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

To make reasonable estimates of resources, costs, and schedules, software project managers need to be provided with models that furnish the essential framework for software project planning and control by supplying important “management numbers” concerning the state and parameters of the project that are critical for resource allocation. Understanding that software development is not a “mechanistic” process brings about the realization that parameters that characterize the development of software possess an inherent “fuzziness,” thus providing the rationale for the development of realistic models based on fuzzy set or neural theories.

Fuzzy systems and neural networks in software engineering project management

In recent times there has been some convergence of apparently disparate disciplines like engineering on one hand and social sciences on the other. A portable philosophy of systems analysis and design has evolved through progressive development from loop and node equations to state-space equations, from electrical networks to other physical systems, and from technological systems to transportation and economic systems. This paper addresses itself to the philosophy underlying the modelling side of system theory techniques that are sufficiently powerful to have important application in the planning, operation and control of complex large-scale systems required by modern society.

General systems from network systems: a philosophy of modelling

The paper describes the ‘ physical system ’ theoretic construction developed to model the Canadian economy at a level of ten sectors in each of five regions, but which has general applicability. The national economy is identified here as a collection of interconnected components and the approach taken is that of component-to-system construction. It is believed that the framework is general enough to embrace many ‘ input-output’ models in oconomic literature. Specifically, specialization to a few selected models is demonstrated.

A generalized system-theoretic framework for modelling large scale national economic systems in dynamic, structural and spatial terms†

Hopfield networks are a class of neural network models where non-linear graded response neurons organized into networks with effectively symmetric synaptic connections are able to implement interesting algorithms, thereby introducing the concept of information storage in the stable states of dynamical systems. In addition to opening up the possibility of using system dynamics as a vehicle to gain potentially useful insights into the behaviour of such networks, especially in the field or nonelectrical engineering, we study the dynamics of the state-space trajectory as well as time domain evolution of sensitivities of the states with respect to circuit parameters.

System dynamics simulation of Hopfield neural networks

The present paper develops a state-space model for an economic system viewed from a multi-dimensional standpoint of temporal, spatial and structural dimensions. As its distinctive features, the model maintains a distinction between supply and demand in a dynamic equilibrium framework by invoking the concept of inventory in a certain feedback sense and takes into account the non-linear economies (e.g. of scale) existing in production as well as transportation. The end result of this exercise IB a vastly general and powerful theoretical framework for economic system modelling which is believed to be amenable to empirical implementation even with the current status of available data base.

Prem S. Satsangi

Papers

Fuzzy systems and neural networks in software engineering project management

General systems from network systems: a philosophy of modelling

A generalized system-theoretic framework for modelling large scale national economic systems in dynamic, structural and spatial terms†

System dynamics simulation of Hopfield neural networks

Economic system : a multi-dimensional model