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Customizing Feedback for Introductory Programming Courses Using Semantic Clusters 17th

Customizing Feedback for Introductory Programming Courses Us...

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17th International Conference on Intelligent Tutoring Systems (ITS)

作者： Marin, Victor J. Hosseini, Hadi Rivero, Carlos R. Rochester Inst Technol Rochester NY 14623 USA Penn State Univ State Coll PA USA

ISBN: (纸本)9783030804213;9783030804206

The number of introductory programming learners is increasing worldwide. Delivering feedback to these learners is important to support their progress;however, traditional methods to deliver feedback do not scale to thousands of programs. We identify several opportunities to improve a recent data-driven technique to analyze individual program statements. These statements are grouped based on their semantic intent and usually differ on their actual implementation and syntax. The existing technique groups statements that are semantically close, and considers outliers those statements that reduce the cohesiveness of the clusters. Unfortunately, this approach leads to many statements to be considered outliers. We propose to reduce the number of outliers through a new clustering algorithm that processes vertices based on density. Our experiments over six real-world introductory programming assignments show that we are able to reduce the number of outliers and, therefore, increase the total coverage of the programs that are under evaluation.

关键词： graph clustering approximate graph alignment

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Clustering Recurrent and Semantically Cohesive Program Statements in Introductory Programming Assignments 19

Clustering Recurrent and Semantically Cohesive Program State...

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28th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Marin, Victor J. Rivero, Carlos R. Rochester Inst Technol Rochester NY USA

ISBN: (纸本)9781450369763

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.

关键词： Program Dependence graph approximate graph alignment Structural graph Clustering

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Towards Summarizing Program Statements in Source Code Search 20

Towards Summarizing Program Statements in Source Code Search

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35th Annual ACM Symposium on Applied Computing (SAC)

作者： Marin, Victor J. Bansal, Iti Rivero, Carlos R. Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9781450368667

A common practice among programmers is to find pieces of source code using search engines. Programs retrieved by these engines are typically semantically but not necessarily syntactically similar. As a result, ranking methods are exploited to present relevant programs to users. However, due to implementation variability, users need to understand such programs. In this paper, we propose a method to group statements into clusters from a set of programs retrieved by a source code search engine. Each cluster comprises a number of program statements that have similar but not exact semantics and are pervasive. Our hypothesis is that such clusters help understand at a glance a set of semantically-related programs. We use approximate graph alignment to find correspondences among statements in two program dependence graphs that are similar with respect to their control and data flows, as well as operations they perform. We then build a graph with pairwise comparisons of program dependence graphs, and cast the problem of clustering statements as finding communities of statements that consistently align. Our evaluation using programs collected by BigCloneBench shows that clusters of statements discovered by our approach help discern implementation variations.

关键词： Source Code Search Program Dependence graph approximate graph alignment Community Detection

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