In our previous studies we’ve used simple summation when aggregating complexity measures. The complexity measures are usually calculated on function level erectile dysfunction and aggregated on the file level. An example is the McCabe complexity.
An example of our papers in this area is:
Antinyan, Vard, et al. “Identifying risky areas of software code in Agile/Lean software development: An industrial experience report.” Software Maintenance, Reengineering and Reverse Engineering (CSMR-WCRE), 2014 Software Evolution Week-IEEE Conference on. IEEE, 2014.
or this one:
Antinyan, Vard, et al. “Monitoring Evolution of Code Complexity and Magnitude of Changes.” Acta Cybernetica 21.3 (2014).
and this one:
Antinyan, Vard, et al. “Monitoring Evolution of Code Complexity in Agile/Lean Software Development.“
I was always Aciclovir without prescription wondering if the results are not biased by the mathematical operations which might not always have a reflection in the empirical world. Until I’ve come across this article which said that the summation is not that problematic after all.
Read the article http://arxiv.org/pdf/1503.08504.pdf: Assi, Rawad Abou. “Investigating the Impact of Metric Aggregation Techniques on Defect Prediction.” arXiv preprint arXiv:1503.08504 (2015).
Since the sample of projects was very small some replication is needed, but the results look quite promising and definitely Colchicine without prescription interesting.
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