Title

BugHint: A Visual Debugger based on Graph Mining

Abstract

Why doesn't my code work? Instructors for introductory programming courses frequently are asked that question. Often students understand the problem they are trying to solve well enough to specify a variety of input and output scenarios. However, they lack the ability to identify where the bug is occurring in their code. Mastering the use of a full-feature debugger can be difficult at this stage in their computer science education. But simply providing a hint as to where the problem lies may be sufficient to guide the student to add print statements or do a hand-trace focusing on a certain section of the code. Herein we present a software tool which, given a C++ program, some sample inputs, and respective expected outputs, uses graph mining to identify which lines in the program are most likely the source of a bug. The tool includes a visual display of the control flow graph for each test case, allowing the user to step through the statements executed. Experimental results from a group of CS1 students show that practice with this method: (1) makes students faster at finding bugs, (2) improves the way students test a program, and (3) improves program comments by students.

Meeting Name

24th International DMS Conference on Visualization and Visual Languages, DMSVIVA 2018 (2018: Jun. 29-30, Redwood City, CA)

Department(s)

Computer Science

Keywords and Phrases

C++ (programming language); Codes (symbols); Computer debugging; Data flow analysis; Data mining; Data visualization; Education computing; Flow graphs; Flow visualization; Program debugging; Software testing; Students; Visualization; Computer Science Education; Control flow graphs; Graph; Graph mining; Input and outputs; Introductory programming course; Most likely; Visual display; Visual languages; Debugging

International Standard Book Number (ISBN)

978-189170645-5

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2018 Knowledge Systems Institute Graduate School, All rights reserved.

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