Paper Info
Title
Software Effort Estimation with Ridge Regression and Evolutionary Attribute Selection
Abstract
Software cost estimation is one of the prerequisite managerial activities carried out at the software development initiation stages and also repeated throughout the whole software life-cycle so that amendments to the total cost are made. In software cost estimation typically, a selection of project attributes is employed to produce effort estimations of the expected human resources to deliver a software product. However, choosing the appropriate project cost drivers in each case requires a lot of experience and knowledge on behalf of the project manager which can only be obtained through years of software engineering practice. A number of studies indicate that popular methods applied in the literature for software cost estimation, such as linear regression, are not robust enough and do not yield accurate predictions. Recently the dual variables Ridge Regression (RR) technique has been used for effort estimation yielding promising results. In this work we show that results may be further improved if an AI method is used to automatically select appropriate project cost drivers (inputs) for the technique. We propose a hybrid approach combining RR with a Genetic Algorithm, the latter evolving the subset of attributes for approximating effort more accurately. The proposed hybrid cost model has been applied on a widely known high-dimensional dataset of software project samples and the results obtained show that accuracy may be increased if redundant attributes are eliminated.
Year
Venue
Keywords
2010
Clinical Orthopaedics and Related Research
linear regression,human resource,ridge regression,software engineering,genetic algorithm,software life cycle,attribute selection,software development,artificial intelligent
Field
DocType
Volume
Data mining,Analysis effort method,Computer science,Software reliability testing,Software metric,COCOMO,Use Case Points,Software development,Software sizing,Software regression
Journal
abs/1012.5
Citations 
PageRank 
References 
6
0.73
14
Authors
3
Name
Order
Citations
PageRank
Efi Papatheocharous113321.97
Harris Papadopoulos221926.33
Andreas S. Andreou321636.65