image: Cover for "Artificial Intelligence: Methods for Software Engineering" view more
Credit: World Scientific
Software Engineering as a discipline is still evolving (the field is a merely shy of 50-years-old). And with the emergence of agile practices, software engineers today need to consider technology management, legacy software integration, organizational management, as well as deployment and infrastructure issues instead of solely focusing on developing software code. Successful software projects, therefore, require more than just technical expertise; it includes understanding the real needs of different stakeholders, collaborating in a team (nowadays, potentially globally distributed), managing complexity, mitigating risks, delivering projects on time and on budget, and determining when a software product is good enough to be shipped are at least equally important topics that often have a significant human component (the so-called soft skills). The quest toward software engineering processes with high quality can be achieved with Artificial Intelligence.
Back in 2011, Marc Andreessen, co-founder and general partner of venture capital firm Andreessen Horowitz, wrote an essay in The Wall Street Journal on the fact that “Software is eating the world.” A couple of years later, in 2014, Dutch computer scientist and entrepreneur Erik Meijer, since 2015 a Director of Engineering at Facebook, co-authored a paper published in the Communications of the ACM corroborating the same thought: “of the top five fastest-growing companies with regard to market capitalization in 2014, three are software companies: Apple, Google, and Microsoft (in fact, one could argue that Intel is also driven by software, making it four out of five).” Arguably, over the last couple of decades, software technology has been one of the primary drivers of economic growth in the world. Developing reliable software, however, remains far from trivial. A 2013 study from Cambridge University estimates that software bugs cost the global software Industry a staggering $316 billion per year. As software becomes one of the fundamental pillars of almost any company, following engineering concepts for software designing, creating, improving, and maintaining software becomes of paramount importance.
Written by contributors ranging from PhD scholars to world leading experts and researchers, Artificial Intelligence: Methods for Software Engineering covers applications of state-of-the-art AI techniques to the key areas of software engineering, including but not limited to: design, development, debugging and testing. This reference text will benefit researchers, academics, professionals, and postgraduate students in AI, machine learning and software engineering.
This book has been partially funded by the Cyber Security Research Center at Ben-Gurion University of the Negev and by ISF grant No. 1716/17. This material is based upon work supported by Fundação para a Ciência e a Tecnologia (FCT), with the reference PTDC/CCI-COM/29300/2017 and UID/CEC/50021/2019. The authors further would like to thank Roni Stern for being a research partner.
Artificial Intelligence: Methods for Software Engineering retails for US$158 / £140 (hardcover) and is also available in electronic formats. To order or know more about the book, visit http://www.worldscientific.com/worldscibooks/10.1142/12360.
###
About the Editors
Meir Kalech completed his Ph.D. at the Computer Science Department of Bar-Ilan University, Israel, in 2006. In 2008, he became a faculty member of the Department of Software and Information System Engineering at Ben-Gurion University of the Negev, Israel. Kalech’s research interests lie in artificial intelligence and specifically in anomaly detection and diagnosis. Kalech established the Anomaly Detection and Diagnosis Lab (AiDnD) which integrates two main AI approaches: model-based and data driven. He is a recognized expert in model-based diagnosis (MBD) and has published dozens of papers in leading journals and refereed conferences. In the past five years Kalech promotes research that implement these approaches for software engineering tasks such as debugging and testing. Kalech’s lab promotes cooperation research with the government and leading corporations such as General Motors, Mekorot and IBM. Among the research of Kalech exist anomaly detection of Supervisory Control And Data Acquisition (SCADA) systems, Automated debugging, survival analysis and troubleshooting. Kalech has served as a senior program committee in leading AI conferences such as AAAI, IJCAI, AAMAS and the International Workshop on Principles of Diagnosis.
Rui Abreu holds a Ph.D. in Computer Science, Software Engineering from the Delft University of Technology, The Netherlands, and a M.Sc. in Computer and Systems Engineering from the University of Minho, Portugal. His research revolves around software quality, with emphasis in automating the testing and debugging phases of the software development life-cycle as well as self-adaptation. Dr. Abreu has extensive expertise in both static and dynamic analysis algorithms for improving software quality. He is the recipient of 6 Best Paper Awards, including a Distinguished Paper Award at ESEC/FSE 2019, and his work has attracted considerable attention. Before joining FEUP as a Full Professor, he was an Associate Professor at IST, ULisbon and a member of the Model-Based Reasoning group at PARC’s System and Sciences Laboratory and an Assistant Professor at the University of Porto. He has co-founded DashDash in January 2017, a platform to create web apps using only spreadsheet skills. The company has secured $9M in Series A funding in May 2018. He was a Visiting Researcher at Google NYC between 2019 and 2020, working on building systems and tools to increase the security of C/C++ codebases.
Mark Last is a Full Professor at the Department of Software and Information Systems Engineering, at Ben-Gurion University of the Negev, Israel, and the Head of the Data Engineering Program. Prof. Last obtained his Ph.D. degree from Tel Aviv University, Israel, in 2000. Prior to starting his appointment at Ben-Gurion University in March 2001, Mark Last was a Visiting Assistant Professor at the Department of Computer Science and Engineering, University of South Florida, Tampa, Florida, USA (1999–2001). Prof. Last has served as the Head of the Software Engineering Program (2009–2012) and as the Founding Head of the Data Science Research Center at Ben-Gurion University (2018–2020). Prof. Last has published over 210 peer-reviewed papers, two monographs, and 10 edited volumes on data mining, text mining, and cyber security. According to Google Scholar, his works were cited more than 6,000 times. Five Ph.D. students and 41 M.Sc. students have graduated under his supervision. He is a Senior Member of the IEEE Computer Society and a Professional Member of the Association for Computing Machinery (ACM). Prof. Last currently serves as an Editorial Board Member of two leading data science journals: Data Mining and Knowledge Discovery and Machine Learning Journal. Previously, he served as an Associate Editor of IEEE Transactions on Systems, Man, and Cybernetics - Part C (2004–2012), Pattern Analysis and Applications (2007–2016), and IEEE Transactions on Cybernetics (2013¬2019). His main research interests are focused on data mining, cross-lingual text mining, soft computing, cyber intelligence, and medical informatics.
About World Scientific Publishing Co.
World Scientific Publishing is a leading international independent publisher of books and journals for the scholarly, research and professional communities. World Scientific collaborates with prestigious organisations like the Nobel Foundation and US National Academies Press to bring high quality academic and professional content to researchers and academics worldwide. The company publishes about 600 books and over 140 journals in various fields annually. To find out more about World Scientific, please visit www.worldscientific.com.
For more information, contact Amanda at heyun@wspc.com.