Accepted Papers

Call for Papers

We accept papers in three categories:

• Full research papers describing original research work and results. We encourage authors to include validation of their contributions by means of a case study or experiments. We also welcome research papers focusing on tools and tool development.
• Case study papers discussing a significant application that suggests general lessons learned and motivates further research, or empirically validates theoretical results (such as a technique’s scalability).
• Research ideas papers describing new ideas in preliminary form, in a way that can stimulate interesting discussions at the conference, and suggest future work.

All papers submitted to the FormaliSE 2025 conference must be written in English, must be unpublished original work, and must not be under review or submitted elsewhere at the time of submission. Submissions must comply with the FormaliSE’s lightweight double-anonymous review process (see below).

Full research papers and case study papers can take up to 10 pages including all text, figures, tables and appendices, but excluding references. Research ideas papers can take up to 4 pages, plus up to 1 additional page solely for references.

To avoid that authors waste time fitting their papers into the stated limit at the expense of presentation clarity, paper lengths slightly exceeding the stated limit will still be considered, provided that the reviewers find that the presentation is of high quality.

All submissions must be in PDF format and must conform to the IEEE conference proceedings template, specified in the IEEE Conference Proceedings Formatting Guidelines (i.e., title in 24pt font and full text in 10pt type). In LaTeX, use \documentclass[10pt,conference]{IEEEtran} without including the compsoc or compsocconf options. Note that IEEE format is being used this year, whereas last year it was ACM format, hence the appearance will differ from year to year.

To submit a paper to FormaliSE 2025 use the following HotCRP link.

Lightweight Double-Anonymous Review Process for Papers

As in recent editions, FormaliSE 2025 will use a lightweight double-anonymous process. Authors must omit their names and institutions from the title page, cite their own work in the third person, and omit acknowledgments that may reveal their identity or affiliation. The purpose is reducing chances of reviewer bias influenced by the authors’ identities. The double-anonymous process is, however, lightweight, which means that it should not pose a heavy burden for authors, nor should make a paper’s presentation weaker or more difficult to review. Also, advertising the paper as part of your usual research activities (for example, on your personal web-page, in a pre-print archive, by email, in talks or discussions with colleagues) is permitted without penalties.

PAPER SELECTION

Each paper will be reviewed by at least three program committee members that will judge its overall quality in terms of its soundness, significance, novelty, verifiability, and presentation clarity.

FormaliSE 2025 will adopt a lightweight response process: if all the reviewers of a given paper agree that a clarification from the authors regarding a specific question could move the paper from “borderline” to “accept”, the chairs will relay the reviewers’ questions to the authors by email, and then share their reply with the reviewers in HotCRP. The goal of lightweight responses is reducing the chance of random decisions on borderline papers. Hence, they will only be used for a minority of submissions; most papers will not require such an author response. Nevertheless, we would ask the corresponding authors of all submissions to make sure that they are available to answer questions by email upon request.

PUBLICATION

All accepted papers are published as part of the ICSE 2025 Proceedings in the ACM and IEEE Digital Libraries.

At least one author of each accepted paper is required to register for the conference and present the paper at the conference — physically or, if the circumstances do not allow so, virtually. Failure to register an author will result in a paper being removed from the proceedings.

Adversarial Perturbations and Self-Defenses for Large Language Models on Coding Task

Abstract: Large language models (LLMs) have demonstrated impressive capabilities for coding tasks including writing and reasoning about code. They improve upon previous neural network models of code that already demonstrated competitive results when performing tasks such as code summarization and identifying code vulnerabilities . However, it is known that these pre-LLM code models are vulnerable to adversarial examples, i.e. small syntactic perturbations that do not change the program’s semantics, such as the inclusion of “dead code” through false conditions, the addition of inconsequential print statements, or change in control flow, designed to “fool” the models. LLMs can also be vulnerable to the same adversarial perturbations. In this talk we discuss the effect of adversarial perturbations on coding tasks with LLMs and propose effective defenses against such adversaries. The coding tasks we’ll consider include both classification (e.g., use LLMs for summarization, vulnerability detection) and code generation (e.g., use LLMs for code completion, based on prompts plus code snippets).

Corina Pasareanu is an ACM Fellow and an IEEE ASE Fellow, working at NASA Ames. She is affiliated with KBR and Carnegie Mellon University's CyLab. Her research interests include model checking, symbolic execution, compositional verification, probabilistic software analysis, autonomy, and security. She is the recipient of several awards, including ETAPS Test of Time Award (2021), ASE Most Influential Paper Award (2018), ESEC/FSE Test of Time Award (2018), ISSTA Retrospective Impact Paper Award (2018), ACM Impact Paper Award (2010), and ICSE 2010 Most Influential Paper Award (2010).

Assuring AI in Autonomous Driving: Challenges and Emerging Approaches

Abstract: Artificial intelligence plays a critical role in autonomous driving, yet it introduces significant assurance challenges. Unlike traditional software, AI lacks explicit specifications and is applied to inherently uncertain, complex, and open problems such as road environment perception. Furthermore, modern AI systems are often opaque, making interpretability and verification difficult. These issues complicate the development of assurance methods that can ensure safety and reliability.
In this talk, I will review current approaches and industry standards for AI assurance in autonomous driving, including scenario-based testing and uncertainty estimation. I will discuss assurance methods for both hybrid systems — where AI functions as a component within a classical software stack — and end-to-end AI-driven systems. Finally, I will explore emerging directions for handling edge-case scenarios, including the use of foundation models and dual-processing architectures as potential solutions for enhancing the safety and robustness of end-to-end AI systems.



Krzysztof Czarnecki is a Professor of Electrical and Computer Engineering and a University Research Chair at the University of Waterloo, where he leads the Waterloo Intelligent Systems Engineering (WISE) Laboratory. He currently also serves as an Associate Director of the Waterloo Centre for Automotive Research (WatCAR). His research focuses on assuring the safety of AI systems and driving behavior. In 2018, he co-led the development of the first autonomous vehicle tested on public roads in Canada. He has made significant contributions to automotive AI and software safety standards, including SAE J3164 and ISO 8800. Before joining the University of Waterloo, he worked at DaimlerChrysler Research in Germany (1995-2002), where he advanced software development practices and technologies for enterprise, automotive, and aerospace sectors. His work has been recognized with numerous awards, including the Premier's Research Excellence Award (2004) and the British Computing Society’s Upper Canada Award for Outstanding Contributions to the IT Industry (2008). He has also received twelve Best Paper Awards, two ACM Distinguished Paper Awards, and five Most Influential Paper Awards.