Help

This application allows to inspect the distributions (probability densities) of a large corpus of metrics. It was conceived to demonstrate the significant differencs in these densities when they become conditional on the context. For example, the distribution of the TLOC metric differs vastly among the contexts "Programming language" and "Middleware".

In order to operationalize a metric in a given context, it is transformed into a score using some ideal value. Some metrics have an implicit ideal value. For example, the McCabe Cyclomatic Complexity has a lowest-possible (and desirable) ideal value of 1. This application allows to transform any metric into a distance using a user-chosen explicit ideal value. After transforming a metric into a distance, its distribution reflects the observed distances from the chosen ideal value. When we then proceed to obtaining the complementary cumulative distribution function, the CCDF, those distances can be translated into scores. The application also allows checking metrics values from own applications in specific contexts.

Elements


References

  1. Hönel, Sebastian, Morgan Ericsson, Welf Löwe, and Anna Wingkvist. "Contextual Operationalization of Metrics As Scores: Is My Metric Value Good?". 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS) 2022. https://doi.org/10.1109/QRS57517.2022.00042.
  2. Terra, Ricardo, Luis Miranda, Marco Valente, and Roberto Bigonha. "Qualitas.class Corpus." ACM SIGSOFT Software Engineering Notes 38, no. 5 (2013): 1–4. https://doi.org/10.1145/2507288.2507314.
  3. Tempero, Ewan, Craig Anslow, Jens Dietrich, Ted Han, Jing Li, Markus Lumpe, Hayden Melton, and James Noble. "The Qualitas Corpus: A Curated Collection of Java Code for Empirical Studies." 2010 Asia Pacific Software Engineering Conference, 2010. https://doi.org/10.1109/APSEC.2010.46.
  4. Stephens, M. A. "EDF Statistics for Goodness of Fit and Some Comparisons." Journal of the American Statistical Association 69, no. 347 (1974): 730–37. https://doi.org/10.1080/01621459.1974.10480196.
  5. Epps, T.W., and Kenneth J. Singleton. "An Omnibus Test for the Two-Sample Problem Using the Empirical Characteristic Function." Journal of Statistical Computation and Simulation 26, no. 3-4 (1986): 177—203. https://doi.org/10.1080/00949658608810963.