Paper Info
Title
Identifying Metrics' Biases When Measuring or Approximating Size in Heterogeneous Languages
Abstract
Context: To compare the effectiveness of development techniques, the size of compared software systems needs to be taken into account. However, in industry new development techniques often come with changes in the applied programming languages. Goal: Our goal is to investigate how different size metrics and approximations are biased towards the languages c and c++. Further, we investigate whether triangulation of metrics has the potential to compensate for biases. Method: We identify crucial preconditions for a triangulation and investigate on 34 open source projects, whether a set of 16 size metrics fulfills these preconditions for the languages c and c++. Results: We identify how metrics differ in their biases and find that the preconditions for triangulation are fulfilled. Conclusion: Triangulation has the potential to address language biases, but high variance among metrics and tools need to be taken into account, too.
Year
DOI
Venue
2015
10.1109/ESEM.2015.7321201
2015 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)
Keywords
Field
DocType
metrics biases identification,heterogeneous languages,software systems,software development techniques,programming languages,size metrics,C language,C++ language,triangulation condition
Computer science,Theoretical computer science,Software system,Triangulation (social science)
Conference
ISSN
Citations 
PageRank 
1949-3770
0
0.34
References 
Authors
9
3
Name
Order
Citations
PageRank
Regina Hebig117924.24
Jesper Derehag211.40
Michel R. V. Chaudron369366.15