English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This paper places its attention on a familiar phenomena: that code metrics such as lines of code are extremely context dependent and their distribution differs from project to project. We apply visual inspection, as well as statistical reasoning and testing, to show that such metric values are so sensitive to context, that their measurement in one project offers little prediction regarding their measurement in another project. On the positive side, we show that context bias can be neutralized, at least for the majority of metrics that we considered, by what we call Log Normal Standardization (LNS). Concretely, the LNS transformation is obtained by shifting (by subtracting the mean) and scaling (by dividing by the standard deviation) of the log of a metric value. Thus, we conclude that the LNS-transformed-, are to be preferred over the plain-, values of metrics, especially in comparing modules from different projects. Conversely, the LNS-transformation suggests that the “context bias” of a software project with respect to a specific metric can be summarized with two numbers: the mean of the logarithm of the metric value, and its standard deviation.

When do Software Complexity Metrics Mean Nothing? – When Examined out of Context.