Artificial Intelligence

   

Mining Software Metrics from Jazz

Authors: Jacqui Finlay, Andy M. Connor, Russel Pears

In this paper, we describe the extraction of source code metrics from the Jazz repository and the application of data mining techniques to identify the most useful of those metrics for predicting the success or failure of an attempt to construct a working instance of the software product. We present results from a systematic study using the J48 classification method. The results indicate that only a relatively small number of the available software metrics that we considered have any significance for predicting the outcome of a build. These significant metrics are discussed and implication of the results discussed, particularly the relative difficulty of being able to predict failed build attempts.

Comments: 7 Pages. 9th International Conference on Software Engineering Research, Management and Applications

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Submission history

[v1] 2014-08-04 03:55:23

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