Logistics, Affordances, and Evaluation of Build Programming: A Code Reading Instructional Strategy

Abstract

Computing students are expected to contribute to large unfamiliar codebases as they transition from university to industry settings. While computing courses provide students ample opportunities to write code independently or utilize abstract functionalities from standard libraries, students have fewer opportunities to read or extend codebases written by other programmers. This paper presents the logistics, affordances, and empirical evaluation of a novel instructional strategy, Build Programming, which is designed to promote code reading and extension in CS courses. In this strategy, a student (1) solves a programming problem, (2) is assigned a new codebase from a peer who solved the same problem, and (3) is asked to extend the assigned codebase to solve another problem. This allows a student to understand and extend an authentic codebase that is situated in a familiar context. In this paper, we shed light on the logistics of operationalizing this strategy in the context of an undergraduate Data Structures and Algorithms course (N=206). We also describe the affordances of this strategy through student experiences and evaluate the efficacy of one of these affordances, improving code quality through source code analysis. Most students (91%) proposed continuing Build Programming and students' code quality significantly improved after our strategy. Our findings underscore the benefits of Build Programming, and we hope that more instructors incorporate it in CS courses.