SBST 2022

About SBST

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.

Attending SBST

SBST 2022 is co-located with ICSE 2022. However, SBST will be completely online.
In order to attend SBST, you have to register for our workshop using the official ICSE registration link.
Once you registered for SBST, the ICSE team will send you an e-mail with the invitation to attend the conference through Midspace, the virtual conference meeting platform.

Similar to last year, we will also live-stream SBST 2022 via Twitch. Feel free to join our stream and ask questions in the chat if you are not registered.

Special Issue

We are please to announce that SBST'22 is hosting an open call special issue at the Science of Computer Programming Journal, called SBST'22: Search-Based Software Engineering - Tools.

Researchers and practitioners are invited to submit tools showcasing new or improved SBST approaches. It is important to mention that this call for original software does not publish papers accompanied by software but expects short papers supported by artifacts such as the original software described in the papers together with all the experimental packages, and other data needed for for sake of reproducibility. Hence, we invite submissions showing practical experience of using SBST techniques and tools.

This is the perfect opportunity to showcase your tool. Use the following link for more information and submission instructions: Science of Computer Programming Call

Call for Papers

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.

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.

Sponsors