A systematic review of statistical power in software engineering experiments

By Tore Dybå, Vigdis By Kampenes, Dag I.K. Sjøberg. Information and Software Technology, 2006

312 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems

View Paper: https://www.semanticscholar.org/paper/988613a84f434a0ec95b0150103304aac7ad8119

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Citations in the corpus

A systematic review of effect size in software engineering experiments (2007, background): “Hence, as also recommended by others [12,23,29], effect sizes should be part of experimental results in software engineering.”
A method for evaluating rigor and industrial relevance of technology evaluations (2011, background): “The main arguments center around a lack of empirical evaluations (Tichy et al. 1995; Glass et al. 2002), poor study execution (Kitchenham et al. 2002; Dybå et al. 2006) and a lack of realism when performed (Sjøberg et al. 2002).”
A practical guide for using statistical tests to assess randomized algorithms in software engineering (2011, background): “, [15, 28]) in the phenomena under study, the use of statistical tests is also required but the constraints we work with are quite different from those of randomized algorithms, such as for example the size of data samples and the types of distributions.”
The Future of Empirical Methods in Software Engineering Research (2007, background): “Systematic reviews are one of the key building blocks of evidence-based SE [41, 70, 76], and the interest in conducting such reviews within SE is clearly growing [40, 56, 65, 66, 123], although the coverage of SE topics by systematic reviews is still in its infancy and very limited.”
The New C Standard (Excerpted material) An Economic and Cultural Commentary (2008, unknown): “6% used students only as subjects) and even then the statistical power of these experiments fell below expected norms.[103]”
Systematic literature reviews in software engineering - A systematic literature review (2009, background): “[4] 2006 Research trends Power in SE e”
Using mapping studies as the basis for further research - A participant-observer case study (2011, unknown): “This set of papers has been used in numerous subsequent systematic reviews that have investigated aspects of empirical software engineering (i.e. [19] [20] [21] [22] [23]).”
Experimentation in Software Engineering (2012, methodology): “[49] reviewed the statistical power in software engineering experiments, and Hannay et al.”
A practical guide to controlled experiments of software engineering tools with human participants (2013, background): “A more principled way to decide how many participants to recruit is to do a prospective power analysis (e.g., Dybå et al. 2006), which gives an estimate of the sample size required to detect a difference between experimental conditions.”
Evaluating Pair Programming with Respect to System Complexity and Programmer Expertise (2007, methodology): “We did not calculate the effect size f from the first phase of this experiment (because the experiment used a different main treatment) but based it on a medium effect size f ¼ 0:25 (as suggested by Dyba u et al. [ 16 ]) for all the three variables and interactions.”
A Systematic Review of the Application and Empirical Investigation of Search-Based Test Case Generation (2010, methodology): “Based on the expected (minimum) difference between techniques (if this can be estimated) and the statistical tests used to compare cost and effectiveness across techniques, the minimum required number of runs can be estimated using power analysis [18].”
Lessons from applying the systematic literature review process within the software engineering domain (2007, unknown): “Another example, where new insights have emerged, is a systematic review of statistical power in software engineering experiments (Dyba˚ et al., 2006).”
A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering (2014, background): “When human resources and factors introduce randomness (e.g., [33, 63]) in the phenomena under study, the use of statistical tests is also required.”
Systematic Mapping Studies in Software Engineering (2008, methodology): “TABLE 4: Systematic Reviews Included ID Reference ID Reference 1 (Dybå et al. 2006) 6 (Mendes 2005) 2 (Grimstad et al.”