Conference Information

PROMISE 2026: International Conference on Predictive Models and Data Analytics in Software Engineering

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Submission Date:
2026-01-09
Notification Date:
2026-03-06
Conference Date:
2026-07-05
Location:
Montreal, Quebec, Canada
Years:
22
Viewed: 18299   Tracked: 0   Attend: 0
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Conference Partner Index (CP-I)

50.8 / 100
Ranked #1,430 of 5,695 conferences · Top 26%

#70 of 336 in Data Mining & Databases #110 of 247 in Software Engineering

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
84
Community attention (10%)
12
Public record completeness (15%)
35

Inputs used: Editions on record: 22 · Researchers who opened this page in the past 24 months: 4

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-10-10

Call For Papers

PROMISE 2026 (International Conference on Predictive Models and Data Analytics in Software Engineering) is an academic conference held in Montreal, Quebec, Canada on 2026-07-05. The paper submission deadline is 2026-01-09. Acceptance notifications are sent on 2026-03-06.

The International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE) welcomes four 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.
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