A Tailored Domain Analysis Method for the Development of System-Specific Testing DSLs Enabling Their Smooth Introduction in Automotive Practice

Juhnke, Katharina; Matthias, Tichy

doi:10.1109/seaa.2019.00011

Cited by 2 publications

(4 citation statements)

References 29 publications

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“…In addition, a larger proportion of instructions remain that could not be assigned to a cluster. In contrast, using the Run Length distance for clustering leads to cluster results that are very similar to the results of manual clustering as described in Juhnke & Tichy (2019). In the end, we chose Eps = 0.333 as a suitable value for the clustering of instructions, which is also evident from the results of the clusterings performed in the evaluation (cf.…”

Section: Determining Suitable Parameters For Dbscan Algorithmmentioning

confidence: 78%

“…To obtain instructions, action and expected result descriptions must first be extracted from test case specifications, split, rectified, and, if necessary, transformed (cf. Juhnke & Tichy 2019). In this evaluation, the same instructions from action and expected result descriptions are used as input for manual grouping and automated clustering.…”

Section: Study Design and Data Collectionmentioning

confidence: 99%

“…Based on this, so-called conceptual templates (output) are derived which represent common parts as well as alternatives and optional parts of all instructions contained in a cluster. These conceptual templates are suitable to be used by domain engineers for the definition of Testing DSLs and to cover 70%-95% of a test case specification (see Juhnke & Tichy 2019).…”

Section: Introductionmentioning

confidence: 99%

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Clustering Natural Language Test Case Instructions as Input for Deriving Automotive Testing DSLs.

Juhnke¹,

Nikic²,

Tichy³

2021

JOT

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System testing is an important quality assurance technique in the area of automotive software development where predominantly natural language test cases are used for testing prototype vehicles. To ensure that these test cases are able to identify faults, the test cases themselves must also be of high quality. Testing DSLs can improve the quality of the test cases.To support the smooth introduction of Testing DSLs into the industry, the reuse of system-specific terminology and syntax of existing natural language test case specifications is recommended. Consequently, it is necessary to identify and cluster highly similar domain-specific instructions used in the action and expected result descriptions from those specifications. This is a key activity in automating the development of Testing DSLs to enable the application of grammar inference approaches.However, with an average of 400 -500 test cases per specification, this is a time-consuming task when executed manually.We present a clustering approach based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to automatically cluster similar instructions. Due to the special structure of the instructions used in our industrial test case specifications, we further developed a specific distance function required by the DBSCAN algorithm. Additionally, we determined appropriate values for the DBSCAN's parameters MinPts and Eps. Our evaluation on three industrial test case specifications shows that our approach is suitable for automatically clustering instructions from those test case specifications due to an almost perfect agreement (κ > 0.81) between clusters, created manually by experts, and the automatically created clusters. Furthermore, we show how to use the Multiple Sequence Alignment (MSA) approach to automatically derive grammar suggestions from the clustered instructions.

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Section: Determining Suitable Parameters For Dbscan Algorithmmentioning

confidence: 78%

Section: Study Design and Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Clustering Natural Language Test Case Instructions as Input for Deriving Automotive Testing DSLs.

Juhnke¹,

Nikic²,

Tichy³

2021

JOT

Self Cite

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“…Juhnke and Tichy define a domain analysis method for automotive systems test specification (Juhnke & Tichy 2019). The method applies to the analysis phase of the DSL development, to extract the domain-specific concepts from the existing textual 8 Foundational Subset for Executable UML Models 9 Action language for fUML test specifications of a specific automotive system.…”

Section: Ad-hoc Testing Facilities For Dslsmentioning

confidence: 99%

Adapting TDL to Provide Testing Support for Executable DSLs.

Khorram¹,

Bousse²,

Mottu³

et al. 2021

JOT

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Testing is one of the most prevalent and successful verification and validation (V&V) techniques used in the field of software engineering. While a large number of testing frameworks exist for general-purpose programming languages, providing testing facilities for any given executable Domain Specific Language (xDSL) remains a costly and challenging task. In this context, a standard such as the Test Description Language (TDL) appears as a suitable foundation for the definition of a generic testing approach for xDSLs. Unfortunately, TDL does not provide the domain-specific concepts required to write test cases for a given xDSL and does not include any model execution facilities. Our contribution addresses these limitations and thereby provides a fully generic testing approach for xDSLs based on TDL. Required TDL domain-specific concepts are automatically inferred from the xDSL definition through a model transformation into TDL. Model execution facilities are provided through the definition of a refined operational semantics for TDL. The application of our approach on 5 different xDSLs shows its generality and that it can successfully be used for testing executable models.

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A Tailored Domain Analysis Method for the Development of System-Specific Testing DSLs Enabling Their Smooth Introduction in Automotive Practice

Cited by 2 publications

References 29 publications

Clustering Natural Language Test Case Instructions as Input for Deriving Automotive Testing DSLs.

Clustering Natural Language Test Case Instructions as Input for Deriving Automotive Testing DSLs.

Adapting TDL to Provide Testing Support for Executable DSLs.

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