Journal ArticleDOI
Adaptive fuzzy logic-based framework for software development effort prediction
TLDR
An adaptive fuzzy logic framework for software effort prediction that tolerates imprecision, explains prediction rationale through rules, incorporates experts knowledge, offers transparency in the prediction system, and could adapt to new environments as new data becomes available is presented.Abstract:
Algorithmic effort prediction models are limited by their inability to cope with uncertainties and imprecision present in software projects early in the development life cycle. In this paper, we present an adaptive fuzzy logic framework for software effort prediction. The training and adaptation algorithms implemented in the framework tolerates imprecision, explains prediction rationale through rules, incorporates experts knowledge, offers transparency in the prediction system, and could adapt to new environments as new data becomes available. Our validation experiment was carried out on artificial datasets as well as the COCOMO public database. We also present an experimental validation of the training procedure employed in the framework.read more
Citations
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Software engineering economics
TL;DR: In this article, the authors provide an overview of economic analysis techniques and their applicability to software engineering and management, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.
Journal ArticleDOI
Towards an early software estimation using log-linear regression and a multilayer perceptron model
TL;DR: A novel log-linear regression model based on the use case point model (UCP) to calculate the software effort based on use case diagrams is presented and demonstrates that the MLP model can surpass the regression model when small projects are used, but the log- linear regression model gives better results when estimating larger projects.
Journal ArticleDOI
An Improved Fuzzy Approach for COCOMO’s Effort Estimation Using Gaussian Membership Function
Ch. Satyananda Reddy,Kvsvn Raju +1 more
TL;DR: Gaussian function is found to be performing better than the trapezoidal function, as it demonstrates a smoother transition in its intervals, and the achieved results were closer to the actual effort.
Journal ArticleDOI
Improved estimation of software project effort using multiple additive regression trees
TL;DR: Improved estimation accuracy of software project effort has been achieved using MART when compared with linear regression, radial basis function neural networks, and support vector regression models.
Proceedings ArticleDOI
Software development effort estimation using fuzzy logic: a case study
TL;DR: This paper describes an application whose results are compared with those of a multiple regression, and shows that the value of MMRE (an aggregation of magnitude of relative error, MRE) applying fuzzy logic was slightly higher than MMRE applying multiple regression.
References
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TL;DR: In this article, the authors present a comprehensive introduction to the theory and practice of artificial intelligence for modern applications, including game playing, planning and acting, and reinforcement learning with neural networks.
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TL;DR: The book updates the research agenda with chapters on possibility theory, fuzzy logic and approximate reasoning, expert systems, fuzzy control, fuzzy data analysis, decision making and fuzzy set models in operations research.
Software engineering economics
TL;DR: In this paper, the authors provide an overview of economic analysis techniques and their applicability to software engineering and management, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.
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Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence
TL;DR: This text provides a comprehensive treatment of the methodologies underlying neuro-fuzzy and soft computing with equal emphasis on theoretical aspects of covered methodologies, empirical observations, and verifications of various applications in practice.
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Fuzzy Set Theory and Its Applications
TL;DR: In this paper, a new book about fuzzy set theory and its applications is presented, which can be used to explore the knowledge of the knowledge in a new way, even for only few minutes to read a book.