Static Analysis of Code along with Autograders
Abstract
This work aims to study and develop approaches to help students and instructors of introductory programming courses (CS1 courses) in universities. These courses, which have significant dropout rates, are increasingly relevant in the academic environment, being offered not only to computer science students but also in the STEM learning context. At this moment, we wish to use automated code graders in order to obtain and show feedback beyond the result if a program passes or not its test cases. In that regard, static analysis of code is being done with students’ solutions to exercises in an introductory programming course at UNICAMP searching for simple comprehension problems with some metrics. In a general analysis, there is student code that uses up to seven times more conditional statements than the official solution and the class average to the same exercise. This kind of feedback can alert both the teacher and the student that this programming pattern can lead to future problems. Furthermore, this research proposal also has the intent of applying the feedback alongside Active Learning, in order to stimulate discussion and elevate student engagement in the classroom. Moreover, with those interventions set, we also want to evaluate the impact in the teacher’s workload, expecting it to be reduced.
Link to the presentation video (in brazillian portuguese): https://youtu.be/ghBTMRN2jEQ
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