Software Technologies: 17th International Conference, ICSOFT 2022, Lisbon, Portugal, July 11-13, 2022, Revised Selected Papers

by Hans-Georg Fill (Editor), Marten van Sinderen (Editor), Leszek A. Maciaszek (Editor)

Overview

This book includes extended and revised versions of a set of selected papers from the 17th International Conference on Software Technologies, ICSOFT 2022, held in Lisbon, Portugal, during July 11–13, 2022. 

The 10 full papers included in this book were carefully reviewed and selected from 102 submissions. They were organized in topical sections as follows: tool assisted empirical approach to reusability models assessment; functional programming; three forms of mutant subsumption: basic, strict and broad; and on the efficiency of building large collections of software: modeling, algorithms, and experimental results.

Product Details

ISBN-13:	9783031372315
Publisher:	Springer-Verlag New York, LLC
Publication date:	07/18/2023
Series:	Communications in Computer and Information Science , #1859
Sold by:	Barnes & Noble
Format:	eBook
File size:	29 MB
Note:	This product may take a few minutes to download.

Table of Contents

Tool Assisted Empirical Approach to Reusability Models Assessment.- Microservices Deployment on a Multi-platform Ecosystem: A Contract-based Approach.- A Decision Model based on an Optimized Choquet Integral: Multifactor Prediction and Intelligent Agriculture Application.-A New Simulation Tool for Sensor Networks based on an Energy-efficient and Fault-tolerant Methodology.- Adapting Cyber-Risk Assessment for the Planning of Cyber-Physical Smart Grids based on Industrial Needs.- Three Forms of Mutant Subsumption: Basic, Strict and Broad.- On the Efficiency of Building Large Collections of Software: Modeling, Algorithms, and Experimental Results.- An AST-based Code Change Representation and Its Performance in Just-in-time Vulnerability Prediction.- Towards Extracting Reusable and Maintainable Code Snippets.- A deep learning architecture based on advanced textual language models for detecting disease through its symptoms associated with a reinforcement learning algorithm.