlds. On a high‐level view, the proposed method uses a genetic algorithm to generate tests, but the twist is that during evolution, a constraint solver is used to ensure that mutated offspring efficiently explores different control flow. Experiments on 20 case study programmes show that on average the combination improves branch coverage by 28% over search‐based techniques while reducing the number of tests by 55%, and improves coverage by 13% over constraint‐based techniques while reducing the number of tests by 73%. Copyright © 2013 John Wiley & Sons, Ltd.

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Test input generation for java containers using state matching

Cited by 137 publications

References 28 publications

Testing Container Classes: Random or Systematic?

Testing Container Classes: Random or Systematic?

Practical JFSL verification using TACO

Search‐based testing using constraint‐based mutation

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