%0 Journal Article
%T Genetic Programming for Automating the Development of Data Management Algorithms in Information Technology Systems
%A Gabriel A. Archanjo
%A Fernando J. Von Zuben
%J Advances in Software Engineering
%D 2012
%I Hindawi Publishing Corporation
%R 10.1155/2012/893701
%X Information technology (IT) systems are present in almost all fields of human activity, with emphasis on processing, storage, and handling of datasets. Automated methods to provide access to data stored in databases have been proposed mainly for tasks related to knowledge discovery and data mining (KDD). However, for this purpose, the database is used only to query data in order to find relevant patterns associated with the records. Processes modelled on IT systems should manipulate the records to modify the state of the system. Linear genetic programming for databases (LGPDB) is a tool proposed here for automatic generation of programs that can query, delete, insert, and update records on databases. The obtained results indicate that the LGPDB approach is able to generate programs for effectively modelling processes of IT systems, opening the possibility of automating relevant stages of data manipulation, and thus allowing human programmers to focus on more complex tasks. 1. Introduction Information technology (IT) systems have become the basis of process management of today¡¯s successful enterprises. We can find this kind of system in virtually all fields of activities and inside corporations of any size. The intensive adoption of IT systems has promoted the emergence of an entire ensemble of technologies and services to supply a wide range of demands. Similar to what happens in other areas of product development, methodologies, processes, and tools have been enhanced over the years in order to improve the development of software products, which are going to promote increasing productivity and reduced costs. The first methodologies were inspired by principles found in other areas of product development, like manufacturing. However, the dynamic environment involved in software development is fostering a continuous improvement and customization of methodologies to embrace inevitable uncertainties and necessary redefinition of the product specification, resulting in an iterative and evolutionary process [1]. The need for more agile methodologies is promoting the development of enhanced tools and techniques, more notably in the field of code and design reuse. Approaches to automate entire modules of the software development or to support decision on software engineering have been explored. However, the automated generation of computer algorithms still remains restricted to the scientific field. Knowledge discovery and data mining (KDD) applications are associated with many different approaches to extract relevant patterns from datasets, including solutions
%U http://www.hindawi.com/journals/ase/2012/893701/