Applying spectrum-based fault localization to generate debugging suggestions for student programmers

Edmison, Bob; Edwards, Stephen H.

doi:10.1109/issrew.2015.7392052

Cited by 7 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2. (SFL) Spectrum based fault localisation [7]. Test cases are run against a student's program, comparing its output to the expected output on various inputs.…”

Section: Themes Of Hint Generation Techniquesmentioning

confidence: 99%

“…Instead, the AST structure is considered so buggy patterns closer to the leaves take precedence over their ancestors. Similarly, in [7] (spectrum-based fault localisation) and [41], the student's output and execution trace is compared to the expected output and execution trace (respectively), and the differences are used to infer which parts of the program contain bugs. As such, the incorrect output and intermediate values are not directly reported, but rather the program parts associated with them.…”

Section: Syntaxmentioning

confidence: 99%

“…5.2). These can also be based on buggy patterns [38], constraints [15], output [7] or execution traces [41]. (See Sec.…”

Section: Steps → Next Stepsmentioning

confidence: 99%

“…Specifically, automated hints can help students progress in their learning by providing instant and relevant feedback to correct their mistakes and help them advance through exercises. In recent years, numerous techniques for producing programming hints have been developed, including approaches aimed at scaling up instructor feedback [1,2,3], extracting patterns from peer data [4,5,6], identifying particular dynamic or static issues with student programs [7], automatically generating personalised paths to solutions [8,9,10,11], or combinations of these [12,13].…”

Section: Introductionmentioning

confidence: 99%

“…While this diversity of approaches offers a great range of potential options for improving feedback, it also presents a difficult challenge -namely, it is difficult for instructors and researchers to decide which techniques are most effective for different situations. To address this issue, many studies have employed a range of different evaluation methods, including user studies and surveys [14,9,6,1,2], comparisons with experts [12,10], analysis using historical data [8,5,7,13,9,4,15,11,16,3] or other technical evaluations [15]. Researchers have also conducted comparison studies on small subsets of techniques [17,12].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Survey of Automated Programming Hint Generation -- The HINTS Framework

McBroom,

Koprinska,

Yacef

2019

Preprint

View full text Add to dashboard Cite

Automated tutoring systems offer the flexibility and scalability necessary to facilitate the provision of high quality and universally accessible programming education. In order to realise the full potential of these systems, recent work has proposed a diverse range of techniques for automatically generating hints to assist students with programming exercises. This paper integrates these apparently disparate approaches into a coherent whole. Specifically, it emphasises that all hint techniques can be understood as a series of simpler components with similar properties. Using this insight, it presents a simple framework for describing such techniques, the Hint Iteration by Narrow-down and Transformation Steps (HINTS) framework, and it surveys recent work in the context of this framework. It discusses important implications of the survey and framework, including the need to further develop evaluation methods and the importance of considering hint technique components when designing, communicating and evaluating hint systems. Ultimately, this paper is designed to facilitate future opportunities for the development, extension and comparison of automated programming hint techniques in order to maximise their educational potential.

show abstract

“…2. (SFL) Spectrum based fault localisation [7]. Test cases are run against a student's program, comparing its output to the expected output on various inputs.…”

Section: Themes Of Hint Generation Techniquesmentioning

confidence: 99%

Section: Syntaxmentioning

confidence: 99%

“…5.2). These can also be based on buggy patterns [38], constraints [15], output [7] or execution traces [41]. (See Sec.…”

Section: Steps → Next Stepsmentioning

confidence: 99%