A Systematic Approach to Automatically Derive Test Cases from Use Cases Specified in Restricted Natural Languages

Zhang, Man; Yue, Tao; Ali, Shaukat; Zhang, Huihui; Wu, Jianming

doi:10.1007/978-3-319-11743-0_10

Cited by 24 publications

(26 citation statements)

References 9 publications

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“…In addition, we examined other 30 TCSs in RTCM that were automatically generated from use case models by an approach proposed in our previous work [37]. Results show that RTCM notations are sufficient to specify TCSs from diverse case studies.…”

Section: Introductionmentioning

confidence: 94%

“…Moreover, the NL aspect of RTCM eases its adoption in practice. Automated generation of high-level TCSs from requirements is also feasible, as discussed in our previous work [37]. 2) Specify low-level TCSs to enable automated generation of manually executable test cases.…”

Section: Modeling Guidelinesmentioning

confidence: 99%

“…However, it is important to mention that in this paper, we propose aToucan4Test to bridge the gap between TCSs in RTCM and executable test cases. Though in [37], RTCM was only used as the means to represent the output of the transformation: TCSs, but it was not discussed as the means to enable the automated generation of either manually or automatically executable test cases.…”

Section: Requirements Based Testingmentioning

confidence: 99%

“…Our previous work [37] proposes an automated solution to generate TCSs (in RTCM) from use case models specified in RUCM. The transformation aims to enable automated requirements-based testing and establish automated traceability between requirements and TCSs.…”

Section: Requirements Based Testingmentioning

confidence: 99%

“…The generated TCMeta instances can then be automatically transformed into either manual test cases or automatically executable test cases. It is also important to mention that in our previous work [37], we have proposed an approach to transform requirements (specified in RUCM) into RTCM TCSs without API information.…”

Section: Introductionmentioning

confidence: 99%

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RTCM: a natural language based, automated, and practical test case generation framework

Yue

Ali

Zhang

2015

Proceedings of the 2015 International Symposium on Software Testing and Analysis

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Based on our experience of collaborating with industry, we observed that test case generation usually relies on test case specifications (TCSs), commonly written in natural language, specifying test cases of a System Under Test at a high level of abstraction. In practice, TCSs are commonly used by test engineers as reference documents to perform these activities: 1) Manually executing test cases in TCSs; 2) Manually coding test cases in a test scripting language for automated test case execution. In the latter case, the gap between TCSs and executable test cases has to be filled by test engineers, requiring a significant amount of coding effort and domain knowledge. Motivated by the above observations from the industry, we first propose, in this paper, a TCS language, named as Restricted Test Case Modeling (RTCM), which is based on natural language and composed of an easy-to-use template, a set of restriction rules and keywords. Second, we propose a test case generation tool (aToucan4Test), which takes TCSs in RTCM as input and generates either manual test cases or automatically executable test cases, based on various coverage criteria defined on RTCM. To assess the applicability of RTCM, we manually modeled two industrial case studies and examined 30 automatically generated TCSs. To evaluate aToucan4Test, we modeled three subsystems of a Video Conferencing System developed by Cisco Systems, Norway and automatically generated executable test cases. These test cases were successfully executed on two commercial software versions. In the paper, we also discuss our experience of applying RTCM and aToucan4Test in an industrial context and compare our approach with other model-based testing methodologies.

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Section: Introductionmentioning

confidence: 94%

Section: Modeling Guidelinesmentioning

confidence: 99%

Section: Requirements Based Testingmentioning

confidence: 99%

Section: Requirements Based Testingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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