Search-Based Software Engineering
Springer International Publishing (Verlag)
978-3-030-59761-0 (ISBN)
This book constitutes the refereed proceedings of the 12th International Symposium on Search-Based Software Engineering, SSBSE 2020, held in Bari, Italy, in October 2020.
The 13 research papers and 5 short papers presented together with 1 keynote were carefully reviewed and selected from 34 submissions. SBSE 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.
Due to the Corona pandemic SSBSE 2020 was held as a virtual event.
Search-Based Predictive Modelling for Software Engineering: How Far Have We Gone.- Automated Unit Test Generation for Python.- Do Quality Indicators Prefer Particular Multi-Objective Search Algorithms in Search-Based Software Engineering.- It is not Only About Control Dependent Nodes: Basic Block Coverage for Search-Based Crash Reproduction.- Search-based Testing for Scratch Programs.- Solving Schedulability as a Search Space Problem with Decision Diagrams.- Transforming Multi-Objective Interactive Metamodel/Model Co-Evolution into Mono-Objective Search via Designer's Preferences Extraction.- Evolutionary Grammar-Based Fuzzing.- Commonality-Driven Unit Test Generation.- Using a Genetic Algorithm to Optimize Configurations in a Data Driven Application.- Measuring and Maintaining Population Diversity in Search-based Unit Test Generation.- Search@Home: A Commercial Off-the-Shelf Environment for Investigating Optimization Problems.- Exploring The Use of Genetic Algorithm Clustering forMobile App Categorisation.- Impact of Test Suite Coverage on Overfitting in Genetic Improvement of Software.- Bet and Run for Test Case Generation.- Bytecode-based Multiple Condition Coverage: An Initial Investigation.- An application of model seeding to search-based unit test generation for Gson.- Generating Diverse Test Suites for Gson Through Adaptive Fitness Function Selection.- Defects4J as a Challenge Case for the Search-Based Software Engineering Community.
| Erscheinungsdatum | 01.10.2020 |
|---|---|
| Reihe/Serie | Lecture Notes in Computer Science | Programming and Software Engineering |
| Zusatzinfo | XIV, 263 p. 86 illus. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 433 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Software Entwicklung |
| Schlagworte | Artificial Intelligence • automated crash reproduction • automated unit testing • block-based programming • common paths coverage • Computer-Aided Design (CAD) • Computer Hardware • computer programming • Computer Science • Computer systems • data-driven applications • Engineering • evolutionary algorithms • Field Programmable Gate Array (FPGA) • Genetic algorithms • grammar-based fuzzing • integrated circuit layout • interactive multi-objective search • Internet • Logic Design • Mathematics • Microprocessor chips • model co-evolution • Multi-Objective Optimization • object-oriented programming • probabilistic fuzzing • random test generation • Real-Time Systems • search-based software testing • search diversity • Software • Software Design • Software engineering • Software Testing • VLSI circuits • whole suite test generation |
| ISBN-10 | 3-030-59761-X / 303059761X |
| ISBN-13 | 978-3-030-59761-0 / 9783030597610 |
| Zustand | Neuware |
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