The International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE) is an annual forum for researchers and practitioners to present, discuss and exchange ideas, results, expertise and experiences in construction and/or application of predictive models, artificial intelligence, and data analytics in software engineering. PROMISE encourages researchers to publicly share their data in order to provide interdisciplinary research between the software engineering and data mining communities, and seek for verifiable and repeatable experiments that are useful in practice.
Sun 5 JulDisplayed time zone: Eastern Time (US & Canada) change
09:00 - 10:30 | Session 1: Keynote and Research Papers on LLM Code Generation FoundationsPROMISE 2026 at MB 3.430 Chair(s): Lili Wei McGill University | ||
09:00 5mDay opening | Opening PROMISE 2026 Lili Wei McGill University, Xiaoyu Sun Australian National University, Australia, Csaba Nagy PONTUM Software GmbH | ||
09:05 55mTalk | Keynote 1 - The Limits of “Cute” CI/CD Pipelines PROMISE 2026 Shane McIntosh University of Waterloo | ||
10:00 15mTalk | An Empirical Study of Waterfall-style Multi-Agent Workflows for Class-Level Code Generation PROMISE 2026 Wasique Islam Shafin Concordia University, Md Naklha Rafi Concordia University, Zhenhao Li York University, Tse-Hsun (Peter) Chen Concordia University | ||
10:15 15mTalk | Secure-Instruct: Prompt, Synthesize, and Fine-Tune for Secure Code Generation PROMISE 2026 Junjie Li Concordia University, Fazle Rabbi Concordia University, Bo Yang Concordia University, Song Wang York University, Jinqiu Yang Concordia University | ||
10:30 - 11:00 | Coffee BreakFSE Catering | ||
10:30 30mCoffee break | Break FSE Catering | ||
11:00 - 12:30 | Session 2: LLMs for Code Quality and Developer InteractionPROMISE 2026 at MB 3.430 Chair(s): Jinqiu Yang Concordia University | ||
11:00 15mTalk | Developer Behavior in Response to LLM-Generated Code Refactoring Suggestions PROMISE 2026 David Schön Chalmers University of Technology and University of Gothenburg, Faiza Amjad Chalmers University of Technology and University of Gothenburg, Tehreem Asif Chalmers University of Technology and University of Gothenburg, Ranim Khojah Chalmers University of Technology and University of Gothenburg, Mazen Mohamad Chalmers | RISE - Research Institutes of Sweden, Francisco Gomes de Oliveira Neto Chalmers | University of Gothenburg, Philipp Leitner Chalmers | University of Gothenburg Pre-print | ||
11:15 15mTalk | High Agreement, Shallow Reasoning: A Mixed-Method Study of LLMs in Refactoring Reviews PROMISE 2026 Larisse Amorim Federal University of Minas Gerais, Caique Fortunato Federal University of Minas Gerais, Gustavo Vale Federal University of Minas Gerais, Eduardo Figueiredo Federal University of Minas Gerais | ||
11:30 15mTalk | NGQA: Next-Gen Software Quality Accelerator using AI Agents and LLM Reasoning PROMISE 2026 | ||
11:45 15mTalk | Enforcing LLMs to Use Software Design Patterns: A Case of Singleton PROMISE 2026 Viktor Kjellberg Chalmers University of Technology and University of Gothenburg, Farnaz Fotrousi Chalmers University of Technology and University of Gothenburg, Miroslaw Staron University of Gothenburg and Chalmers University of Technology | ||
12:30 - 14:00 | LunchFSE Catering | ||
12:30 90mLunch | Lunch FSE Catering | ||
14:00 - 15:30 | Session 3: Keynote and Research Papers on Code Understanding and ReasoningPROMISE 2026 at MB 3.430 Chair(s): Csaba Nagy PONTUM Software GmbH | ||
14:00 60mTalk | Keynote 2 - Rethinking Reliability for Software Engineering Agents PROMISE 2026 Tse-Hsun (Peter) Chen Concordia University | ||
15:00 15mPaper | Beyond Code Snippets: Benchmarking LLMs on Repository-Level Question Answering PROMISE 2026 Yoseph Berhanu Alebachew Virginia Tech, Hunter Leary Virginia Tech, Swanand Vaishampayan Virginia Tech, Chris Brown Virginia Tech Pre-print | ||
15:15 15mTalk | Do LLMs Learn Structure or Names? A Study on the Robustness of Design-Pattern Detection to Identifier Shifts PROMISE 2026 | ||
15:30 - 16:00 | Coffee BreakFSE Catering | ||
15:30 30mCoffee break | Break FSE Catering | ||
16:00 - 18:00 | Session 4: Security, Trust, and Verification of LLM-Generated CodePROMISE 2026 at MB 3.430 Chair(s): Zhijie Wang Concordia University | ||
16:00 15mTalk | Model-Driven Automation of Cyber-Physical Systems via AADL and LLMs PROMISE 2026 | ||
16:15 15mTalk | MAS-SRE: A Multi-Agent System for Security Requirements Engineering PROMISE 2026 Savvas Mantzouranidis Blekinge Institute of Technology, Ricardo Britto Ericsson / Blekinge Institute of Technology | ||
16:30 15mTalk | Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models PROMISE 2026 Youwei Huang Independent Researcher, Jianwen Li Carnegie Mellon University, Silicon Valley, Sen Fang North Carolina State University, Yao Li Macau University of Science and Technology, Peng Yang Institute of Intelligent Computing Technology, Suzhou, CAS, Bin Hu Institute of Computing Technology, Chinese Academy of Sciences | ||
16:45 10mTalk | Probabilistic Evidence Aggregation for Source Code Authorship Verification: An Ongoing Set-Based Approach PROMISE 2026 | ||
16:55 5mDay closing | Closing PROMISE 2026 Lili Wei McGill University, Xiaoyu Sun Australian National University, Australia, Csaba Nagy PONTUM Software GmbH | ||
Accepted Papers
Call for Papers
The International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE) welcomes three types of submissions:
Technical papers (10 pages)
- PROMISE accepts a wide range of papers where AI tools have been applied to SE such as predictive modeling and other AI methods.
