2017 |
Volume 5 |
Issue Issue 1
Applications of Artificial Intelligence and Data Mining in Optimizing Software Engineering
1.
Upamanyu Chakravarty
– 3rd Year Student, B. Tech. Computer Science, Vit University, Vellore, Tamil Nadu, India.
2.
Rajit Pimpale
– 3rd Year Student, B. Tech. Computer Science, Vit University, Vellore, Tamil Nadu, India.
3.
Raghav Sharma
– 3rd Year Student, B. Tech. Computer Science, Vit University, Vellore, Tamil Nadu, India.
4.
Ramanathan L
– 3rd Year Student, B. Tech. Computer Science, Vit University, Vellore, Tamil Nadu, India.
Received
22-Aug-2017
|
Accepted
-
|
Published
22-Aug-2017
|
Abstract
Software engineering usually deals with a huge amount of data while attempting optimization, consistent cost estimation and other activities. Software engineering uses life cycle models with a number of phases that seem most suitable for development of a particular software. These phases have goals, which when carried out effectively and efficiently, can increase the success rate of the software. This paper focuses on two main ideas which involve the use of data mining and artificial intelligence techniques in software engineering respectively. Data mining techniques such as association, classification and clustering can be used to achieve development goals more efficiently by finding hidden patterns in data. As a result, selection of the appropriate data mining technique for each phase of the life cycle model optimizes the development process. Artificial Intelligence techniques like fuzzy logic, genetic algorithms, neural networks, knowledge bases and machine learning are being increasingly used to improve software development procedures and their functionality. Since a software evolves by exposure to varied environments and expertise of developers, there is a huge scope for applying artificial intelligence techniques to software development and engineering.
Keywords Software engineering, Data mining, Fuzzy logic, Genetic algorithms, Artificial neural networks.
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