Computational Intelligence in Software Modeling

Researchers, academicians and professionals expone in this book their research in the application of intelligent computing techniques to software engineering. As software systems are becoming larger and complex, software engineering tasks become increasingly costly and prone to errors. Evolutionary algorithms, machine learning approaches, meta-heuristic algorithms, and others techniques can help the effi ciency of software engineering. THE SERIES: FRONTIERS IN COMPUTATIONAL INTELLIGENCE The series Frontiers In Computational Intelligence is envisioned to provide comprehensive coverage and understanding of cutting edge research in computational intelligence. It intends to augment the scholarly discourse on all topics relating to the advances in artifi cial life and machine learning in the form of metaheuristics, approximate reasoning, and robotics. Latest research findings are coupled with applications to varied domains of engineering and computer sciences. This field is steadily growing especially with the advent of novel machine learning algorithms being applied to different domains of engineering and technology. The series brings together leading researchers that intend to continue to advance the field and create a broad knowledge about the most recent research. Series Editor Dr. Siddhartha Bhattacharyya, CHRIST (Deemed to be University), Bangalore, India Editorial Advisory Board Dr. Elizabeth Behrman, Wichita State University, Kansas, USA Dr. Goran Klepac Dr. Leo Mrsic, Algebra University College, Croatia Dr. Aboul Ella Hassanien, Cairo University, Egypt Dr. Jan Platos, VSB-Technical University of Ostrava, Czech Republic Dr. Xiao-Zhi Gao, University of Eastern Finland, Finland Dr. Wellington Pinheiro dos Santos, Federal University of Pernambuco, Brazil Preface Acknowledgment Contents Editors’ profile Revolutionary transformations in twentieth century: making AI-assisted software development Useful techniques and applications of computational intelligence Machine learning-based attribute value search technique software component retrieval Use of fuzzy logic approach for software quality evaluation in Agile software development environment Metaheuristics for empirical software measurements Use of genetic algorithms in software testing models Insights into DevOps automation tools employed at different stages of software development Study of computational techniques to deal with ambiguity in SRS documents Utilization of images in an open source software to detect COVID-19 Designing of a tool for comparing and analyzing different test suites of open source software Decision tree–based improved software fault prediction: a computational intelligence approach Performance analysis for SDN POX and open daylight controller using network emulator Mininet under DDoS attack Index Researchers, academicians and professionals expose in this book their research in the application of intelligent computing techniques to software engineering. As software systems are becoming larger and complex, software engineering tasks become increasingly costly and prone to errors. Evolutionary algorithms, machine learning approaches, meta-heuristic algorithms, and others techniques can help the efficiency of software engineering