Predicting (individual) software productivity

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

19 Scopus Citations
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Author(s)

  • Watts S. Humphrey
  • Nozer D. Singpurwalla

Detail(s)

Original languageEnglish
Pages (from-to)196-207
Journal / PublicationIEEE Transactions on Software Engineering
Volume17
Issue number2
Publication statusPublished - Feb 1991
Externally publishedYes

Abstract

A method for projecting software productivity with reasonable accuracy which uses the statistical techniques of time series analysis is described. The measure of productivity is the development time required per line of code. In making productivity projections, the key issue is the need to achieve a balance between forecasting stability and responsiveness to changing conditions. Since software development is a highly varied and often dynamic process, there is a need for projection methods which are both reasonably responsive and highly reliable. The method described is directed at this need. An integrated moving average process of order one, using exponential smoothing of all the previous observations, is judged appropriate for software productivity analysis, particularly where there are limited data available or where conditions are sufficiently varied to make much of the available data inapplicable. Empirical evidence suggests that most commonly encountered time series can be reasonably well described by such methods. The methods for computing the weights used for exponential smoothing are described, as are the means for determining prediction intervals, or measures of forecast uncertainty. This data analytic approach uses historical data alone, unlike structural methods where learning curves as well as prior data are used to define the predictive process.

Citation Format(s)

Predicting (individual) software productivity. / Humphrey, Watts S.; Singpurwalla, Nozer D.

In: IEEE Transactions on Software Engineering, Vol. 17, No. 2, 02.1991, p. 196-207.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review