NSE 2025

1st International Workshop on Neuro-Symbolic Software Engineering

Software engineering has a success history of evolving symbolic techniques, e.g., formal methods and programming languages, to solve increasingly challenging problems like providing safety and performance guarantees for 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 applications from code summarization & generation to automatic program repair & formal verification. The integration of symbolic and ML techniques has opened new 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:

• Case studies

• Proofs-of-concept

• New ideas and emerging results

• Evaluation of tools

• Controlled experiment reports

Accepted Papers

Call for Papers

1st International Workshop on Neuro-Symbolic Software Engineering

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 which 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.

Important Dates

• Paper submissions: November 11th, 2024.
• Paper notifications: December 1st, 2024.
• Camera-ready versions: February 5th, 2025.
• Workshop: TBA

Guidelines

Submissions must conform to the IEEE conference proceedings template, specified in the IEEE Conference Proceedings Formatting Guidelines, using the double-column format. Research papers must have a maximum length of 6 pages. Short papers are limited to 4 pages. Industry demonstrations and extended abstracts must have a maximum length of 2 pages. 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.

We require all submissions to be original, i.e., they should not have been previously published in any conference proceedings, book, or journal and should not currently be under review for another archival conference. Papers must be submitted electronically by the defined deadline (see above) on the link: TBA.