9th International Conference on Predictive Models in Software Engineering, Baltimore, Vereinigte Staaten

PROMISE conference is an annual forum for researchers and practitioners to present, discuss and exchange ideas, results, expertise and experiences in construction and/or application of prediction models in software engineering&#046; Such models 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&#046;<br>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 machine learning communities, and seeking for verifiable and repeatable prediction models that are useful in practice&#046;<br>SPECIAL THEME:<br>The special theme of PROMISE’13 is predictions across projects, contexts and organizations, where the predictions employ approaches (e&#046;g&#046; transfer learning, instance selection, data filtering) with an impact that is useful in practice, in order to solve the problem of learning under concept drift (across time and space)&#046;<br>TOPICS OF INTEREST:<br>* (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 in policy and decision making; using predictive models in different settings, e&#046;g&#046; lean/agile, waterfall, distributed, community&#8211;based software development&#046;<br>* (Theory oriented): Interdisciplinary and novel approaches to predictive modeling that contribute to the theoretical body of knowledge in software engineering; verifying/refuting/challenging previous theory and results; the effectiveness of human experts vs&#046; automated models in predictions&#046;<br>* (Data and model oriented): Data quality, sharing, and privacy; ethical issues related to data collection; metrics; contributions to the repository; model construction, evaluation, sharing and reusability; tools and frameworks to support researchers and practitioners to collect data and construct models to share/repeat experiments and results&#046;<br>