Learning to Prioritize Test Cases for Computer Aided Design Software via Quantifying Functional Units
Computer Aided Design (CAD) is a family of techniques that support the automation of designing and drafting 2D and 3D models with computer programs. CAD software is a software platform that provides the process from designing to modeling, such as AutoCAD or FreeCAD. Due to complex functions, the qua...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/20/10414 |
| _version_ | 1797475619214196736 |
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| author | Fenfang Zeng Shaoting Liu Feng Yang Yisen Xu Guofu Zhou Jifeng Xuan |
| author_facet | Fenfang Zeng Shaoting Liu Feng Yang Yisen Xu Guofu Zhou Jifeng Xuan |
| author_sort | Fenfang Zeng |
| collection | DOAJ |
| description | Computer Aided Design (CAD) is a family of techniques that support the automation of designing and drafting 2D and 3D models with computer programs. CAD software is a software platform that provides the process from designing to modeling, such as AutoCAD or FreeCAD. Due to complex functions, the quality of CAD software plays an important role in designing reliable 2D and 3D models. There are many dependencies between defects in CAD software. Software testing is a practical way to detect defects in CAD software development. However, it is expensive to frequently run all the test cases for all functions. In this paper, we design an approach to learning to prioritize test cases for the CAD software, called PriorCadTest. The key idea of this approach is to quantify functional units and to train a learnable model to prioritize test cases. The output of the approach is a sequence of existing test cases. We evaluate PriorCadTest on seven modules of an open-source real-world CAD project, ArtOfIllusion. The Average Percentage of Fault Detect (APFD) is used to measure the effectiveness. Experimental results show that the proposed approach outperforms the current industrial practice without test case prioritization. |
| first_indexed | 2024-03-09T20:46:44Z |
| format | Article |
| id | doaj.art-775a14b37a6b46438f65b4e13c05d223 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:46:44Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-775a14b37a6b46438f65b4e13c05d2232023-11-23T22:44:14ZengMDPI AGApplied Sciences2076-34172022-10-0112201041410.3390/app122010414Learning to Prioritize Test Cases for Computer Aided Design Software via Quantifying Functional UnitsFenfang Zeng0Shaoting Liu1Feng Yang2Yisen Xu3Guofu Zhou4Jifeng Xuan5Wuhan KM Information Technology Co., Ltd., Wuhan 430070, ChinaSchool of Computer Science, Wuhan University, Wuhan 430072, ChinaSchool of Computer Science, Wuhan University, Wuhan 430072, ChinaSchool of Computer Science, Wuhan University, Wuhan 430072, ChinaSchool of Computer Science, Wuhan University, Wuhan 430072, ChinaSchool of Computer Science, Wuhan University, Wuhan 430072, ChinaComputer Aided Design (CAD) is a family of techniques that support the automation of designing and drafting 2D and 3D models with computer programs. CAD software is a software platform that provides the process from designing to modeling, such as AutoCAD or FreeCAD. Due to complex functions, the quality of CAD software plays an important role in designing reliable 2D and 3D models. There are many dependencies between defects in CAD software. Software testing is a practical way to detect defects in CAD software development. However, it is expensive to frequently run all the test cases for all functions. In this paper, we design an approach to learning to prioritize test cases for the CAD software, called PriorCadTest. The key idea of this approach is to quantify functional units and to train a learnable model to prioritize test cases. The output of the approach is a sequence of existing test cases. We evaluate PriorCadTest on seven modules of an open-source real-world CAD project, ArtOfIllusion. The Average Percentage of Fault Detect (APFD) is used to measure the effectiveness. Experimental results show that the proposed approach outperforms the current industrial practice without test case prioritization.https://www.mdpi.com/2076-3417/12/20/10414CAD softwarecomputer aided designtest case prioritizationsoftware testingfeature engineeringmanufacturing software |
| spellingShingle | Fenfang Zeng Shaoting Liu Feng Yang Yisen Xu Guofu Zhou Jifeng Xuan Learning to Prioritize Test Cases for Computer Aided Design Software via Quantifying Functional Units Applied Sciences CAD software computer aided design test case prioritization software testing feature engineering manufacturing software |
| title | Learning to Prioritize Test Cases for Computer Aided Design Software via Quantifying Functional Units |
| title_full | Learning to Prioritize Test Cases for Computer Aided Design Software via Quantifying Functional Units |
| title_fullStr | Learning to Prioritize Test Cases for Computer Aided Design Software via Quantifying Functional Units |
| title_full_unstemmed | Learning to Prioritize Test Cases for Computer Aided Design Software via Quantifying Functional Units |
| title_short | Learning to Prioritize Test Cases for Computer Aided Design Software via Quantifying Functional Units |
| title_sort | learning to prioritize test cases for computer aided design software via quantifying functional units |
| topic | CAD software computer aided design test case prioritization software testing feature engineering manufacturing software |
| url | https://www.mdpi.com/2076-3417/12/20/10414 |
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