Brazilian Symposium on Systematic and Automated Software Testing (SAST)

Call for Papers

Software testing plays a major role in the verification and validation of software systems. Furthermore, empirical studies indicate that testing-related activities often represent more than 50% of software development costs. Systematic and automated testing approaches have proven capable of contributing to the reduction of these costs. Success stories in the industry are constantly reported, and interest in academic investigations is continually increasing, as evidenced by the large number of researchers involved in this topic.

The main objective of SAST is to provide a discussion forum to bring together industry and academic communities involved in the systematization and automation of software testing for different system categories.

In its eleventh edition as a symposium co-located with the Brazilian Conference on Software: Theory and Practice (CBSoft), SAST 2026 invites the entire software testing community to submit the results of their innovations in the area. This eleventh edition of the symposium continues to promote dialogue between industry and academia by encouraging industry participation. As a result, in addition to research papers, this year will also feature the Industrial Abstracts category. For this submission type, testing practitioners are invited to submit work focusing on the pragmatic challenges associated with performing testing activities in the industry.

Important Dates

Topics of Interest

SAST promotes a harmonious and joint discussion among academic and industry professionals involved with practices, activities, and tools related to systematic and automated software testing. Topics covered by the symposium include, but are not limited to, the following:

• Automated software testing: environments, tools, and reports on test automation in the industry
• Combinatorial testing and random testing
• Distributed and parallel software testing
• Error-based testing and fault-based testing
• Management and integration of testing activities and resources
• Maintenance and reuse of test artifacts
• Model-based testing (MBT) and model-driven testing
• Performance, load, and stress testing
• Prediction and risk-based testing
• Program-based testing, specification-based testing, and requirements testing
• Test adequacy and coverage measures
• Test case generation and selection techniques
• Test engines, stubs, frameworks, and test script generation
• Test effectiveness analysis and test reporting
• Test oracles and test result verification techniques
• Testing contemporary software systems: Mobile, Cloud, Context-aware, Internet of Things (IoT), Industry 4.0, Big Data
• Learning-based software testing, including Deep Learning
• Testing of non-functional requirements: security, safety, interoperability, among others
• AI-assisted software testing

Submission Guidelines

SAST accepts two types of submissions:

1. Regular Papers (no fewer than 6 pages and no more than 10 pages, including all figures, references, and appendices); and
2. Industry Abstracts (not exceeding 3 pages for the entire content of the paper).

All submissions must present original contributions to the software testing research area. Submissions must comply with the ACM two-column conference format (ACM_SigConf) available at [ACM link]. LaTeX users must use the acmart.cls class provided in the template with the conference format enabled in the document preamble:

\documentclass[sigconf]{acmart}

Authors must use the ACM-Reference-Format.bst bibliography style also provided in the template:

\bibliographystyle{ACM-Reference-Format}

Regular papers and industry abstracts may be written in Portuguese or English. Submissions must be made in Adobe Portable Document Format (PDF) using the SBC JEMS system (https://jems3.sbc.org.br/sast2026).

Regular papers will follow a double-anonymous review process. Initial paper submissions must not contain any author identification. All information regarding authors and their institutions must be removed from the paper, including references to the authors themselves and any links that point to the authors or their institutions. Regarding references, if necessary, we suggest writing "[Anonymous 2026]" in the text and "[Anonymous 2026] Information omitted for double-anonymous review" in the reference list. Regarding links, we suggest replacing potential identifiers within them with "Link omitted for double-anonymous review." We request strict adherence by authors to these issues regarding the double-anonymous review. Upon acceptance of the paper, all hidden information must be included in the final version.

Regular papers will be evaluated based on their suitability to the SAST scope, relevance, technical soundness, and originality. Regular papers must indicate their relationship to previous work, particularly work previously published by the authors themselves. Papers on empirical studies must describe the methods used and interpret their results both qualitatively and quantitatively. At least three Program Committee members will review all submissions.

All industry abstracts will undergo an anonymous peer review process: we will make an effort to include at least one industry expert among the reviewers. Preferably, the author presenting the paper should not have academic affiliations. We encourage contributions focused on industrial innovation or lessons learned that may inspire future academic work. We also accept submissions that are still in an early stage of development and that may lead to new perspectives and ideas. The goal of this track is to promote the exchange of knowledge and experience between academics and practitioners.

The publication of accepted papers in the symposium is conditional upon the presentation of the paper at SAST 2026 and the registration of at least one of its authors at CBSoft 2026.

About the use of AI (Artificial Intelligence) or AI-assisted technologies in scientific papers

By submitting work to SAST 2026, authors acknowledge that they are in compliance with the policy on the use of generative AI, based on existing policies proposed by IEEE, ACM, and Springer.

It is Prohibited:

• Listing generative AI tools and technologies, such as ChatGPT, as authors of the work.
• Using text or sections entirely produced by generative AI tools.

It is Permitted (with explicit disclosure in acknowledgments):

• Using generative AI tools to create parts of the content, with disclosure in the paper's acknowledgments indicating what was generated and which tool was used. It is important to check the tool's terms of use, which is the authors' responsibility. For example, in the acknowledgments: ChatGPT was used to generate the first paragraph of Section 3 and to generate Table 3.2.

It is Permitted (without need for mention):

• Using AI or AI-assisted technologies to improve image quality in terms of contrast and clarity.
• Using generative AI tools to edit and improve the quality of existing text (similar to an assistant like Grammarly to improve spelling, grammar, punctuation, clarity, and engagement).

Organization

Program Committee Chairs

Roberta Coelho - Federal University of Rio Grande do Norte (UFRN)

Elvys Soares - Federal Institute of Alagoas (IFAL)