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Proceedings ArticleDOI

FULSOME: a fuzzy logic modeling tool for software metricians

10 Jun 1999-pp 263-267
TL;DR: While there are many tools available for developing fuzzy models, it is suggested that before there will be real adoption of such techniques by project managers there will need to be suitable tools that support their particular workflows and that use appropriate terminology.
Abstract: There has been a growing body of literature suggesting that some of the problems faced by software development project managers can be at least partially overcome by using fuzzy logic techniques. However, one issue that has been generally overlooked in this recommendation is the means by which these "software metricians" can collect data for, develop, and interpret fuzzy logic models in practice. We describe a freely available system that has been built with this in mind called FULSOME (FUzzy Logic for SOftware MEtrics). While there are many tools available for developing fuzzy models, it is suggested that before there will be real adoption of such techniques by project managers there will need to be suitable tools that support their particular workflows and that use appropriate terminology. Another requirement will be the development of some standard procedures and definitions for such models. Issues involved with membership function elicitation and extraction are also discussed as a first step towards this second goal.

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Citations
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Proceedings ArticleDOI
28 Dec 2009
TL;DR: This work aims to propose a fuzzy logic realistic model to achieve more accuracy in software effort estimation, by applying fuzzy logic and using training procedure to the system, the accuracy of the results is desirable in comparison with the famous traditional algorithmic technique, COCOMO II model.
Abstract: Software development effort estimation is the process of predicting the most realistic use of effort required for developing software based on some parameters. It has always characterised one of the biggest challenges in Computer Science for the last decades. Because time and cost estimate at the early stages of the software development are the most difficult to obtain, and they are often the least accurate. Traditional algorithmic techniques such as regression models, Software Life Cycle Management (SLIM), COCOMO model, function points, etc, require an estimation process in a long term. But, nowadays that is not acceptable for software developers and companies. Newer soft computing techniques to effort estimation based on non-algorithmic techniques such as Fuzzy Logic (FL) may offer an alternative for solving the problem. This work aims to propose a fuzzy logic realistic model to achieve more accuracy in software effort estimation. In this innovative model, by applying fuzzy logic and using training procedure to the system, the accuracy of the results is desirable in comparison with the famous traditional algorithmic technique, COCOMO II model. This novelty model will lead researchers to focus on non-algorithmic models to overcome the estimation problems. Our validation experiment was carried out on artificial dataset as well as the COCOMO standard dataset.

11 citations

Proceedings ArticleDOI
01 Oct 2013
TL;DR: This article presents one approach to automatically create the RTM based on fuzzy logic, called RTM-Fuzzy, which combines two other approaches, one based on functional requirements' entry data and the other based on natural language processing - calledRTM-NLP.
Abstract: Background: The Requirements Trace ability Matrix (RTM) is one of the most commonly used ways to represent requirements trace ability. Nevertheless, the difficulty of manually creating such a matrix motivates the investigation into alternatives to generate it automatically. Objective: This article presents one approach to automatically create the RTM based on fuzzy logic, called RTM-Fuzzy, which combines two other approaches, one based on functional requirements' entry data - called RTM-E - and the other based on natural language processing - called RTM-NLP. Method: To create the RTM based on fuzzy logic, the RTM-E and RTM-NLP approaches were used as entry data for the fuzzy system rules. Aimed at evaluating these approaches, an experimental study was conducted where the RTMs created automatically were compared to the reference RTM (oracle) created manually based on stakeholder knowledge. Results: On average the approaches matched the following results in relation to the reference RTM: RTM-E achieved 78% effectiveness, RTM-NLP 76% effectiveness and the RTM-Fuzzy 83% effectiveness. Conclusions: The results show that using fuzzy logic to combine and generate a new RTM offered an enhanced effectiveness for determining the requirement's dependencies and consequently the requirement's trace ability links.

10 citations

Journal ArticleDOI
01 Nov 2002
TL;DR: A role of Computational Intelligence (CI) and visual computing being viewed as a sound methodological and algorithmic environment for knowledge-oriented Software Engineering is discussed and a number of selected and most visible trends occurring at the junction of CI and Software Engineering are highlighted.
Abstract: The discipline of Software Engineering is abstract and complex with all its endeavors being cast in a knowledge-intensive environment. It is not surprising that there have been a number of important initiatives that have attempted to address a burning need for solid development tools and comprehensive environments supporting an in-depth analysis. The objective of this study is to discuss a role of Computational Intelligence (CI) and visual computing being viewed as a sound methodological and algorithmic environment for knowledge-oriented Software Engineering. The CI itself is regarded as a synergistic consortium of granular computing (including fuzzy sets) promoting abstraction, neurocomputing supporting various learning schemes and evolutionary computing providing important faculties of global optimization. By its very nature, CI embraces a diversity of design paradigms; in particular it promotes a top-down approach (when exploiting fuzzy sets first and afterwards working in the neural network environment) or bottom-up style (where these two technologies are used in a reverse order). Visual computing is inherently associated with CI: it is human-centric where fuzzy sets make visualization activities feasible. Fuzzy sets are treated as a graphic means of accepting information from users. They are regarded as a vehicle used to visualize results in a linguistic manner. Software Engineering and CI are highly compatible: they are knowledge-intensive, human-oriented, and have to deal with various manifestations of the abstract world of software constructs and thought processes. This multifaceted conceptual compatibility is a prerequisite for the development of vital synergistic links that bring the technology of CI into Software Engineering. The symbiosis accrues considerable benefits for both technologies by posing new categories of challenging and highly stimulating problems. The facet of visual computing is essential in handling of software processes and software products. The intent of this study is to provide a general overview of this new development in Software Engineering. In particular, we highlight a number of selected and most visible trends occurring at the junction of CI and Software Engineering. Furthermore we discuss several specific applications of the technology of CI to software cost estimation, analysis of software measures and neural models of software quality.

