Students taking introductory programming courses are typically required to complete assignments and expect timely feedback to advance their learning. With the current popularity of these courses in both traditional and online versions, graders are seeing themselves overwhelmed by the sheer amount of student programs they have to handle, and the quality of the educational experience provided is often compromised for promptness. Thus, there is a need for automated approaches to effectively increase grading productivity. Existing approaches in this context fail to support flexible grading schemes and customization based on the assignment at hand. This paper presents a data-driven approach for clustering recurrent program statements performing similar but not exact semantics across student programs, which we refer to as core statements. We rely on structural graph clustering over the program dependence graph representations of student programs. Such clustering is performed over the graph resulting from the pairwise approximate graph alignments of programs. Core statements help graders understand solution variations at a glance and, since they group program statements present in individual student programs, can be used to propagate feedback, thus increasing grading productivity. Our experimental results show that, on average, we discover core statements covering more than 50% of individual student programs, and that program statements grouped by core statements are semantically cohesive, which ensures effective grading.

Currently, there is a "boom" in introductory programming courses to help students develop their computational thinking skills. Providing timely, personalized feedback that makes students reflect about what and why they did correctly or incorrectly is critical in such courses. However, the limited number of instructors and the great volume of submissions instructors need to assess, especially in Massive Open Online Courses (MOOCs), prove this task a challenge. One solution is to hire graders or create peer discussions among students, however, feedback may be too general, incomplete or even incorrect. Automatic techniques focus on: a) Functional testing, in which feedback usually does not sufficiently guide novices, b) Software verification to find code bugs, which may confuse novices since these tools usually skip true errors or produce false errors, and c) Comparing using reference solutions, in which a large amount of reference solutions or pre-existing correct submissions are usually required. This paper presents a semantic-aware technique to provide personalized feedback that aims to mimic an instructor looking for code snippets in student submissions. These snippets are modeled as subgraph patterns with natural language feedback attached to them. Submissions are transformed into extended program dependence graphs combining control and data flows. We leverage subgraph matching techniques to compute the adequate personalized feedback. Also, constraints correlating patterns allow performing fine-grained assessments. We have evaluated our method on several introductory programming assignments and a large number of submissions. Our technique delivered personalized feedback in milliseconds using a small set of patterns, which makes it appealing in real-world settings.

As software projects evolve, carefully understanding the behavior of a program is mandatory before making any change. Repetitive code snippets also tend to appear throughout the codebase, and developers have to understand similar semantics multiple times. Building on this observation, we present Arcc: an Assistant for Repetitive Code Comprehension. The tool, implemented as an Eclipse plugin, assists developers in leveraging knowledge of a program to understand other programs containing a subset of the semantics in the former. Arcc differs from existing approaches in that it uses an extensible knowledge base of recurrent semantic code snippets, instead of heuristics or salient features, to summarize the behavior of a program. Given a program, we detect the occurrences of such snippets. Developers can create strategies as combinations of the snippets found and search for strategy occurrences in their workspace. Arcc highlights the source code related to every snippet and their interleaving, assisting in getting an intuition of similar programs. Finally, Arcc underlines potential common errors associated with the snippets, assisting in detecting overlooked problems. https://youtube.com/playlist?list=PLmizZtBESdPHDyKXKHMXj13r2pBCKzIoA