Foreword to the special section on negative results in software engineering

Foreword to the special section on negative results in software engineering Empir Software Eng (2017) 22:2453–2456 DOI 10.1007/s10664-017-9498-0 EDITORIAL Foreword to the special section on negative results in software engineering 1 2 3 Richard F. Paige · Jordi Cabot · Neil A. Ernst Published online: 27 January 2017 © Springer Science+Business Media New York 2017 Welcome to this special issue on Negative Results in Software Engineering. First, what do we mean by negative results? Negative or null results—that is, results which fail to show an effect—are all too uncommon in the published literature for many reasons, including pub- lication bias and self-selection effects. Such results are nevertheless important in showing the research directions that did not pay off. In particular, “replication cannot be meaningful without the potential acknowledgment of failed replications” (Ferguson and Heene 2012). Negative Results in Software Engineering We believe negative results are especially important in software engineering, in order to firmly embrace the nature of experimentation in software research, just like most of us believe industry should do. This means scientific inquiry that is conducted along Lean Startup (Ries 2014) principles: start small, use vali- dated learning and be prepared to ‘pivot’, or change course, if the learning outcome was negative. In this context, negative results are, http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Empirical Software Engineering Springer Journals

Foreword to the special section on negative results in software engineering

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Software Engineering/Programming and Operating Systems; Programming Languages, Compilers, Interpreters
ISSN
1382-3256
eISSN
1573-7616
D.O.I.
10.1007/s10664-017-9498-0
Publisher site
See Article on Publisher Site

Abstract

Empir Software Eng (2017) 22:2453–2456 DOI 10.1007/s10664-017-9498-0 EDITORIAL Foreword to the special section on negative results in software engineering 1 2 3 Richard F. Paige · Jordi Cabot · Neil A. Ernst Published online: 27 January 2017 © Springer Science+Business Media New York 2017 Welcome to this special issue on Negative Results in Software Engineering. First, what do we mean by negative results? Negative or null results—that is, results which fail to show an effect—are all too uncommon in the published literature for many reasons, including pub- lication bias and self-selection effects. Such results are nevertheless important in showing the research directions that did not pay off. In particular, “replication cannot be meaningful without the potential acknowledgment of failed replications” (Ferguson and Heene 2012). Negative Results in Software Engineering We believe negative results are especially important in software engineering, in order to firmly embrace the nature of experimentation in software research, just like most of us believe industry should do. This means scientific inquiry that is conducted along Lean Startup (Ries 2014) principles: start small, use vali- dated learning and be prepared to ‘pivot’, or change course, if the learning outcome was negative. In this context, negative results are,

Journal

Empirical Software EngineeringSpringer Journals

Published: Jan 27, 2017

References

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