This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

Cost estimation generally involves predicting labor, material, utilities or other costs over time given a small subset of factual data on “cost drivers.” Statistical models, usually of the regression form, have assisted with this projection. Artificial neural networks are non-parametric statistical estimators, and thus have potential for use in cost estimation modeling. This research examined the performance, stability and ease of cost estimation modeling using regression versus neural networks to develop cost estimating relationships (CERs). Results show that neural networks have advantages when dealing with data that does not adhere to the generally chosen low order polynomial forms, or data for which there is little a priori knowledge of the appropriate CER to select for regression modeling. However, in cases where an appropriate CER can be identified, regression models have significant advantages in terms of accuracy, variability, model creation and model examination. Both simulated and actua...

Cost estimation predictive modeling: regression versus neural network

A production-inventory model with probabilistic deterioration in two-echelon supply chain management

Software cost models and effort estimates help project managers allocate resources, control costs and schedule and improve current practices, leading to projects finished on time and within budget. In the context of Web development, these issues are also crucial, and very challenging given that Web projects have short schedules and very fluidic scope. In the context of Web engineering, few studies have compared the accuracy of different types of cost estimation techniques with emphasis placed on linear and stepwise regressions, and case-based reasoning (CBR). To date only one type of CBR technique has been employed in Web engineering. We believe results obtained from that study may have been biased, given that other CBR techniques can also be used for effort prediction. Consequently, the first objective of this study is to compare the prediction accuracy of three CBR techniques to estimate the effort to develop Web hypermedia applications and to choose the one with the best estimates. The second objective is to compare the prediction accuracy of the best CBR technique against two commonly used prediction models, namely stepwise regression and regression trees. One dataset was used in the estimation process and the results showed that the best predictions were obtained for stepwise regression.

A Comparative Study of Cost Estimation Models for Web Hypermedia Applications

The use of regression analysis to derive predictive equations for software metrics has recently been complemented by increasing numbers of studies using non-traditional methods, such as neural networks, fuzzy logic models, case-based reasoning systems, and regression trees. There has also been an increasing level of sophistication in the regression-based techniques used, including robust regression methods, factor analysis, and more effective validation procedures. This paper examines the implications of using these methods and provides some recommendations as to when they may be appropriate. A comparison of the various techniques is also made in terms of their modelling capabilities with specific reference to software metrics.

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A comparison of techniques for developing predictive models of software metrics

The objective of the analysis is to present a testable control system model of the processes determining the fraction of total inputs into a national economy accounted for by imported inputs. Although such processes are complex, they are of primo importance in long term stability studies of national balance of payments. It is observed that the proportion of imported inputs to total inputs will be determined primarily by the state of production capacity on the one hand and by the dynamics of the domestic price level, on the other. Therefore, the model is based upon some control considerations relating to the economic mechanism via which these two forces may cause changes in the fraction, imported inputs/total inputs. A describing-function technique is used to model non-linearities of market behaviour, and the model is tested by comparing the observed fractions against the predicted ones for the case of the Canadian economy. An outstanding feature of the empirical validation is that independently d...

Prem S. Satsangi

Papers

A control system excitation-response model for import-proportion in a national economy†