9 citations

01 Jan 2012
TL;DR: A new model using fuzzy logic is proposed in order to estimate the most important factors of software effort estimation such as cost and time and neural network models used for carrying out the effort estimations for developing a software project.
Abstract: Software estimation accuracy is among the greatest challenges for software developers. Software development effort estimation is one of the most major activities in software project management. A number of models have been proposed to make software effort estimations but still no single model can predict the effort accurately. The need for accurate effort estimation in software industry is still a challenge. It can analyze the project decisions like resource allocation and bidding which can be used to complete the project with respect to time/within the scope of the time. It gives estimation about the cost and time required for software development. It can be implemented through various estimation techniques and estimation models. In this paper, we proposed a new model using fuzzy logic in- order to estimate the most important factors of software effort estimation such as cost and time and neural network models used for carrying out the effort estimations for developing a software project & this field of Soft Computing is suitable in effort estimations. We use MATLAB to determine the parameters of various time estimation models. The performance of model is evaluated on published software projects data. A simple review of our models with existing ubiquitous models is shown in this paper.

7 citations


Cites background or methods from "FULSOME: a fuzzy logic modeling too..."

  • ...Neural networks have also been effectively used for software testing problems.[15-22]....

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  • ...[17] explored an expert knowledge based application of FL to effort prediction....

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Book ChapterDOI
01 Jan 2003
TL;DR: Evidence is provided to support the use of fuzzy sets, fuzzy rules and fuzzy inference in modeling predictive relationships of relevance to software project management and a software toolset is constructed that enables data, classes and rules to be defined for any such relationship.
Abstract: In this paper we provide evidence to support the use of fuzzy sets, fuzzy rules and fuzzy inference in modeling predictive relationships of relevance to software project management. In order to make such an approach accessible to managers we have constructed a software toolset that enables data, classes and rules to be defined for any such relationship (e.g. determination of project risk, or estimation of product size, based on a variety of input parameters). We demonstrate the effectiveness of the approach by applying our fuzzy logic modeling toolset to two previously published data sets. It is shown that the toolset does indeed facilitate the creation and refinement of classes of data and rules mapping input values or classes to outputs. This in itself represents a positive outcome, in that the approach is shown to be capable of incorporating data and knowledge in a single model. The predictive results achieved from this approach are then compared to those produced using linear regression. While this is not the principal aim of the work, it is shown that the results are at least comparable in terms of accuracy, and in specific cases fuzzy logic modeling outperforms regression. Given its other appealing characteristics (for instance, transparency, robustness, incorporation of uncertainty), we believe that fuzzy logic modeling will be useful in assisting software personnel to further improve their management of projects.

4 citations

References
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Book
01 Jan 2013
TL;DR: The Second Edition of Software Metrics provides an up-to-date, coherent, and rigorous framework for controlling, managing, and predicting software development processes.
Abstract: From the Publisher: The Second Edition of Software Metrics provides an up-to-date, coherent, and rigorous framework for controlling, managing, and predicting software development processes. With an emphasis on real-world applications, Fenton and Pfleeger apply basic ideas in measurement theory to quantify software development resources, processes, and products. The book offers an accessible and comprehensive introduction to software metrics, now an essential component of software engineering for both classroom and industry. Software Metrics features extensive case studies from Hewlett Packard, IBM, the U.S. Department of Defense, Motorola, and others, in addition to worked examples and exercises. The Second Edition includes up-to-date material on process maturity and measurement, goal-question-metric, planning a metrics program, measurement in practice, experimentation, empirical studies, ISO9216, and metric tools.

2,827 citations


"FULSOME: a fuzzy logic modeling too..." refers background in this paper

  • ...Software metrics is the field of research and practice that involves investigating the characteristics of and relationships between sets of attributes associated with software development projects, usually in terms of products, processes, and resources [1]....

    [...]

Journal ArticleDOI
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.
Abstract: 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. There has also been an increasing level of sophistication in the regression-based techniques used, including robust regression methods, factor analysis, and more effective validation procedures. This paper examines the implications of using these methods and provides some recommendations as to when they may be appropriate. A comparison of the various techniques is also made in terms of their modelling capabilities with specific reference to software metrics.

201 citations

Journal ArticleDOI
TL;DR: A new consistent and robust method called the least-squares of inverted balanced relative errors (LIRS) is proposed and its superiority to the ordinary least-Squares method is demonstrated by use of five actual data sets.

178 citations


"FULSOME: a fuzzy logic modeling too..." refers background in this paper

  • ...Even once sufficient quantities of data are available, data purity is generally difficult to ascertain – necessitating some treatment of unusual observations [4]....

    [...]

Proceedings ArticleDOI
21 Sep 1997
TL;DR: Software metrics are measurements of the software development process and product that can be used as variables (both dependent and independent) in models for project management and the use of alternative techniques, especially fuzzy logic, is investigated and some usage recommendations are made.
Abstract: Software metrics are measurements of the software development process and product that can be used as variables (both dependent and independent) in models for project management. The most common types of these models are those used for predicting the development effort for a software system based on size, complexity, developer characteristics, and other metrics. Despite the financial benefits from developing accurate and usable models, there are a number of problems that have not been overcome using the traditional techniques of formal and linear regression models. These include the nonlinearities and interactions inherent in complex real-world development processes, the lack of stationarity in such processes, over-commitment to precisely specified values, the small quantities of data often available, and the inability to use whatever knowledge is available where exact numerical values are unknown. The use of alternative techniques, especially fuzzy logic, is investigated and some usage recommendations are made.

87 citations


"FULSOME: a fuzzy logic modeling too..." refers background in this paper

  • ...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]....

    [...]