A fuzzy logic based set of measures for software project similarity: validation and possible improvements

Idri, Ali; Abran, Alain

doi:10.1109/metric.2001.915518

Cited by 39 publications

(43 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Idri et. Al have implemented the COCOMO cost model using fuzzy logic in [1] and also a fuzzy logic based analogy estimation approach in [2][3][4]. Case based reasoning has also been used by Kadoda et.…”

Section: Background and Motivationmentioning

confidence: 99%

V Bhattacharya Analogy-Based Software Quality Prediction with Project Feature Weights

Rashid¹,

Patnaik²,

Bhattacherjee

2013

AJSEA

View full text Add to dashboard Cite

This paper presents analogy-based software quality estimation with project feature weights. The objective of this research is to predict the quality of project accurately and use the results in future predictions. The focus includes identifying parameters on which the quality of software depends. Estimation of rate of improvement of software quality chiefly depends on the development time. Assigning weights to these parameters to improve upon the results is also in the area of interest. In this paper two different similarity measures namely, Euclidian and Manhattan were the measures used for retrieving the matching cases from the knowledgebase to increases estimation accuracy & reliability. Expert judgment, weights and rating levels were used to assign weights and quality rating levels. The results show that assigning weights to software metrics increases the prediction performance considerably. In order to obtain the results, we have used indigenous tools.

show abstract

Section: Background and Motivationmentioning

confidence: 99%

V Bhattacharya Analogy-Based Software Quality Prediction with Project Feature Weights

Rashid¹,

Patnaik²,

Bhattacherjee

2013

AJSEA

View full text Add to dashboard Cite

show abstract

“…al in [22]have used unsupervised Learning techniques to build a software quality estimation system. Idri et al have implemented the COCOMO cost model using fuzzy logic in [1] and also a fuzzy logic based analogy estimation approach in [2][3][4]. T. M. Khoshgoftaar has described six software quality estimation techniques namely Regression Tree, Fuzzy Systems, Case-Based Reasoning, RuleBased Systems, Multiple Linear Regression and Neural Networks [20].…”

Section: Background and Related Workmentioning

confidence: 99%

Software Quality Estimation using Machine Learning: Case-based Reasoning Technique

Rashid¹,

Patnaik²,

Bhattacherjee³

2012

IJCA

View full text Add to dashboard Cite

Software quality estimation is one of the most interesting research areas in the domain of software engineering for last few decades. Large numbers of techniques and models have already been worked out in the area of error estimation. The aim of software quality estimation is to identify error prone tasks as the cost can be minimized with advance knowledge about the errors and this early treatment of error will enhance the software quality. In this paper we have explored a set of data in university setting. This paper advocates the use of case-based reasoning (i.e., CBR) to make a software quality estimation system by the help of human experts. CBR relies on historical information from similar past projects, whereby similarities are determined by comparing the projects, and key attributes. We have used different similarity measures to find the best method which increases estimation accuracy & reliability. This paper presents a work in which we have expanded our previous work [24]. The software is a console based application and thus does not use the GUI functions of the Operating System, which makes it very fast in execution. In order to obtain results we have used an indigenous tool for software quality estimation, run in c++ compiler. General TermsSoftware Engineering: Quality

show abstract

“…For effort estimation, CBR is based on the affirmation that similar software projects have similar costs. Earlier on, Idri et al proposed to measure the similarity between projects using categorical data represented by fuzzy set and to compute other measures using fuzzy reasoning [19]. Categorical data is a type of linguistic values.…”

Section: Fuzzy Logic In Non-algorithmic Modelsmentioning

confidence: 99%

Enhanced Software Development Effort and Cost Estimation Using Fuzzy Logic Model

Ling

Phang

et al. 2007

MJCS

View full text Add to dashboard Cite

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.

show abstract

A fuzzy logic based set of measures for software project similarity: validation and possible improvements

Cited by 39 publications

References 12 publications

V Bhattacharya Analogy-Based Software Quality Prediction with Project Feature Weights

V Bhattacharya Analogy-Based Software Quality Prediction with Project Feature Weights

Software Quality Estimation using Machine Learning: Case-based Reasoning Technique

Enhanced Software Development Effort and Cost Estimation Using Fuzzy Logic Model

Contact Info

Product

Resources

About