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.