Effective feedback is often timely and consistent but, with large cohorts, this is not always achievable. This study explored the potential of GPT4 to generate feedback on code readability for students enrolled in a CS1 Java course. We developed rubrics based on three readability criteria: naming, commenting, and formatting. We defined feedback criteria and incorporated them into GPT4 prompts to guide feedback generation. Results were mixed: while some feedback messages closely aligned with the rubrics, offering valuable insights, others fell short in providing corrective guidance. This highlights the potential and limitations of using LLMs to generate feedback on code readability. Future research could refine these methods to improve feedback consistency and quality.

Patterns and pattern catalogs (pattern languages) have been proposed as a mechanism for re-use. Traditionally, patterns have been used to foster design re-use, and generative design patterns have been used to achieve both design and code re-use. In theory, a pattern catalog could be created and used to provide re-usable patterns within a project and across a group of related projects. This idea raises a natural question. How can we measure the effectiveness of a pattern catalog or compare the effectiveness of different pattern catalogs? In this paper, we define four metrics that can be used to measure the effectiveness of pattern catalogs. We illustrate these metrics by applying them to a case study that uses a pattern catalog of generative design patterns to generate scripting code for computer games. The metrics are general enough to assess any pattern catalog, independent of application domain or whether the patterns are generative or descriptive.