Handbook of Software Fault Localization


Book Description

Handbook of Software Fault Localization A comprehensive analysis of fault localization techniques and strategies In Handbook of Software Fault Localization: Foundations and Advances, distinguished computer scientists Prof. W. Eric Wong and Prof. T.H. Tse deliver a robust treatment of up-to-date techniques, tools, and essential issues in software fault localization. The authors offer collective discussions of fault localization strategies with an emphasis on the most important features of each approach. The book also explores critical aspects of software fault localization, like multiple bugs, successful and failed test cases, coincidental correctness, faults introduced by missing code, the combination of several fault localization techniques, ties within fault localization rankings, concurrency bugs, spreadsheet fault localization, and theoretical studies on fault localization. Readers will benefit from the authors’ straightforward discussions of how to apply cost-effective techniques to a variety of specific environments common in the real world. They will also enjoy the in-depth explorations of recent research directions on this topic. Handbook of Software Fault Localization also includes: A thorough introduction to the concepts of software testing and debugging, their importance, typical challenges, and the consequences of poor efforts Comprehensive explorations of traditional fault localization techniques, including program logging, assertions, and breakpoints Practical discussions of slicing-based, program spectrum-based, and statistics-based techniques In-depth examinations of machine learning-, data mining-, and model-based techniques for software fault localization Perfect for researchers, professors, and students studying and working in the field, Handbook of Software Fault Localization: Foundations and Advances is also an indispensable resource for software engineers, managers, and software project decision makers responsible for schedule and budget control.




Software Engineering Research, Management and Applications


Book Description

This edited book presents the scientific outcomes of the 17th International Conference on Software Engineering, Artificial Intelligence Research, Management and Applications (SERA 2019) held on May 29–31, 2019 in Honolulu, Hawaii. The aim of the conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. This book includes 13 of the conference’s most promising papers featuring recent research in software engineering, management and applications




Automated Software Engineering: A Deep Learning-Based Approach


Book Description

This book discusses various open issues in software engineering, such as the efficiency of automated testing techniques, predictions for cost estimation, data processing, and automatic code generation. Many traditional techniques are available for addressing these problems. But, with the rapid changes in software development, they often prove to be outdated or incapable of handling the software’s complexity. Hence, many previously used methods are proving insufficient to solve the problems now arising in software development. The book highlights a number of unique problems and effective solutions that reflect the state-of-the-art in software engineering. Deep learning is the latest computing technique, and is now gaining popularity in various fields of software engineering. This book explores new trends and experiments that have yielded promising solutions to current challenges in software engineering. As such, it offers a valuable reference guide for a broad audience including systems analysts, software engineers, researchers, graduate students and professors engaged in teaching software engineering.




Search-Based Software Engineering


Book Description

This book constitutes the proceedings of the 13th International Symposium on Search-Based Software Engineering, SSBSE 2021, which was held in Bari, Italy, during October 11-12, 2021. The 9 full and 2 short papers included in this volume were carefully reviewed and selected from 19 submissions. The papers deal with novel ideas and applications of search-based software engineering, focusing on engineering challenges and the application of automated approaches and optimization techniques from AI and machine learning research.




Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis


Book Description

ISSTA '17: International Symposium on Software Testing and Analysis Jul 10, 2017-Jul 14, 2017 Santa Barbara, USA. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.




Structured Object-Oriented Formal Language and Method


Book Description

This book constitutes the thoroughly refereed workshop proceedings of the 9th International Workshop on Structured Object-Oriented Formal Language and Method, SOFL+MSVL 2019, held in Shenzhen, China, in November 2019. The 23 revised full papers included in the volume were carefully reviewed and selected from 43 submissions. They are organized in the following topical sections: testing and debugging, formal verification, problem solving, software analysis and evolution, and software analysis and testing.




Artificial Intelligence Methods For Software Engineering


Book Description

Software is an integral part of our lives today. Modern software systems are highly complex and often pose new challenges in different aspects of Software Engineering (SE).Artificial Intelligence (AI) is a growing field in computer science that has been proven effective in applying and developing AI techniques to address various SE challenges.This unique compendium covers applications of state-of-the-art AI techniques to the key areas of SE (design, development, debugging, testing, etc).All the materials presented are up-to-date. This reference text will benefit researchers, academics, professionals, and postgraduate students in AI, machine learning and software engineering.Related Link(s)




Imaging: Sensors and Technologies


Book Description

This book is a printed edition of the Special Issue "Imaging: Sensors and Technologies" that was published in Sensors




Graph Algorithms


Book Description

Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection. Learn how graph analytics vary from conventional statistical analysis Understand how classic graph algorithms work, and how they are applied Get guidance on which algorithms to use for different types of questions Explore algorithm examples with working code and sample datasets from Spark and Neo4j See how connected feature extraction can increase machine learning accuracy and precision Walk through creating an ML workflow for link prediction combining Neo4j and Spark




Uncertainty in Complex Networked Systems


Book Description

The chapters in this volume, and the volume itself, celebrate the life and research of Roberto Tempo, a leader in the study of complex networked systems, their analysis and control under uncertainty, and robust designs. Contributors include authorities on uncertainty in systems, robustness, networked and network systems, social networks, distributed and randomized algorithms, and multi-agent systems—all fields that Roberto Tempo made vital contributions to. Additionally, at least one author of each chapter was a research collaborator of Roberto Tempo’s. This volume is structured in three parts. The first covers robustness and includes topics like time-invariant uncertainties, robust static output feedback design, and the uncertainty quartet. The second part is focused on randomization and probabilistic methods, which covers topics such as compressive sensing, and stochastic optimization. Finally, the third part deals with distributed systems and algorithms, and explores matters involving mathematical sociology, fault diagnoses, and PageRank computation. Each chapter presents exposition, provides new results, and identifies fruitful future directions in research. This book will serve as a valuable reference volume to researchers interested in uncertainty, complexity, robustness, optimization, algorithms, and networked systems.