How Developers Visualize Compiler Messages: A Foundational Approach to Notification Construction

Barik, Titus; Lubick, Kevin; Christie, Samuel H.; Murphy-Hill, Emerson

doi:10.1109/vissoft.2014.24

Cited by 14 publications

(4 citation statements)

References 19 publications

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“…Presentation and Observation. To understand how existing presentations in IDEs do not align with the way developers self-explain errors to themselves, I conducted a pencil-and-paper think-aloud study in which developers selfexplained source code listings and error messages as presented by Eclipse [6]. 2 I asked participants to explain each compiler anomaly while making annotations on the source code during their explanation.…”

Section: Resultsmentioning

confidence: 99%

“…Explain this message. error messages should be reframed as assistive error explanations to better align and support the self-generated explanations [12], or self-explanations, that developers use to understand anomalies ( Figure 1) [6]. The significance of this work is that static analysis explanations have the potential to substantially improve developer comprehension of static analysis anomalies.…”

Section: Error List Outputmentioning

confidence: 99%

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How should static analysis tools explain anomalies to developers?

Barik

2016

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering

Self Cite

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Despite the advanced static analysis tools available within modern integrated development environments (IDEs), the error messages these tools produce remain perplexing for developers to comprehend. This research postulates that tools can computationally expose their internal reasoning processes to generate assistive error explanations that more closely align with how developers explain errors to themselves. CCS Concepts •Human-centered computing → Human computer interaction (HCI); •Software and its engineering → Software usability; •Theory of computation → Program analysis;

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Section: Resultsmentioning

confidence: 99%

Section: Error List Outputmentioning

confidence: 99%

How should static analysis tools explain anomalies to developers?

Barik

2016

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering

Self Cite

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“…Therefore, we classified the list of the specific applications based on the underlying data and the visualization techniques. For example, one study (Barik et al, 2014) elaborates on how developers comprehend compiler messages, which is a type of textual data, by providing a notification construction. Indeed, evaluation results are often used by developers to find explanations to development concerns.…”

Section: Classification By Information Visualization Techniquesmentioning

confidence: 99%

A Systematic Literature Review of Modern Software Visualization

Chotisarn,

Merino,

Zheng

et al. 2020

Preprint

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We report on the state-of-the-art of software visualization. To ensure reproducibility, we adopted the Systematic Literature Review methodology. That is, we analyzed 1440 entries from IEEE Xplore and ACM Digital Library databases. We selected 105 relevant full papers published in 2013-2019, which we classified based on the aspect of the software system that is supported (i.e., structure, behavior, and evolution). For each paper, we extracted main dimensions that characterize software visualizations, such as software engineering tasks, roles of users, information visualization techniques, and media used to display visualizations. We provide researchers in the field an overview of the state-of-the-art in software visualization and highlight research opportunities. We also help developers to identify suitable visualizations for their particular context by matching software visualizations to development concerns and concrete details to obtain available visualization tools.

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“…Becker's dissertation (Becker, 2015) attempted to enhance compiler error messages in order to improve student performance. Barik et al (Barik et al, 2014) studied how developers visualize compilation errors. They motivate their research with an example of a compiler misreporting the location of a fault.…”

Section: Prior Workmentioning

confidence: 99%

Finding and correcting syntax errors using recurrent neural networks

Santos

Campbell

Hindle

et al. 2017

Preprint

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Minor syntax errors are made by novice and experienced programmers alike; however, novice programmers lack the years of intuition that help them resolve these tiny errors. Standard LR parsers typically resolve syntax errors and their precise location poorly. We propose a methodology that helps locate where syntax errors occur, but also suggests possible changes to the token stream that can fix the error identified. This methodology finds syntax errors by checking if two language models "agree" on each token. If the models disagree, it indicates a possible syntax error; the methodology tries to suggest a fix by finding an alternative token sequence obtained from the models. We trained two LSTM (Long short-term memory) language models on a large corpus of JavaScript code collected from GitHub. The dual LSTM neural network model predicts the correct location of the syntax error 54.74% in its top 4 suggestions and produces an exact fix up to 35.50% of the time. The results show that this tool and methodology can locate and suggest corrections for syntax errors. Our methodology is of practical use to all programmers, but will be especially useful to novices frustrated with incomprehensible syntax errors.

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How Developers Visualize Compiler Messages: A Foundational Approach to Notification Construction

Cited by 14 publications

References 19 publications

How should static analysis tools explain anomalies to developers?

How should static analysis tools explain anomalies to developers?

A Systematic Literature Review of Modern Software Visualization

Finding and correcting syntax errors using recurrent neural networks

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