Search Based Software Engineering

Software engineering, is widely recognized as one of today's most exciting, stimulating, and profitable research areas, with a significant practical impact on the software industry and academia. The LASER school, held annually since 2004 on Elba Island, Italy, is intended for professionals from industry (engineers and managers) as well as university researchers, including PhD students. This book contains selected lecture notes from the LASER summer schools 2008-2010, which focused on concurrency and correctness in 2008, software testing in 2009, and empirical software engineering, in 2010.

This book constitutes the refereed proceedings of the 8th International Symposium on Search-Based Software Engineering, SSBSE 2016, held in Raleigh, NC, USA, in October 2016.The 13 revised full papers and 4 short papers presented together with 7 challenge track and 4 graduate student track papers were carefully reviewed and selected from 48 submissions. Search Based Software Engineering (SBSE) studies the application of meta-heuristic optimization techniques to various software engineering problems, ranging from requirements engineering to software testing and maintenance.

This book constitutes the refereed proceedings of the Third International Symposium on Search Based Software Engineering, SSBSE 2011 held in Szeged, Hungary in collocation with ESEC/FSE 2011. The 18 revised full papers presented together with two invited contributions and abstracts of eight poster presentations were carefully reviewed and selected from 43 submissions. The papers are organized in topical sections on foundations of SSBSE; concurrency and models; requirements and planning; software testing; and comprehension, transformation and scalability.

This book covers a range of basic and advanced topics in software engineering. The field has undergone several phases of change and improvement since its invention, and there is significant ongoing research in software development, addressing aspects such as analysis, design, testing and maintenance. Rather than focusing on a single aspect of software engineering, this book provides a systematic overview of recent techniques, including requirement gathering in the form of story points in agile software, and bio-inspired techniques for estimating the effort, cost, and time required for software development. As such it is a valuable resource for new researchers interested in advances in software engineering — particularly in the area of bio-inspired techniques.

This book constitutes the refereed proceedings of the 10th International Symposium on Search-Based Software Engineering, SSBSE 2018, held in Montpellier, France, in September 2018. The 12 full papers and 7 short papers presented together with 3 keynotes, 2 tutorials, and 1 anniversary paper were carefully reviewed and selected from 21 submissions. SSBSE welcomes not only applications from throughout the software engineering lifecycle but also a broad range of search methods ranging from exact Operational Research techniques to nature-inspired algorithms and simulated annealing. Chapter "Deploying Search Based Software Engineering with Sapienz at Facebook" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This book constitutes the refereed proceedings of the 6th International Symposium on Search-Based Software Engineering, SSBSE 2014, held in Fortaleza, Brazil. The 14 revised full papers presented together with 2 keynote addresses, 1 invited talk, 1 short paper, 3 papers of the graduate track, and 4 challenge track papers were carefully reviewed and selected from 51 submissions. Search Based Software Engineering (SBSE) studies the application of meta-heuristic optimization techniques to various software engineering problems, ranging from requirements engineering to software testing and maintenance.

This book constitutes the refereed proceedings of the 11th International Symposium on Search-Based Software Engineering, SSBSE 2019, held in Tallinn, Estonia, in August/September 2019. The 9 research papers and 3 short papers presented together with 1 keynote and 1 challenge paper were carefully reviewed and selected from 28 submissions. SSBSE is a research area focused on the formulation of software engineering problems as search problems, and the subsequent use of complex heuristic techniques to attain optimal solutions to such problems. A wealth of engineering challenges - from test generation, to design refactoring, to process organization - can be solved efficiently through the application of automated optimization techniques. SBSE is a growing field - sitting at the crossroads between AI, machine learning, and software engineering - and SBSE techniques have begun to attain human-competitive results.

With the growth of public and private data stores and the emergence of off-the-shelf data-mining technology, recommendation systems have emerged that specifically address the unique challenges of navigating and interpreting software engineering data. This book collects, structures and formalizes knowledge on recommendation systems in software engineering. It adopts a pragmatic approach with an explicit focus on system design, implementation, and evaluation. The book is divided into three parts: “Part I – Techniques” introduces basics for building recommenders in software engineering, including techniques for collecting and processing software engineering data, but also for presenting recommendations to users as part of their workflow. “Part II – Evaluation” summarizes methods and experimental designs for evaluating recommendations in software engineering. “Part III – Applications” describes needs, issues and solution concepts involved in entire recommendation systems for specific software engineering tasks, focusing on the engineering insights required to make effective recommendations. The book is complemented by the webpage rsse.org/book, which includes free supplemental materials for readers of this book and anyone interested in recommendation systems in software engineering, including lecture slides, data sets, source code, and an overview of people, groups, papers and tools with regard to recommendation systems in software engineering. The book is particularly well-suited for graduate students and researchers building new recommendation systems for software engineering applications or in other high-tech fields. It may also serve as the basis for graduate courses on recommendation systems, applied data mining or software engineering. Software engineering practitioners developing recommendation systems or similar applications with predictive functionality will also benefit from the broad spectrum of topics covered.