NDLI: Estimating software project effort by analogy based on linguistic values

Content Provider	IEEE Xplore Digital Library
Author	Idri, A. Abran, A. Khoshgoftaar, T.M.
Copyright Year	2002
Description	Author affiliation: ENSIAS, Univ. Mohamed V Souissi, Rabat, Morocco (Idri, A.)
Abstract	Estimation models in software engineering are used to predict some important attributes of future entities such as software development effort, software reliability and programmers' productivity. Among these models, those estimating software effort have motivated considerable research in recent years. The prediction procedure used by these software-effort models can be based on a mathematical function or other techniques such as analogy based reasoning, neural networks, regression trees, and rule induction models. Estimation by analogy is one of the most attractive techniques in the software effort estimation field. However, the procedure used in estimation by analogy is not yet able to handle correctly linguistic values (categorical data) such as 'very low', 'low' and 'high'. We propose a new approach based on reasoning by analogy, fuzzy logic and linguistic quantifiers to estimate software project effort when it is described either by numerical or linguistic values; this approach is referred to as Fuzzy Analogy. This paper also presents an empirical validation of our approach based on the COCOMO'81 dataset.
Sponsorship	ALCATEL Carleton Univ. Cistel MetricCentre Nat. Res. Council Canada Conseil national de recherches Canada
Starting Page	21
Ending Page	30
File Size	334427
Page Count	10
File Format	PDF
ISBN	0769513395
ISSN	15301435
DOI	10.1109/METRIC.2002.1011322
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-06-04
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Mathematical model Fuzzy logic Software engineering Software reliability Programming profession Productivity Neural networks Regression tree analysis Fuzzy reasoning
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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