PROMISE 2016 - 12th 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 and data analytics in software engineering. Such models and analyses could be targeted at: planning, design, implementation, testing, maintenance, quality assurance, evaluation, process improvement, management, decision making, and risk assessment in software and systems development.
PROMISE is distinguished from similar forums with its public data repository and focus on methodological details, providing a unique interdisciplinary venue for software engineering and data mining communities, and seeking for verifiable and repeatable experiments that are useful in practice.
Topics of interest include, but are not limited to:
Application oriented:
predicting for 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.
Theory oriented:
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:
contributions to the repository;
data quality, sharing, and privacy;
ethical issues related to data collection;
metrics;
tools and frameworks to support researchers and practitioners to collect data and construct models to share/repeat experiments and results.