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.
The workshop will adhere to the general ICSE workshop dates (AOE):
Fri 1 Feb 2019
Notification to Authors
Fri 1 Mar 2019
Camera Ready Due
Fri 15 Mar 2019
All submissions must conform to the ICSE 2019 formatting and submission instructions. All submissions must be anonymized, in PDF format and should be performed electronically through EasyChair.
Researchers and practitioners are invited to submit:
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, prioritizing test cases, constructing test oracles, minimizing test suites, verifying software models, testing service-orientated architectures, constructing test suites for interaction testing, and validating real time 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, and particle swarm optimization.