TY - CHAP
T1 - Computational Intelligence Approaches for Software Quality Improvement
AU - Albeanu, Grigore
AU - Madsen, Henrik
AU - Popențiu-Vlădicescu, Florin
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Computational intelligence
KW - Immune algorithms
KW - Software quality
KW - Software reliability
KW - Neutrosophic computational models
U2 - 10.1007/978-3-030-43412-0_18
DO - 10.1007/978-3-030-43412-0_18
M3 - Book chapter
SN - 9783030434113
T3 - Springer Series in Reliability Engineering
SP - 305
EP - 317
BT - Reliability and Statistical Computing
PB - Springer
ER -