A Method to Assess and Argue for Practical Significance in Software Engineering
Journal article, 2021

A key goal of empirical research in software engineering is to assess practical significance, which answers whether the observed effects of some compared treatments show a relevant difference in practice in realistic scenarios. Even though plenty of standard techniques exist to assess statistical significance, connecting it to practical significance is not straightforward or routinely done; indeed, only a few empirical studies in software engineering assess practical significance in a principled and systematic way. In this paper, we argue that Bayesian data analysis provides suitable tools to assess practical significance rigorously. We demonstrate our claims in a case study comparing different test techniques. The case study's data was previously analyzed (Afzal et al., 2015) using standard techniques focusing on statistical significance. Here, we build a multilevel model of the same data, which we fit and validate using Bayesian techniques. Our method is to apply cumulative prospect theory on top of the statistical model to quantitatively connect our statistical analysis output to a practically meaningful context. This is then the basis both for assessing and arguing for practical significance. Our study demonstrates that Bayesian analysis provides a technically rigorous yet practical framework for empirical software engineering. A substantial side effect is that any uncertainty in the underlying data will be propagated through the statistical model, and its effects on practical significance are made clear. Thus, in combination with cumulative prospect theory, Bayesian analysis supports seamlessly assessing practical significance in an empirical software engineering context, thus potentially clarifying and extending the relevance of research for practitioners.

Bayesian analysis

Bayes methods

statistical significance

Decision making

empirical software engineering

Testing

Software engineering

Analytical models

Data models

Statistical analysis

practical significance

Author

Richard Torkar

University of Gothenburg

Carlo A Furia

Universita della Svizzera italiana

Robert Feldt

Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers), Software Engineering for Testing, Requirements, Innovation and Psychology

Francisco Gomes de Oliveira Neto

University of Gothenburg

Lucas Gren

University of Gothenburg

Per Lenberg

University of Gothenburg

Neil A. Ernst

University of Victoria

IEEE Transactions on Software Engineering

0098-5589 (ISSN)

Vol. In Press

Subject Categories

Software Engineering

DOI

10.1109/TSE.2020.3048991

More information

Latest update

1/21/2021