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Journal ArticleDOI

Fuzzy systems and neural networks in software engineering project management

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TLDR
It is shown that the MBI selection process can be based upon 64 different fuzzy associative memory (FAM) rules, and the same rules are used to generate 64 training patterns for a feedforward neural network.
Abstract
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.

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Citations
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Software engineering economics

Barry Boehm
TL;DR: In this article, the authors provide an overview of economic analysis techniques and their applicability to software engineering and management, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.
Journal ArticleDOI

Cost estimation predictive modeling: regression versus neural network

TL;DR: 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.
Journal ArticleDOI

A Comparative Study of Cost Estimation Models for Web Hypermedia Applications

TL;DR: A comparison of the prediction accuracy of three CBR techniques used to estimate the effort to develop Web hypermedia applications and to choose the one with the best estimates is presented.
Journal ArticleDOI

A comparison of techniques for developing predictive models of software metrics

TL;DR: 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.
Journal ArticleDOI

Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects

TL;DR: A modified version of the famous COCOMO model provided to explore the effect of the software development adopted methodology in effort computation and two new model structures to estimate the effort required for the development of software projects using Genetic Algorithms.
References
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Journal ArticleDOI

Adaptive fuzzy systems for backing up a truck-and-trailer

TL;DR: The fuzzy systems performed well until over 50% of their fuzzy-associative-memory (FAM) rules were removed, and they also performed well when the key FAM equilibration rule was replaced with destructive, or ;sabotage', rules.
Journal ArticleDOI

On hidden nodes for neural nets

TL;DR: A proof is given that the maximum number of separable regions (M) in the input space is a function of both H and input space dimension (d).
Journal ArticleDOI

Planning the software industrial revolution

B.J. Cox
- 01 Nov 1990 - 
TL;DR: The author traces the US history of invention and technology transfer from the 1700s to the present and asks whether software development can stop being a process-centered cottage industry by the application of interchangeable parts technology.
Journal ArticleDOI

Measuring programming quality and productivity

T. C. Jones
- 01 Mar 1978 - 
TL;DR: An analysis of the commonly used units of measure in programming has revealed deficiencies in some units that lead to incorrect and even to paradoxial conclusions.
Dissertation

The dynamics of software development project management: An integrative systems dynamic perspective

TL;DR: A number of research questions are ""ripe'' for investigating including: (1) the efficacy of different organizational structures in different software development environments, (2) personnel turnover, (3) impact of management approaches such as management by objectives, and the organizational/environmental determinants of productivity.
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