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Goto in C

Posted Jun 9, 2016 by Greg Wilson in Research

Meiyappan Nagappan, Romain Robbes, Yasutaka Kamei, Eric Tante, Shane McIntos4, Audris Mocku5,and Ahmed E. Hassa: "An Empirical Study of Goto in C Code from GitHub Repositories". ESEC/FSE’15, August 2015, http://www.se.rit.edu/~mei//publications/publications/FSE2015-Nagappan.pdf .

It is nearly 50 years since Dijkstra argued that goto obscures the flow of control in program execution and urged programmers to abandon the goto statement. While past research has shown that goto is still in use, little is known about whether goto is used in the unrestricted manner that Dijkstra feared, and if it is ‘harmful’ enough to be a part of a post-release bug. We, therefore, conduct a two part empirical study – (1) qualitatively analyze a statistically representative sample of 384 files from a population of almost 250K C programming language files collected from over 11K GitHub repositories and find that developers use goto in C files for error handling (80.21±5%) and cleaning up resources at the end of a procedure (40.36±5%); and (2) quantitatively analyze the commit history from the release branches of six OSS projects and find that no goto statement was removed/modified in the post-release phase of four of the six projects. We conclude that developers limit themselves to using goto appropriately in most cases, and not in an unrestricted manner like Dijkstra feared, thus suggesting that goto does not appear to be harmful in practice.

This abstract shows just how far software engineering has progressed in the last fifty years. Where one of founders of the field could only express a (strong) opinion, today’s researchers can gather data, analyze, and settle the question empirically.

But saying "how far software engineering has progressed" is misleading. What this paper and others presented on this blog actually show is how far the leading edge of empirical software engineering research has come. Most practitioners are still arguing from first principles and personal experience, just as Dijkstra did. But where he can be forgiven—the data needed to settle the issue either didn’t exist or wasn’t available in 1968—the only excuses today’s practitioners have are that they aren’t exposed to this kind of work as undergraduates, and can’t read much of it after graduation because it’s locked away behind paywalls. I’m grateful that this study’s authors chose not to do the second; now, how are we going to fix the first?

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