A systematic review of effect size in software engineering experiments

By Vigdis By Kampenes, Tore Dybå, Jo Erskine Hannay, Dag I.K. Sjøberg. Information and Software Technology, 2007

385 citations recorded by the source corpus.

Subfields: Developmental and Educational Psychology, Information Systems, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/658a89f31cd662e78aa0c4236b090894f07c0ba2

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In Developmental and Educational Psychology

In Information Systems

In Statistics and Probability

Citations in the corpus

A practical guide to controlled experiments of software engineering tools with human participants (2013, background): “…practice, such as the involvement of non-representative participants and the omission of key methodological details about the experimental setting and the population sampled (Zannier et al. 2006; Dieste et al. 2011; Kampenes et al. 2007; Glass et al. 2002; Hannay et al. 2007; Sjøberg et al. 2005).”
The Future of Empirical Methods in Software Engineering Research (2007, background): “periments there is a lack of power analysis [40] and effect size estimation [73].”
Systematic Mapping Studies in Software Engineering (2008, methodology): “It is worth noting that three of the reviews we found (Dyb˚a et al. 2006, Hannay et al. 2007, Kampenes et al. 2007) are based on the 103 articles identiﬁed in a single of the other studies (Sjøberg et al. 2005).”
A practical guide for using statistical tests to assess randomized algorithms in software engineering (2011, methodology): “To analyze such a property, effect size measures are needed [24, 28, 46].”
Reporting Experiments in Software Engineering (2008, methodology): “To this list, we add the alpha value and the confidence interval where appropriate (Dyba et al., 2006; Kampenes et al., 2007 ).”
Experimentation in Software Engineering (2012, background): “[92] give an overview of different effect size concepts and present a systematic review on how this is handled in published articles.”
A systematic review on regression test selection techniques (2010, background): “This is a problem identified in experimental studies in general [26].”
A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types (2011, methodology): “Table 11 provides an interpretation of the effect sizes estimated in this study, according to the categories proposed by Kampenes et al. (2007).”