Handling imprecision in inputs using fuzzy logic to predict effort in software development

2010 ◽  
Author(s):  
Harsh Kumar Verma ◽  
Vishal Sharma
Keyword(s):  
Fuzzy Logic ◽  
Software Development

Artificial Intelligence in Software Engineering

2012 ◽  
pp. 1215-1236 ◽  
Author(s):  
Farid Meziane ◽  
Sunil Vadera
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Software Engineering ◽  
Software Development ◽  
Language Processing ◽  
Knowledge Based ◽  
Development Life Cycle ◽  
Planning Methods ◽  
Software Development Life Cycle ◽  
Processing Techniques

Artificial intelligences techniques such as knowledge based systems, neural networks, fuzzy logic and data mining have been advocated by many researchers and developers as the way to improve many of the software development activities. As with many other disciplines, software development quality improves with the experience, knowledge of the developers, past projects and expertise. Software also evolves as it operates in changing and volatile environments. Hence, there is significant potential for using AI for improving all phases of the software development life cycle. This chapter provides a survey on the use of AI for software engineering that covers the main software development phases and AI methods such as natural language processing techniques, neural networks, genetic algorithms, fuzzy logic, ant colony optimization, and planning methods.

Artificial Intelligence in Software Engineering

2010 ◽  
pp. 278-299 ◽  
Author(s):  
Farid Meziane ◽  
Sunil Vadera
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Software Engineering ◽  
Software Development ◽  
Language Processing ◽  
Knowledge Based ◽  
Development Life Cycle ◽  
Planning Methods ◽  
Software Development Life Cycle ◽  
Processing Techniques

Artificial intelligences techniques such as knowledge based systems, neural networks, fuzzy logic and data mining have been advocated by many researchers and developers as the way to improve many of the software development activities. As with many other disciplines, software development quality improves with the experience, knowledge of the developers, past projects and expertise. Software also evolves as it operates in changing and volatile environments. Hence, there is significant potential for using AI for improving all phases of the software development life cycle. This chapter provides a survey on the use of AI for software engineering that covers the main software development phases and AI methods such as natural language processing techniques, neural networks, genetic algorithms, fuzzy logic, ant colony optimization, and planning methods.

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