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A practical guide to analyzing IDE usage data


Snipes, Will; Murphy-Hill, Emerson; Fritz, Thomas; Damevski, Kostadin; Vakilian, Mohsen; Nair, Anil; Shepherd, David (2015). A practical guide to analyzing IDE usage data. In: Bird, Christian; Menzies, Tim; Zimmermann, Thomas. Perspectives on Data Science for Software Engineering. Burlington, Massachusetts: Morgan Kaufmann/Elsevier, 85-136.

Abstract

Integrated development environments such as Eclipse and Visual Studio provide tools and capabilities to perform tasks such as navigating among classes and methods, continuous compilation, code refactoring, automated testing, and integrated debugging, all designed to increase productivity. Instrumenting the integrated development environment to collect usage data provides a more fine-grained understanding of developers' work than was previously possible. Usage data supports analysis of how developers spend their time, what activities might benefit from greater tool support, where developers have difficulty comprehending code, and whether they are following specific practices such as test-driven development. With usage data, we expect to uncover more nuggets of how developers create mental models, how they investigate code, how they perform mini trial-and-error experiments, and what might drive productivity improvements for everyone.

Abstract

Integrated development environments such as Eclipse and Visual Studio provide tools and capabilities to perform tasks such as navigating among classes and methods, continuous compilation, code refactoring, automated testing, and integrated debugging, all designed to increase productivity. Instrumenting the integrated development environment to collect usage data provides a more fine-grained understanding of developers' work than was previously possible. Usage data supports analysis of how developers spend their time, what activities might benefit from greater tool support, where developers have difficulty comprehending code, and whether they are following specific practices such as test-driven development. With usage data, we expect to uncover more nuggets of how developers create mental models, how they investigate code, how they perform mini trial-and-error experiments, and what might drive productivity improvements for everyone.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Date:2015
Deposited On:09 Aug 2016 06:47
Last Modified:14 Feb 2018 11:27
Publisher:Morgan Kaufmann/Elsevier
ISBN:9780128042069
OA Status:Closed
Other Identification Number:merlin-id:13566

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