Estimating software project effort by analogy based on linguistic values

Idri, Ali; Abran, Alain; Khoshgoftaar, Taghi M.

doi:10.1109/metric.2002.1011322

Cited by 67 publications

(80 citation statements)

References 19 publications

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“…to cater for uncertainty in the latter models. One such approach is fuzzy analogy where fuzzy logic is combined with estimation by analogy [8]. Estimation by analogy is one of the popularly-used techniques classified under expert-based estimation method [18].…”

Section: Fuzzy Logic In Non-algorithmic Modelsmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

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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Section: Fuzzy Logic In Non-algorithmic Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhanced Software Development Effort and Cost Estimation Using Fuzzy Logic Model

Ling

Phang

et al. 2007

MJCS

View full text Add to dashboard Cite

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“…More recently, Idri and his colleagues Idri and Abran 2001;Idri et al 2002) have focused their work to date on two areas: the use of fuzzy logic in identifying similar observations to a target project, which can then be used as the basis of predictions for that project; and in fuzzy logic modeling of the COCOMO prediction method, particularly the specification of fuzzy membership functions for each of the cost drivers. 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.…”

Section: Modeling Aspects Of Software Project Managementmentioning

confidence: 99%

Software source code sizing using fuzzy logic modeling

MacDonell

2003

Information and Software Technology

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Accuracy Comparison of Analogy-Based Software Development Effort Estimation Techniques

Idri

Amazal

Abran

2015

Int. J. Intell. Syst.

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Estimation by analogy is a commonly used software effort estimation technique and a suitable alternative to other conventional estimation techniques: It predicts the effort of the target project using information from former similar projects. While it is relatively easy to handle numerical attributes, dealing with categorical attributes is one of the most difficult issues for analogy-based estimation techniques. Therefore, we propose, in this paper, a novel analogy-based approach, called 2FA-kprototypes, to predict effort when software projects are described by a mix of numerical and categorical attributes. To this aim, the well-known fuzzy k-prototypes algorithm is integrated into the process of estimation by analogy. The estimation accuracy of 2FA-kprototypes was evaluated and compared with that of two techniques: (1) classical analogy-based technique and (2) 2FAkmodes, which is a technique that we have developed recently. The comparison was performed using four data sets that are quite diverse and have different sizes: ISBSG, COCOMO, USP05-FT, and USP05-RQ. The results obtained showed that both 2FA-kprototypes and 2FA-kmodes perform better than classical analogy. C 2015 Wiley Periodicals, Inc.

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Estimating software project effort by analogy based on linguistic values

Cited by 67 publications

References 19 publications

Enhanced Software Development Effort and Cost Estimation Using Fuzzy Logic Model

Enhanced Software Development Effort and Cost Estimation Using Fuzzy Logic Model

Software source code sizing using fuzzy logic modeling

Accuracy Comparison of Analogy-Based Software Development Effort Estimation Techniques

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