ICSE2009 - International Conference on Predictor Models in Software Engineering, Vancouver, Kanada

Abbrevation

PROMISE

Link

http://promisedata.org/2009/welcome.html

City

Vancouver

Country

Canada

Deadline Paper

2009-01-26

Start Date

2009-05-18

End Date

2009-05-19

Abstract

The PROMISE conference leverages the successful experience from the four previous workshops. The objective of the conference is to deliver to the software engineering community useful, usable, verifiable, and repeatable models applicable to better manage software processes and projects. To this end, we: <ul><li class=″blk_heading″> Maintain an on–line repository where researchers and practitioners can store the data they use to make their conclusions, access models and data used by other researchers and practitioners, and compare their results to those obtained in previous work. </li><li> Hold an annual conference where researchers and practitioners can meet to discuss and refine their results and methods, and to describe repeatable experiments in software engineering </li></ul> The PROMISE conference provides an annual forum for researchers and practitioners to present and exchange ideas, results, and experiences with predictor models that are applicable to implementation, evaluation, and management of software processes and projects. A key objective of PROMISE is to help establish verifiable and repeatable models that are useful to the software engineering community. Traditionally, PROMISE meetings have focused on effort and defect prediction. For 2009, PROMISE is expanding its traditional scope to include topics such as model–based requirements engineering and value–based software engineering. We encourage submissions that extend the idea of PROMISE to many more fields of software engineering. Topics of Interest include, but are not limited, to <ul><li class=″blk_heading″>Effort prediction models </li><li class=″blk_heading″>Defect prediction models </li><li class=″blk_heading″>Applications of predictive models in software engineering </li><li class=″blk_heading″>Classification of predictive models </li><li class=″blk_heading″>Model–based requirements engineering </li><li class=″blk_heading″>Value–based software engineering </li><li class=″blk_heading″>Building predictive models using AI and statistical methods </li><li class=″blk_heading″>Strengths and limitations of predictive models </li><li>Cost benefit analysis of predictive models </li></ul> The PROMISE on–line repository provides researchers and practitioners a central location where they can store the data they use to make their conclusions, and access models and data used by other researchers and practitioners, The repository now contains 76 data sets, which can be used to repeat/confirm/refute/improve previous results. Conference attendees are encouraged to provide their datasets to the PROMISE repository, to be used for experiments and as benchmarks by other researchers. PROMISE 2009 gives the highest priority to case studies, experience reports, and presented results that are based on publicly available datasets. To increase the chance of acceptance, authors are urged to submit papers that use such datasets. Data can come from anywhere including the conference Web site (<a href=″http://promisedata.org/repository/″ target=″_blank″>http://promisedata.org/repository/</a>). Submissions should be five to ten pages long (max), and must be original and previously unpublished. The details of the paper and data submission process are available at <a href=″http://promisedata.org/2009/cfp.html″ target=″_self″>http://promisedata.org/2009/cfp.html</a> Papers will be reviewed by the program committee in terms of their technical content and their relevance to the scope of the conference, as well as their ability to stimulate discussion. At least one author of an accepted paper is required to register and attend the conference. Selected papers from prior PROMISE workshops appeared in IEEE Software and the Empirical Software Engineering journal. Papers presented at PROMISE 2009 will be submitted to the ACM digital library and are eligible for submission to a special issue TBD.