Comments on "More Success and Failure Factors in Software Reuse"

Authors:
Maurizio Morisio;Michel Ezran;Colin Tully
Affiliations:
IEEE Computer Society;-;IEEE
Venue:
IEEE Transactions on Software Engineering
Year:
2003

Citing 2
Cited 2

Success and Failure Factors in Software Reuse

IEEE Transactions on Software Engineering
More Success and Failure Factors in Software Reuse

IEEE Transactions on Software Engineering

Mining extremely small data sets with application to software reuse

Software—Practice & Experience
Use of data mining in system development life cycle

Data Mining

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Abstract

For original paper see ibid., p. 474. This is a clear example of how research in software engineering can progress when empirical methods are applied. Menzies and Di Stefano apply a number of data mining tools to the data set. While, inmost cases, their results are in agreement with ours, in some cases they are not. Our first and main observation is that our interpretation of the data set is based not only on the data set itself but also on the knowledge gathered during the interviews with project members. The main problem with the data set is its size: 23 data points. Although this data set is the largest one available about reuse projects, it is too limited to base analysis only on data mining techniques; data mining is usually applied to data sets with thousands if not millions of data points.