PROMISE 2026
Sun 5 Jul 2026 Montreal, Canada
co-located with FSE 2026

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.

Plenary
You're viewing the program in a time zone which is different from your device's time zone change time zone

Sun 5 Jul

Displayed 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
5m
Day opening
Opening
PROMISE 2026
Lili Wei McGill University, Xiaoyu Sun Australian National University, Australia, Csaba Nagy PONTUM Software GmbH
09:05
55m
Talk
Keynote 1 - The Limits of “Cute” CI/CD Pipelines
PROMISE 2026
Shane McIntosh University of Waterloo
10:00
15m
Talk
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
15m
Talk
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
30m
Coffee 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
15m
Talk
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
15m
Talk
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
15m
Talk
NGQA: Next-Gen Software Quality Accelerator using AI Agents and LLM Reasoning
PROMISE 2026
Moein Abtahi Ontario Tech University, Akramul Azim Ontario Tech University
11:45
15m
Talk
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
12:30
90m
Lunch
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
60m
Talk
Keynote 2 - Rethinking Reliability for Software Engineering Agents
PROMISE 2026
Tse-Hsun (Peter) Chen Concordia University
15:00
15m
Paper
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
15m
Talk
Do LLMs Learn Structure or Names? A Study on the Robustness of Design-Pattern Detection to Identifier Shifts
PROMISE 2026
Ichsan Budiman University of Birmingham, Leandro Minku University of Birmingham, UK
15:30 - 16:00
Coffee BreakFSE Catering
15:30
30m
Coffee 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
15m
Talk
Model-Driven Automation of Cyber-Physical Systems via AADL and LLMs
PROMISE 2026
Abdul Wahab Ontario Tech University, Akramul Azim Ontario Tech University
16:15
15m
Talk
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
15m
Talk
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
10m
Talk
Probabilistic Evidence Aggregation for Source Code Authorship Verification: An Ongoing Set-Based Approach
PROMISE 2026
Francisco Ortin University of Oviedo, David Álvarez-Fidalgo University of Oviedo
16:55
5m
Day closing
Closing
PROMISE 2026
Lili Wei McGill University, Xiaoyu Sun Australian National University, Australia, Csaba Nagy PONTUM Software GmbH

Accepted Papers

Title
An Empirical Study of Waterfall-style Multi-Agent Workflows for Class-Level Code Generation
PROMISE 2026
Beyond Code Snippets: Benchmarking LLMs on Repository-Level Question Answering
PROMISE 2026
Pre-print
Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models
PROMISE 2026
Developer Behavior in Response to LLM-Generated Code Refactoring Suggestions
PROMISE 2026
Pre-print
Do LLMs Learn Structure or Names? A Study on the Robustness of Design-Pattern Detection to Identifier Shifts
PROMISE 2026
Enforcing LLMs to Use Software Design Patterns: A Case of Singleton
PROMISE 2026
High Agreement, Shallow Reasoning: A Mixed-Method Study of LLMs in Refactoring Reviews
PROMISE 2026
MAS-SRE: A Multi-Agent System for Security Requirements Engineering
PROMISE 2026
Model-Driven Automation of Cyber-Physical Systems via AADL and LLMs
PROMISE 2026
NGQA: Next-Gen Software Quality Accelerator using AI Agents and LLM Reasoning
PROMISE 2026
Probabilistic Evidence Aggregation for Source Code Authorship Verification: An Ongoing Set-Based Approach
PROMISE 2026
Secure-Instruct: Prompt, Synthesize, and Fine-Tune for Secure Code Generation
PROMISE 2026

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:
  1. Ongoing or preliminary work not yet ready for a full paper.
  2. Tool demonstrations, case studies, or experience reports.
  3. 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.

ACM Digital Library

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