Obtaining reliable, secure and efficient software under optimal resource allocation is an important objective of software engineering science. This work investigates the usage of classical and recent development paradigms of computational intelligence (CI) to fulfill this objective. The main software engineering steps asking for CI tools are: product requirements analysis and precise software specification development, short time development by evolving automatic programming and pattern test generation, increasing dependability by specific design, minimizing software cost by predictive techniques, and optimal maintenance plans. The tasks solved by CI are related to classification, searching, optimization, and prediction. The following CI paradigms were found useful to help software engineers: fuzzy and intuitionistic fuzzy thinking over sets and numbers, nature inspired techniques for searching and optimization, bio inspired strategies for generating scenarios according to genetic algorithms, genetic programming, and immune algorithms. Neutrosophic computational models can help software management when working with imprecise data.
|Series||Springer Series in Reliability Engineering|
- Computational intelligence
- Immune algorithms
- Software quality
- Software reliability
- Neutrosophic computational models