ACCE

Coding style is important to teach to beginning programmers, so that bad habits don't become permanent. This is often done manually at the University level because automated Python static analyzers cannot accurately grade based on a given rubric. However, even manual analysis of coding style encounters problems, as we have seen quite a bit of inconsistency among our graders. We introduce ACCE--Automated Coding Composition Evaluator--a module that automates grading for the composition of programs. ACCE, given certain constraints, assesses the composition of a program through static analysis, conversion from code to AST, and clustering (unsupervised learning), helping automate the subjective process of grading based on style and identifying common mistakes. Further, we create visual representations of the clusters to allow readers and students understand where a submission falls, and the overall trends. We have applied this tool to CS61A--a CS1 level course at UC, Berkeley experiencing rapid growth in student enrollment--in an attempt to help expedite the involved process as well as reduce human grader inconsistencies.