Debugging: finding, fixing and flailing, a multi-institutional study of novice debuggers

By Sue Fitzgerald, G. Lewandowski, R. McCauley, Laurie Murphy, B. Simon, L. Thomas, Carol Zander. 2008

227 citations recorded by the source corpus.

Subfields: Computer Science Applications, Developmental and Educational Psychology, Software, Program Analysis & Verification, Human-Computer Interaction

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Citations in the corpus

Graph-based, Self-Supervised Program Repair from Diagnostic Feedback (2020, background): “The type of each token, such as identifier (for a), operator (for =) and data type (for char), is recognized by the C/C++ tokenizer in Gupta et al. (2017). We then form the graph by connecting identical tokens in V with undirected edges (E) to capture the semantic correspondence.”
Improving Robot Controller Transparency Through Autonomous Policy Explanation (2017, background): “Novices in particular tend to have a difficult time both understanding the intended operation of a program and identifying connections between malformed logic and actual execution behavior [12].”
Review on teaching and learning of computational thinking through programming: What is next for K-12? (2014, background): “…researchers interested in the computational practice of testing and debugging which is one of the essential problem-solving skill in programming (Fitzgerald et al., 2008; McCauley et al., 2008), the pre-determined categories could be derived from the generic established Polya (1957)‘s 4-step of…”
Learning by Taking Apart: Deconstructing Code by Reading, Tracing, and Debugging (2016, background): “Researchers have studied the kinds of bugs that novice programmers make, their patterns of debugging behavior, technological aids for debugging, and affective factors such as the frustrations of debugging [22, 32, 35, 40, 43, 52].”
Students’ Misconceptions and Other Difficulties in Introductory Programming (2017, background): “…they often hold fragmentary knowledge instead of well-organized patterns, lack programming strategies, and fail to reason at an abstract level when comprehending, writing, and debugging code (Clancy and Linn 1999; Lister 2011; McCauley et al. 2008; Sajaniemi and Prieto 2005; Whalley et al. 2006).”
Common logic errors made by novice programmers (2018, background): “Similar results were reported in the work of Fitzgerald et al. [9] in which students were provided with fragments of bug-ridden code which they were required to correct.”