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Identifying Metrics' Biases When Measuring or Approximating Size in Heterogeneous Languages.

Regina Hebig ; Jesper Derehag ; Michel Chaudron
Empirical Software Engineering and Measurement (ESEM 2015), ACM/IEEE International Symposium on. (1938-6451). p. 202-205. (2015)
[Konferensbidrag, refereegranskat]

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.



Denna post skapades 2015-12-16. Senast ändrad 2017-09-14.
CPL Pubid: 228503

 

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Institutionen för data- och informationsteknik (GU) (GU)

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Chalmers infrastruktur