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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.

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Code clones are identical or similar code segments. The wide existence of code clones can increase the cost of maintenance and jeopardise the quality of software. The research community has developed many techniques to detect code clones, however, there is little evidence of how these techniques may perform in industrial use cases. In this paper, we aim to uncover the differences when such techniques are applied in industrial use cases. We conducted large scale experimental research on the performance of two state-of-the-art code clone detection techniques, SourcererCC and AutoenCODE, on both open source projects and an industrial project written in the Scala language. Our results reveal that both algorithms perform differently on the industrial project, with the largest drop in precision being 30.7%, and the largest increase in recall being 32.4%. By manually labelling samples of the industrial project by its developers, we discovered that there are substantially less Type-3 clones in the aforementioned project than that in the open source projects.