Abstract In model‐driven engineering (MDE) software projects, large portions of the executable code are automatically generated from designs and models. This generated code may or may not be edited by the developers to achieve their development objectives. MDE projects also include a significant amount of handwritten code (HC). This handwritten code is developed under unique constraints, as it must integrate with generated artifacts and code elements that are not directly developed by the engineers. These constraints adversely affect codebase quality and maintainability. This case study aims to investigate the hypothesis pertaining to the handwritten code quality developed in the context of MDE. The study analyzes these unique code fragments and compares their characteristics to handwritten code in repositories where code generation is not present. The study finds that handwritten code quality in the MDE context suffers from elevated technical debt and code smells. We observe key code smells that are particularly evident in this handwritten code. These findings imply that code generators must optimize for human comprehension, prioritize extensibility, and must facilitate integration with handwritten code elements.

We present the results of a survey of 228 software practitioners conducted on two phases ten years apart. The goal of the study is to uncover trends in the practice of software design and the adoption patterns of modeling languages such as UML. The first phase was conducted in April-December 2007 and included 113 responses. The second phase was conducted in March-November 2017 and included 115 responses. Both surveys were conducted online, employed identical solicitation mechanisms, and included the same set of questions. The survey results are analyzed within each phase and across phases. We present the results and analysis of the data identifying upward and downward trends in design and modeling practices. The results suggest some increase in formal and informal modeling and identify key challenges with modeling platforms and tools. The results can help researchers, practitioners, and educators to focus efforts on issues of relevance and significance to the profession.