Using software complexity measures to analyze algorithms - an experiment with the shortest-paths algorithms

By Jukka Kalevi Nurminen. Computers & Operations Research, 2003

542 citations recorded by the source corpus.

In this paper, we apply different software complexity measures to a set of shortest-path algorithms. Our intention is to study what kind of new information about the algorithms the complexity measures (lines-of-code, Halstead's volume, and cyclomatic number) are able to give, to study which software complexity measures are the most useful ones in algorithm comparison, and to analyze when the software complexity comparisons are appropriate. The experiment indicates that the software complexity measures give a new dimension to empirical algorithm comparison. The results explicitly show the trade-off between speed and implementation complexity: a faster algorithm typically requires a more complex implementation. Different complexity measures correlate strongly. Therefore, even the simple lines-of-code measure gives useful results. As the software complexity measures are easy to calculate and since they give useful information, the study suggests that such measures should be included in empirical algorithm comparisons. Unfortunately, for meaningful results, all the algorithms have to be developed in the same fashion which makes the comparison of independent implementations difficult.

Subfields: Information Systems, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/59a857e62216299567ed3d17ae73c52efc3c190b

How researchers see this paper

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In Information Systems

1. G. Solomon
2. Yoshihisa Yamamoto
3. Darrick E. Chang
4. David Fattal
5. Charles M. Santori
6. Jelena Vuckovic

In Software

1. G. Solomon
2. Yoshihisa Yamamoto
3. Darrick E. Chang
4. David Fattal
5. Charles M. Santori
6. Jelena Vuckovic

In Signal Processing

1. G. Solomon
2. Yoshihisa Yamamoto
3. Darrick E. Chang
4. David Fattal
5. Charles M. Santori
6. Jelena Vuckovic

Citations in the corpus

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