Recognizing symbols in architectural CAD drawings is critical for various advanced engineering applications. In this paper, we propose a novel CAD data annotation engine that leverages intrinsic attributes from systematically archived CAD drawings to automatically generate high-quality annotations, thus significantly reducing manual labeling efforts. Utilizing this engine, we construct ArchCAD-400K, a large-scale CAD dataset consisting of 413,062 chunks from 5538 highly standardized drawings, making it over 26 times larger than the largest existing CAD dataset. ArchCAD-400K boasts an extended drawing diversity and broader categories, offering line-grained annotations. Furthermore, we present a new baseline model for panoptic symbol spotting, termed Dual-Pathway Symbol Spotter (DPSS). It incorporates an adaptive fusion module to enhance primitive features with complementary image features, achieving state-of-the-art performance and enhanced robustness. Extensive experiments validate the effectiveness of DPSS, demonstrating the value of ArchCAD-400K and its potential to drive innovation in architectural design and construction.

Similarly to production code, code smells also occur in test code, where they are called test smells. Test smells have a detrimental effect not only on test code but also on the production code that is being tested. To date, the majority of the research on test smells has been focusing on programming languages such as Java and Scala. However, there are no available automated tools to support the identification of test smells for Python, despite its rapid growth in popularity in recent years. In this paper, we strive to extend the research to Python, build a tool for detecting test smells in this language, and conduct an empirical analysis of test smells in Python projects.We started by gathering a list of test smells from existing research and selecting test smells that can be considered language-agnostic or have similar functionality in Python’s standard Unittest framework. In total, we identified 17 diverse test smells. Additionally, we searched for Python-specific test smells by mining frequent code change patterns that can be considered as either fixing or introducing test smells. Based on these changes, we proposed our own test smell called Suboptimal Assert. To detect all these test smells, we developed a tool called PYNOSE in the form of a plugin to PyCharm, a popular Python IDE. Finally, we conducted a large-scale empirical investigation aimed at analyzing the prevalence of test smells in Python code. Our results show that 98% of the projects and 84% of the test suites in the studied dataset contain at least one test smell. Our proposed Suboptimal Assert smell was detected in as much as 70.6% of the projects, making it a valuable addition to the list.