Document Type

Article

Original Publication Date

2017

Volume

43

Issue

4

First Page

359

Last Page

371

DOI of Original Publication

10.1109/TSE.2016.2592905

Comments

Originally published at https://doi.org/10.1109/TSE.2016.2592905

Date of Submission

June 2017

Abstract

In this paper, we present a semi-automatic approach for mining a large-scale dataset of IDE interactions to extract usage smells, i.e., inefficient IDE usage patterns exhibited by developers in the field. The approach outlined in this paper first mines frequent IDE usage patterns, filtered via a set of thresholds and by the authors, that are subsequently supported (or disputed) using a developer survey, in order to form usage smells. In contrast with conventional mining of IDE usage data, our approach identifies time-ordered sequences of developer actions that are exhibited by many developers in the field. This pattern mining workflow is resilient to the ample noise present in IDE datasets due to the mix of actions and events that these datasets typically contain. We identify usage patterns and smells that contribute to the understanding of the usability of Visual Studio for debugging, code search, and active file navigation, and, more broadly, to the understanding of developer behavior during these software development activities. Among our findings is the discovery that developers are reluctant to use conditional breakpoints when debugging, due to perceived IDE performance problems as well as due to the lack of error checking in specifying the conditional.

Rights

© 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

Is Part Of

VCU Computer Science Publications

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