Software cost estimation with fuzzy models

Musílek, Petr; Pedrycz, Witold; Succi, Giancarlo; Reformat, Marek

doi:10.1145/373975.373984

Cited by 64 publications

(32 citation statements)

References 8 publications

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“…It has been found that the most accurate prediction models are based on analogy and experts opinion. Expertbased estimation was also found to be better than all regression-based models [3]. Henceforth the use of fuzzy logic in reusability prediction is desirable since expert knowledge can be incorporated into the fuzzy reusability prediction models.…”

Section: Fuzzy Techniquementioning

confidence: 93%

“…al. [3] to access the reusability of components but the author never applied the concept to use the approaches across the releases of components as well as not the neuro-fuzzy approach. It is proven in many proposed techniques that neuro-fuzzy give the better results as compare to standalone FIS or ANN because it uses the power of rules decision of FIS and adaptive nature of ANN in a single system together.…”

Section: Proposed Anfis Approachmentioning

confidence: 99%

See 1 more Smart Citation

Applying Neuro-fuzzy Approach to build the Reusability Assessment Framework across Software Component Releases - An Empirical Evaluation

Kumar¹,

Kumar²,

Sharma³

2013

IJCA

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To reduce the development time, software reuse methodologies have been used across the software industries. Software reuse is a method to assemble the software components from the existing software. To take advantage of reuse concept, it is necessary to measure the software reusability of the existing components. Although there are various statistical methods exists to find the reusability of the components but soft computing has not been explored for component reusability. The aim of this paper is to formulate, build, evaluate, validate and compare neuro-fuzzy approach in prediction of software reusability of software components during the subsequent releases of a software development process. In this research we have applied neuro-fuzzy approaches which yield to better accuracy than the standalone fuzzy and neural approach. We have taken four main dependent factors to estimate the reusability of software components. This proposed approach has also been validated against different releases of open source development. Also we have proposed a framework for component reusability Management in software component intermediate releases using the neuro-fuzzy approach. The analysis and results of the study shows that neuro-fuzzy provides better results as compare to Fuzzy Inference System and neural network but applicability of best approach depends on the data availability and the quantum of data.

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Section: Fuzzy Techniquementioning

confidence: 93%

Section: Proposed Anfis Approachmentioning

confidence: 99%

Applying Neuro-fuzzy Approach to build the Reusability Assessment Framework across Software Component Releases - An Empirical Evaluation

Kumar¹,

Kumar²,

Sharma³

2013

IJCA

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“…This approach does not appear to perform well over other datasets that are not structurally similar to COCOMO dataset, and it is no suitable for early stage estimation. Musflek et al (2000) developed a granular model for software cost estimation based on Fuzzy number called f-COCOMO. Both input (kilo line of code) and output (effort) are represented by their corresponding triangular Fuzzy numbers.…”

Section: Related Workmentioning

confidence: 99%

Analogy-based software effort estimation using Fuzzy numbers

Azzeh

Neagu

Cowling

2011

Journal of Systems and Software

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“…Musflek et al worked on fuzzifying basic COCOMO model without considering the adjustment factor [15]. In their simple f-COCOMO model, the size input into the COCOMO model is represented by a fuzzy set, while a and b coefficients are crisp values.…”

Section: Fuzzy Logic In Algorithmic Modelsmentioning

confidence: 99%

“…The use of fuzzy set supports continuous belongingness (membership) of elements to a given concept (such as small software project) [22] thus alleviating a dichotomy problem (yes/no) [15] that caused similar projects having different estimated efforts.…”

Section: Why Fuzzy Logic?mentioning

confidence: 99%

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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Software cost estimation with fuzzy models

Cited by 64 publications

References 8 publications

Applying Neuro-fuzzy Approach to build the Reusability Assessment Framework across Software Component Releases - An Empirical Evaluation

Applying Neuro-fuzzy Approach to build the Reusability Assessment Framework across Software Component Releases - An Empirical Evaluation

Analogy-based software effort estimation using Fuzzy numbers

Enhanced Software Development Effort and Cost Estimation Using Fuzzy Logic Model

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