Maintaining mental models: a study of developer work habits

By Thomas D. Latoza, Gina Venolia, R. Deline. International Conference on Software Engineering, 2006

779 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/852ab1852d930fd84af4f7bd0bbad5203df2ba21

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Citations in the corpus

What distinguishes great software engineers? (2019, background): “Consulting with team members to decide how best to implement a feature or to fix a bug is mentioned in nearly all studies that examine everyday activities of engineers (Ko et al. 2007; Latoza et al. 2006).”
Software Engineering for Machine Learning: A Case Study (2019, background): “15 years ago, many teams at Microsoft relied heavily on development triads consisting of a program manager (requirements gathering and scheduling), a developer (programming), and a tester (testing) [6].”
Autofolding for Source Code Summarization (2014, background): “Because of this complexity, programmers tend to spend more time reading and browsing code than actually writing it [1], [2].”
Measuring Program Comprehension: A Large-Scale Field Study with Professionals (2018, background): “identify working habit as a factor that impacts program comprehension [29].”
On the Use of Automated Text Summarization Techniques for Summarizing Source Code (2010, background): “Recent studies have shown that developers spend more time reading and navigating the code than writing it [2-3].”
Expectations, outcomes, and challenges of modern code review (2013, background): “knowledge from other members of their team [23].”
FASTDash: a visual dashboard for fostering awareness in software teams (2007, background): “These studies report that maintaining an awareness of a project’s status consumes a significant proportion of a programmer’s time [27-29]; one study reports programmers spend as much as 40% of their work day communicating about code [27].”
Investigating statistical machine learning as a tool for software development (2008, background): “Extensive prior research has explored the general difﬁculties faced by software developers [14, 15, 18, 24, 25], but little work has studied the application of statistical machine learning in software development.”
An Exploratory Study of How Developers Seek, Relate, and Collect Relevant Information during Software Maintenance Tasks (2006, background): “Because these parts are typically distributed throughout a system’s components and modules [18], [30], this exploration can be both time-consuming and difficult.”
Let's go to the whiteboard: how and why software developers use drawings (2007, neutral): “While we might expect a similar use of visual representations in such work, other research suggests that developers are bound to the written form of their code, and so source code editors are the mostused tools for design despite being considered less effective than paper or whiteboards [16].”
Learning to Repair Software Vulnerabilities with Generative Adversarial Networks (2018, background): “Thousands of such vulnerabilities are reported publicly to the Common Vulnerabilities and Exposures database (CVE) each year, and many more are discovered internally in proprietary code and patched [1, 2].”