\textbf{Background and Context:} Struggling with programming assignments while learning to program is a common phenomenon in programming courses around the world. Supporting struggling students is a common theme in Computing Education Research (CER), where a wide variety of support methods have been created and evaluated. An important stream of research here focuses on program repair, where methods for automatically fixing erroneous code are used for supporting students as they debug their code. Work in this area has so far assessed the performance of the methods by evaluating the closeness of the proposed fixes to the original erroneous code. The evaluations have mainly relied on the use of edit distance measures such as the sequence edit distance and there is a lack of research on which distance measure is the most appropriate.

\noindent \textbf{Objectives:} In the present work, our objective is to provide insight into measures for quantifying the distance between erroneous code written by a student and a proposed change. We conduct the evaluation in an introductory programming context, where insight into the distance measures can provide help in choosing a suitable metric that can inform which fixes should be suggested to novices.

\noindent \textbf{Method:} A team of five experts annotated a subset of the Dublin dataset, creating solutions for over a thousand erroneous programs written by students. We evaluated how the prominent edit distance measures from the CER literature compare against measures used in Natural Language Processing (NLP) tasks for retrieving the experts’ solutions from a pool of proposed solutions. We also evaluated how the expert-generated solutions compare against the solutions proposed by common program repair algorithms. The annotated dataset and the evaluation code are published as part of the work.

\noindent \textbf{Findings:} Our results highlight that the ROUGE score, classically used for evaluating the performance of machine summarization tasks, performs well as an evaluation and selection metric for program repair. We also highlight the practical utility of NLP metrics, which allow an easier interpretation and comparison of the performance of repair techniques when compared to the classic methods used in the CER literature.

\noindent \textbf{Implications:} Our study highlights the variety of distance metrics used for comparing source codes. We find issues with the classically used distance measures that can be combated by using NLP metrics. Based on our findings, we recommend including NLP metrics, and in particular, the ROUGE metric, in evaluations when considering new program repair methodologies. We also suggest incorporating NLP metrics into other areas where source codes are compared, including plagiarism detection.