Call for Papers

DeepTest is an interdisciplinary workshop targeting research at the intersection of software engineering and deep learning. This workshop will explore issues related to:

• Deep Learning applied to Software Engineering (DL4SE)
• Software Engineering applied to Deep Learning (SE4DL)

Although the main focus is on Deep Learning, we also encourage submissions that are more broadly related to Machine Learning.

Topics of Interest

We welcome submissions introducing technology (i.e., frameworks, libraries, program analyses and tool evaluation) for testing DL-based applications, and DL-based solutions to solve open research problems (e.g., what is a bug in a DL/RL model). Relevant topics include, but are not limited to:

• High-quality benchmarks for evaluating DL/RL approaches
• Surveys and case studies using DL/RL technology
• Techniques to aid interpretable DL/RL techniques
• Techniques to improve the design of reliable DL/RL models
• DL/RL-aided software development approaches
• DL/RL for fault prediction, localization and repair
• Fuzzing DL/RL systems
• Metamorphic testing as software quality assurance
• Fault Localization and Anomaly Detection
• Use of DL for analyzing natural language-like artefacts such as code, or user reviews
• DL/RL techniques to support automated software testing
• DL/RL to aid program comprehension, program transformation, and program generation
• Safety and security of DL/RL based systems
• New approaches to estimate and measure uncertainty in DL/RL models

Types of Submissions

We accept two types of submissions:

• Full research papers: up to 8-page papers (including references) describing original and unpublished results related to the workshop topics;
• Short papers up: to 4-page papers (including references) describing preliminary work, new insights in previous work, or demonstrations of testing-related tools and prototypes.

All submissions must conform to the ICSE 2025 formatting instructions. All submissions must be in PDF. The page limit is strict. Submissions must conform to the IEEE conference proceedings template, specified in the IEEE Conference Proceedings Formatting Guidelines.

DeepTest 2025 will employ a double-blind review process. Thus, no submission may reveal its authors’ identities. The authors must make every effort to honor the double-blind review process. In particular, the authors’ names must be omitted from the submission, and references to their prior work should be in the third person.

If you have any questions or wonder whether your submission is in scope, please do not hesitate to contact the organizers.