- author = "Gabriel Jarillo and Giancarlo Succi and Witold Pedrycz and Marek Reformat",
- title = "Analysis of Software Engineering Data Using Computational Intelligence Techniques",
- booktitle = "7th International Conference on Object Oriented Information Systems, OOIS'2001",
- year = "2001",
- editor = "Yingxu Wang and Shushma Patel and Ronald Johnston",
- pages = "133--142",
- address = "Calgary, Canada",
- month = "27-29 " # aug,
- publisher = "Springer",
- bibsource = "DBLP, http://dblp.uni-trier.de",
- keywords = "genetic algorithms, genetic programming, SBSE",
- isbn13 = "9781852335465",
-
URL = "
http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-1-85233-546-5",
-
URL = "
http://www.inf.unibz.it/~gsucci/publications/images/analysisofsoftwareengineeringdatausingcomputationalsoftwaretechniques.pdf",
- size = "10 pages",
-
abstract = "The accurate estimation of software development effort
has major implications for the management of software
development in the industry. Underestimates lead to
time pressures that may compromise full functional
development and thorough testing of the software
product. On the other hand, overestimates can result in
over allocation of development resources and personnel
[7]. Many models for effort estimation have been
developed during the past years; some of them use
parametric methods with some degree of success, other
kind of methods belonging to the computational
intelligence family, such as Neural Networks (NN), have
been also studied in this field showing more accurate
estimations, and finally the Genetic programming (GP)
techniques are being considered as promising tools for
the prediction of effort estimation.
Organizations are wandering how they can predict the quality of their software before it is used. Generally there are tree approaches to do so [1]:
1. - Predicting the number of defects in the system.
2. - Estimating the reliability of the system in terms of time and failure.
3. - Understanding the impact of the design and testing processes on defect counts and failure densities.
Knowing the quality of the software allows the organization to estimate the amount of resources to be invested on its maintenance. Software maintenance is a factor that consumes most of the resources in many software organizations [2], therefore its worth it to be able to characterize, assess and predict defects in the software at early stages of its development in order to reduce maintenance costs. Maintenance involves activities such as correcting errors, maintaining software, and adapting software to deal with new environment requirements [2].",
- notes = "http://enel.ucalgary.ca/oois2001/programme.html",