- Both positive and negative results are welcome, though negative results should still be based on rigorous research and provide details on lessons learned.
Industrial papers (2–4 pages)
- Results, challenges, lessons learned from industrial applications of software analytics.
Extended Abstract Track (1-4 pages)
- Designed to encourage early sharing of initial results and new ideas.
- Papers should clearly explain:
- Ongoing or preliminary work not yet ready for a full paper.
- Tool demonstrations, case studies, or experience reports.
- Should clearly explain the main contribution, the current progress or results, and next steps or planned improvements.
Topics of Interest
PROMISE papers can explore any of the following topics (or more).
Application-oriented papers:
- prediction of cost, effort, quality, defects, business value;
- quantification and prediction of other intermediate or final properties of interest in software development regarding people, process or product aspects;
- using predictive models and data analytics in different settings, e.g. lean/agile, waterfall, distributed, community-based software development;
- dealing with changing environments in software engineering tasks;
- dealing with multiple-objectives in software engineering tasks;
- using predictive models and software data analytics in policy and decision-making;
- generative AI, large language models (LLMs), and “vibe coding” for prediction and development.
Ethically-aligned papers:
- Can we apply and adjust our AI-for-SE tools (including predictive models) to handle ethical non-functional requirements such as inclusiveness, transparency, oversight and accountability, privacy, security, reliability, safety, diversity and fairness?
Theory-oriented papers:
- model construction, evaluation, sharing and reusability;
- interdisciplinary and novel approaches to predictive modelling and data analytics that contribute to the theoretical body of knowledge in software engineering;
- verifying/refuting/challenging previous theory and results;
- combinations of predictive models and search-based software engineering;
- the effectiveness of human experts vs. automated models in predictions.
Data-oriented papers:
- data quality, sharing, and privacy;
- curated data sets made available for the community to use;
- ethical issues related to data collection and sharing;
- metrics;
- tools and frameworks to support researchers and practitioners to collect data and construct models to share/repeat experiments and results.
Validity-oriented papers:
- replication and repeatability of previous work using predictive modelling and data analytics in software engineering;
- assessment of measurement metrics for reporting the performance of predictive models;
- evaluation of predictive models with industrial collaborators.
Submissions
PROMISE 2026 submissions must meet the following criteria:
- be original work, not published or under review elsewhere while being considered;
- conform to the submission format requirements of the FSE 2026 Companion proceedings;
- not exceed 10 (4) pages for technical (industrial, new-ideas) papers including references;
- be written in English;
- be prepared for double blind review.
> Exception: For data-oriented papers, authors may elect not to use double blind by placing a footnote on page 1 saying “Offered for single-blind review”.
- be submitted via HotCRP;
- on submission, please choose the paper category appropriately, i.e.,
technical (main track, 10 pages max); industrial (4 pages max); and new idea papers (4 pages max).
For Industrial papers and New Idea papers, please clearly indicate the paper category in the keywords below the abstract.
To satisfy the double blind requirement submissions must meet the following criteria: - no author names and affiliations in the body and metadata of the submitted paper; - self-citations are written in the third person; - no references to the authors personal, lab, or university website; - no references to personal accounts on GitHub, bitbucket, Google Drive, etc.
Evaluation
Submissions will be peer reviewed by at least three experts from the international program committee. Submissions will be evaluated on the basis of their originality, importance of contribution, soundness, evaluation, quality, and consistency of presentation, and appropriate comparison to related work.
Important Dates
Abstracts due: Jan 9th, 2026 AoE(UPDATE: The mandatory abstract deadline has been waived. Authors may submit full papers directly without a prior abstract submission.)- Submissions due: Jan 16th, 2026 AoE
- Author notification: March 6th, 2026 AoE
- Camera ready: April 2nd, 2026 AoE
- Conference Date: July 5th, 2026
Open Access
Starting 2026, all articles published by ACM will be made Open Access. This is greatly beneficial to the advancement of computer science and leads to increased usage and citation of research. Most authors will be covered by ACM OPEN agreements by that point and will not have to pay Article Processing Charges (APC). Check if your institution participates in ACM OPEN. Authors not covered by ACM OPEN agreements may have to pay APC; however, ACM is offering several automated and discretionary APC Waivers and Discounts.
Submissions must follow the latest policies from ACM (“ACM Policy on Authorship”, with associated FAQ), which includes a policy specific to the use of generative AI tools and technologies, such as ChatGPT.
The official publication date is the date the proceedings are made available in the ACM Digital Library. This date may be up to two weeks prior to the first day of FSE 2026. The official publication date affects the deadline for any patent filings related to published work.
Purchases of additional pages in the proceedings are not allowed.
We also strongly encourage authors to submit their tools and data to Zenodo, which adheres to FAIR (findable, accessible, interoperable and re-usable) principles and provides DOI versioning.
NOTE: Extended abstracts are not subject to APC.
Publication and Attendance
Accepted papers will be published in the ACM Digital Library within its International Conference Proceedings Series and will be available electronically via ACM Digital Library.

Each accepted paper needs to have one registration at the full conference rate and be presented in person at the conference.