Novice programmers struggle with the complex syntax of modern programming languages likeJava, and make lot of syntax errors. The diagnostic syntax error messages from compilers and IDEs are sometimes useful, but often the messages are cryptic and puzzling. Novices could be helped, and instructors’ time saved, by automated repair suggestions when dealing with syntax errors. Large samples of novice errors and fixes are now available, offering the possibility of data-driven machine-learning approaches to help novices fix syntax errors. Current machine-learning approaches do a reasonable job fixing syntax errors in shorter programs, but don't work as well even for moderately longer programs. We introduceSynShine, a machine-learning based tool that substantially improves on the state-of-the-art, by learning to use compiler diagnostics, employing a very large neural model that leverages unsupervised pre-training, and relying on multi-label classification rather than autoregressive synthesis to generate the (repaired) output. We describeSynShine's architecture in detail, and provide a detailed evaluation. We have builtSynShineinto a free, open-source version of Visual Studio Code (VSCode); we make all our source code and models freely available.

One notable part of the academia-industry gap is the deficiency in computing ecosystem literacy, which may result in college graduates exhibiting little technical knowledge of software development tools and practices commonly used in industry. This paper presents our experience developing and teaching "The Missing CS Class," the student-led 1-unit course that we created at our university to address computing ecosystem literacy. This course primarily targets lower-division students and, based on our observations as peer tutors, covers four common but crucial gaps in technical knowledge: (1) Unix-like command-line environments and tools, (2) Software testing and debugging, (3) Scripting, and (4) Version control. Based on the collected feedback from two consecutive offerings of this course during the winter and spring quarters of 2021, most surveyed students reported having increased their self-efficacy on all course topics and incorporated them into their software development workflow.