Managing the software process
Software Engineering Economics
Software Engineering Economics
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Concepts of operational software quality metrics
CSC '92 Proceedings of the 1992 ACM annual conference on Communications
Rules and Tools for Software Evolution Planning and Management
Annals of Software Engineering
Experiences with Behavioural Process Modelling in FEAST, and Some of Its Practical Implications
EWSPT '01 Proceedings of the 8th European Workshop on Software Process Technology
Evidence-Based Guidelines for Assessment of Software Development Cost Uncertainty
IEEE Transactions on Software Engineering
Modeling software evolution defects: a time series approach
Journal of Software Maintenance and Evolution: Research and Practice
Measuring and predicting software productivity: A systematic map and review
Information and Software Technology
Automatic mining of change set size information from repository for precise productivity estimation
Proceedings of the 2011 International Conference on Software and Systems Process
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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. 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.