Applications of fuzzy logic to software metric models for development effort estimation

Gray, Andrew; MacDonell, Stephen G.

doi:10.1109/nafips.1997.624073

Cited by 42 publications

(30 citation statements)

References 11 publications

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“…Although their published empirical analyses are not extensive (given the early stage of the research), their work does highlight the significant potential in fuzzy logic modeling when applied to aspects of software project management. Our own work (Gray and MacDonell 1997b;MacDonell et al 1999) has similarly indicated the applicability of fuzzy logic methods to this domain -one of the aims of the work described here is to provide empirical evidence in support of this.…”

Section: Modeling Aspects Of Software Project Managementmentioning

confidence: 61%

Software source code sizing using fuzzy logic modeling

MacDonell

2003

Information and Software Technology

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Section: Modeling Aspects Of Software Project Managementmentioning

confidence: 61%

Software source code sizing using fuzzy logic modeling

MacDonell

2003

Information and Software Technology

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“…Study showed that fuzzy logic model has a place in software effort estimation [2]. Using real project data, Gray and MacDonell compared Function Point Analysis, Regression techniques, feedforward neural network and fuzzy logic in software effort estimation.…”

Section: Fuzzy Logic In Software Effort Estimationmentioning

confidence: 99%

“…There are a number of problems with the existing effort estimation models [2,23,24,14]. Firstly most prediction models required highly accurate estimates to be supplied as input into the models.…”

Section: Why Fuzzy Logic?mentioning

confidence: 99%

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Enhanced Software Development Effort and Cost Estimation Using Fuzzy Logic Model

Ling

Phang

et al. 2007

MJCS

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Keywords: Fuzzy logic, cost estimation INTRODUCTIONSoftware metric refers to the measurement of software attributes which are typically related to the product, the process and the resources of software development [1]. These measurements can be used as parameters in project management models [2] which provide aids to software project managers in the daunting task of managing software projects to avoid problems such as cost overrun and behind the schedule.One of the most extensively researched areas of software measurement is software effort prediction. Software effort prediction models fall into two main categories: algorithmic and non-algorithmic. The most popular algorithmic prediction models include Boehm's COCOMO [3], Putnam's SLIM [4] and Albrecht's Function Point [5]. These models require as inputs, accurate estimate of certain attributes such as line of code (LOC), complexity and so on which are difficult to obtain during the early stage of a software development project. The models also have difficulty in modeling the inherent complex relationships between the contributing factors, are unable to handle categorical data as well as lack of reasoning capabilities [6]. The limitations of algorithmic models led to the exploration of the non-algorithmic techniques which are soft computing based. These include artificial neural network, evolutionary computation, fuzzy logic models, case-based reasoning and so on.Artificial neural network are used in effort estimation due to its ability to learn from previous data [7][8]. It is also able to model complex relationships between the dependent (effort) and independent variables (cost drivers) [7][8].In addition, it has the ability to generalize from the training data set thus enabling it to produce acceptable result for previously unseen data. Most of the work in the application of neural network to effort estimation made use of feed-forward multi-layer Perceptron, Backpropagation algorithm and sigmoid function [7].Despite of these, neural network's limitations in several aspects prevent it from being widely adopted in effort prediction [7]. It is a 'black box' approach and therefore it is difficult to understand what is going on internally within a neural network. Hence, justification of the prediction rationale is tough. Neural network is known of its ability in tackling classification problem. Contrarily, in effort estimation what is needed is generalization capability. At the same time, there is little guideline in the construction of neural network topologies. On the other hand, genetic programming was used in effort estimation by Burgess et al [9] and the use of case-based reasoning in effort estimation can be found in [8]. The work presented here focused on the application of fuzzy logic in effort prediction.

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“…Fuzzy logic has recently gained a greater amount of attention in the software metrics literature as a means for solving some of these long-standing problems [2,3]. Obviously fuzzy logic is not a panacea for all of project management's ills.…”

Section: Fuzzy Logic For Software Metric Modelsmentioning

confidence: 99%