Moving beyond the mean : analyzing variance in software engineering experiments
Santos, Adrian; Oivo, Markku; Juristo, Natalia (2018-11-03)
Santos A., Oivo M., Juristo N. (2018) Moving Beyond the Mean: Analyzing Variance in Software Engineering Experiments. In: Kuhrmann M. et al. (eds) Product-Focused Software Process Improvement. PROFES 2018. Lecture Notes in Computer Science, vol 11271. Springer, Cham
© Springer Nature Switzerland AG 2018. This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science, vol 11271. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-03673-7_13.
Software Engineering (SE) experiments are traditionally analyzed with statistical tests (e.g., t-tests, ANOVAs, etc.) that assume equally spread data across groups (i.e., the homogeneity of variances assumption). Differences across groups’ variances in SE are not seen as an opportunity to gain insights on technology performance, but instead, as a hindrance to analyze the data. We have studied the role of variance in mature experimental disciplines such as medicine. We illustrate the extent to which variance may inform on technology performance by means of simulation. We analyze a real-life industrial experiment on Test-Driven Development (TDD) where variance may impact technology desirability. Evaluating the performance of technologies just based on means—as traditionally done in SE—may be misleading. Technologies that make developers obtain similar performance (i.e., technologies with smaller variances) may be more suitable if the aim is minimizing the risk of adopting them in real practice.
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