A Hybrid Directed Test Suite Augmentation Technique

Xu, Zhihong; Kim, Yun‐Ho; Kim, Moonzoo; Rothermel, Gregg

doi:10.1109/issre.2011.21

Cited by 28 publications

(23 citation statements)

References 36 publications

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“…For example, in previous work [9] where the goal was to improve upon manually written tests, we re-used the existing test cases by modifying the search operators of EVOSUITE such that whenever a new test case was generated, it was based on an existing test case with a certain probability. Xu et al considered the reuse of test cases during test suite augmentation for DSE [37] or search-based and hybrid techniques [36], by using the old tests as starting population of the next test generation run; in this approach the success of the augmentation depends strongly on the previous tests.…”

Section: Seeding Previous Test Suitesmentioning

confidence: 99%

Continuous test generation

Campos

Arcuri

Fraser

et al. 2014

Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

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In object oriented software development, automated unit test generation tools typically target one class at a time. A class, however, is usually part of a software project consisting of more than one class, and these are subject to changes over time. This context of a class offers significant potential to improve test generation for individual classes. In this paper, we introduce Continuous Test Generation (CTG), which includes automated unit test generation during continuous integration (i.e., infrastructure that regularly builds and tests software projects). CTG offers several benefits: First, it answers the question of how much time to spend on each class in a project. Second, it helps to decide in which order to test them. Finally, it answers the question of which classes should be subjected to test generation in the first place. We have implemented CTG using the EVOSUITE unit test generation tool, and performed experiments using eight of the most popular open source projects available on GitHub, ten randomly selected projects from the SF100 corpus, and five industrial projects. Our experiments demonstrate improvements of up to +58% for branch coverage and up to +69% for thrown undeclared exceptions, while reducing the time spent on test generation by up to +83%.

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Section: Seeding Previous Test Suitesmentioning

confidence: 99%

Continuous test generation

Campos

Arcuri

Fraser

et al. 2014

Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

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show abstract

“…To address this problem, recently, researchers started working on test suite augmentation techniques which create new test cases for areas that have been affected by changes [17], [18], [19], [20], [21], [22]. Apiwattanapong et al [17] and Santelices and Harrold [18] presented a propagation-based approach which uses program dependence analysis and symbolic execution to identify areas affected by code changes, and then provides test requirements for changed software.…”

Section: Related Workmentioning

confidence: 99%

“…Apiwattanapong et al [17] and Santelices and Harrold [18] presented a propagation-based approach which uses program dependence analysis and symbolic execution to identify areas affected by code changes, and then provides test requirements for changed software. Xu et al [19], [20] presented an approach to generate test cases by utilizing the existing test cases and adapting concolic and genetic test case generation techniques. Taneja et al [21] proposed an efficient test generation technique that uses dynamic symbolic execution, eXpress, by pruning irrelevant paths.…”

Section: Related Workmentioning

confidence: 99%

Regression Testing for Web Applications Using Reusable Constraint Values

Hossain

Eda

2014

2014 IEEE Seventh International Conference on Software Testing, Verification and Validation Workshops

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Companies that provide web applications need to perform frequent regression testing because companies often encounter various security attacks and frequent feature update demands from users. Typically, such applications require regression testing processes that require minimal test effort because they have already been deployed and used in the field. In our previous work, we presented an efficient regression testing approach that allows us to focus on the areas of code that have been changed and to regression test them to address this problem. While our experiment results showed that this approach can be efficient in saving the cost of regression testing by reducing the number of test paths necessary for the modified program, we also learned that resolving input constraints requires a lot of effort. In this paper, to accommodate further savings with regression testing, we present a technique that identifies reusable constraint values for regression test cases. The technique finds variables where input values from the previous version can be reused to execute the regression test path for the new version. Our empirical results show that a large number of variable constraints can be reused from the previous version's test cases, thus we can reduce a great deal of effort for resolving constraint values for those variables when we test a new version of the application.

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“…Xu et al . iteratively apply DSE and SBST during test suite augmentation. Here, each application of DSE or search is expected to lead to coverage of new branches.…”

Section: Related Workmentioning

confidence: 99%

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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A Hybrid Directed Test Suite Augmentation Technique

Cited by 28 publications

References 36 publications

Continuous test generation

Continuous test generation

Regression Testing for Web Applications Using Reusable Constraint Values

Search‐based testing using constraint‐based mutation

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