NSE 2026

2nd International Workshop on Neuro-Symbolic Software Engineering (April 18, 2026)

Software engineering has a successful history of evolving symbolic techniques, e.g., formal methods and programming languages, to solve challenging problems like providing safety and performance guarantees to autonomous intelligent systems fulfilling mission-critical functions. With the availability of machine learning (ML) techniques, software engineering expanded its set of problems to how learning from data enables new applications, e.g., code summarization & generation, automatic program repair, and & formal verification.

The integration of symbolic and ML techniques has opened novel methodological challenges that go beyond applying ML to build software (ML4SE) or applying software engineering to build ML (SE4ML). These challenges fall under the umbrella of Neuro-Symbolic methods and comprise problems of “how to reason about learning” and “how to learn about reasoning”.

The NSE workshop aims to discuss these problems in the context of software engineering tasks that have been transformed by the adoption of machine learning techniques. We invite insights on merging symbolic and ML techniques across the software development life-cycle, its activities, tasks, and tools. We welcome case studies, conceptual innovative approach descriptions, empirical research, and more formal or theoretical considerations.

Our goal is to collect experiences, challenges, and solutions involved in combining symbolic methods and machine learning to tackle new and traditional challenges of software engineering tasks from requirements to analysis & design, coding, testing, and maintenance & evolution. We welcome contributions in any of the following formats:

• Research papers

• Case studies

• Proofs-of-concept

• New ideas and emerging results

• Evaluation of tools

• Controlled experiment reports

Call for Papers

Goals

The integration of symbolic and ML techniques has opened new novel methodological challenges that go beyond applying Machine Learning (ML) to build software (ML4SE) or applying software engineering to build ML (SE4ML). These challenges fall under the umbrella of Neuro-Symbolic methods and comprise problems of “how to reason about learning” and “how to learn about reasoning”. NSE aims to discuss these problems in the context of software engineering tasks, which in turn have been the subject of innovation through the adoption of machine learning techniques. To illustrate that, we selected a broad non-exclusive list of topics (see below). Ultimately, this workshop invites insights on merging symbolic and ML techniques across the software development life-cycle, its activities, tasks, and tools. We welcome case studies, conceptual innovative approach descriptions, and empirical research that explore how formal reasoning can benefit from learning from data, e.g., trimming combinatorial search spaces, reconfiguring symbolic representations, generating new rules, etc. Conversely, we look forward to contributions that discuss how learning from data can be improved by being steered by reasoning, e.g., regularization, shaping goals/rewards, ensemble learning, etc. If you are also excited about any of these perspectives, please join us in shaping the future of software engineering.

NSE seeks submissions describing novel research, emerging ideas, and work-in-progress describing original and unpublished results in the field of Neuro-symbolic methods for software engineering.

Topics

• Neuro-symbolic methods in automated software engineering tools, e.g., code & test generation, bug fixing, code summarization, code review, etc.
• Neuro-Symbolic agents to support collaboration and decision making in software teams.
• Neuro-Symbolic methods in validation and verification tools.
• Neuro-Symbolic methods for designing safety-mission-critical systems.
• Neuro-Symbolic methods for extracting and maintaining knowledge graphs for software engineering.
• Methods for reasoning about learning from software data.
• Methods for learning while reasoning about software, e.g., automatically & adaptively determining decision thresholds and magnitude of actions for a desired effect of a software tool & technique.
• Methods for applying prior symbolic or probabilistic knowledge to new or improved software tools & methods.
• Formal methods in neuro-symbolic software engineering.
• Theoretical frameworks or formal guarantees for neuro-symbolic systems

Important Dates

• Paper submissions: October 20th, 2025 November 10th, 2025.
• Paper notifications: November 24th, 2025 Decemebr 8th, 2025.
• Camera-ready versions: January 26th, 2026.
• Workshop day: Saturday, 18 April 2026.

Guidelines

We invite three different types of submissions this year:

• Full papers (research): up to 8 pages
• Short papers (research, experience, or industry demonstration): up to 6 pages
• Extended abstracts: up to 5 pages. These are free of APC (article processing charge).

Submissions must be in English, in PDF format, and must not exceed the page limits (including references and appendices) listed above. Submissions should be made via HotCRP and strictly conform to the ACM conference proceedings formatting instructions, using the double-column format.
There is no limit on the number of submissions an author may submit. We will follow a single-blind process, i.e., anonymizing the submission is not required.

Other detailed submission policies and formatting guidelines are aligned with the ICSE 2026 Research Track submission process.