2021 IEEE/ACM 14th International Workshop on Search-Based Software Testing SBST

活动简介

Search-Based Software Testing (SBST) is the application of optimizing search techniques (for example, Genetic Algorithms) to solve problems in software testing. SBST is used to generate test data, prioritize test cases, minimize test suites, optimize software test oracles, reduce human oracle cost, verify software models, test service-orientated architectures, construct test suites for interaction testing, and validate real time properties (among others).

The objectives of this workshop are to bring together researchers and industrial practitioners both from SBST and the wider software engineering community to collaborate, to share experience, to provide directions for future research, and to encourage the use of search techniques in novel aspects of software testing in combination with other aspects of the software engineering lifecycle.

Sponsor Type:1; 9

组委会

Co-Chairs

Jie Zhang, UNIVERSITY COLLEGE LONDON, UK
Erik Fredericks, GRAND VALLEY STATE UNIVERSITY, USA

Program Committee

Nazareno Aguirre, Universidad Nacional de Río Cuarto - CONICET, Argentina
Giuliano Antoniol, Ecole Polytechnique de Montréal, Canada
Kate Bowers, Oakland University, USA
Jose Campos, University of Washington, USA
Thelma E. Colanzi, State University of Maringá, Brazil
Byron DeVries, Grand Valley State University, USA
Gordon Fraser, University of Passau, Germany
Gregory Gay, Chalmers and the University of Gothenburg, Sweden
Giovani Guizzo, University College London, UK
Gregory Kapfhammer, Allegheny College, USA
Yiling Lou, Peking University, China
Mitchell Olsthoorn, Delft University of Technology, Netherlands
Sebastiano Panichella, University of Zurich, Switzerland
Justyna Petke, University College London, UK
Jose Miguel Rojas, University of Leicester, UK
Paolo Tonella, Università della Svizzera Italiana, Switzerland
Thomas Vogel, Humboldt-Universität zu Berlin, Germany

征稿信息

重要日期

2021-01-19

初稿截稿日期

2021-02-22

初稿录用日期

Researchers and practitioners are invited to submit:

Full papers (maximum of 8 pages, including references) Original research in SBST, either empirical, theoretical, or showing practical experience of using SBST techniques and/or SBST tools.

Short papers (maximum of 4 pages, including references) Work that describes novel techniques, ideas and positions that have yet to be fully developed; or are a discussion of the importance of a recently published SBST result by another author in setting a direction for the SBST community, and/or the potential applicability (or not) of the result in an industrial context.

Position papers (maximum of 2 pages, including references) that analyze trends in SBST and raise issues of importance. Position papers are intended to seed discussion and debate at the workshop, and thus will be reviewed with respect to relevance and their ability to spark discussions.

Tool Competition entries (maximum of 4 pages, including references). We invite researchers, students, and tool developers to design innovative new approaches to software test generation.

作者指南

In all cases, papers should address a problem in the software testing/verification/validation domain or combine elements of those domains with other concerns in the software engineering lifecycle. Examples of problems in the software testing/verification/validation domain include (but are not limited to) generating testing data, fuzzing, prioritizing test cases, constructing test oracles, minimizing test suites, verifying software models, testing service-orientated architectures, constructing test suites for interaction testing, SBST for AI applications, machine learning techniques for SBST, and validating realtime properties.

The solution should apply a metaheuristic search strategy such as (but not limited to) random search, local search (e.g. hill climbing, simulated annealing, and tabu search), evolutionary algorithms (e.g. genetic algorithms, evolution strategies, and genetic programming), ant colony optimization, particle swarm optimization, and multi-objective optimization.