ECOOP 2016 - European Conference on Object-Oriented Programming

ECOOP 2016 solicits high quality submissions describing original and unpublished results. The program committee will evaluate the technical contribution of each submission as well as its general relevance and accessibility to the ECOOP audience according the following criteria:
Topic
ECOOP has become a broad conference for high-level programming language ideas, while retaining expertise in object-oriented topics. The past editions give a clear indication of the kind of material appropriate and interesting to the conference. Please consult the program chair if you have questions about fit and appropriateness.
Originality
Papers must present new ideas and place them appropriately within the context established by previous research in the field. The paper must clearly identify what this contribution has accomplished and how it relates to previous work.
Significance
The results in the paper must have the potential to add to the state of the art or practice in important or significant ways. The paper must challenge or change informed opinion about what is possible, true, or likely.
Evidence
The paper must present evidence supporting its claims. Examples of evidence include formalizations and proofs, implemented systems, experimental results, statistical analyses, and case studies.
Clarity
The paper must present its claims and results clearly. It must be organized so that it is easily understood by an audience with varied expertise.
Length
Papers must be no longer than 25 pages, including references, figures and integral appendices. See below for information about additional appendices. However, papers should be as long as necessary, but not longer. Reviewers will expect papers to be at least 18 pages long, beyond which authors will not be penalized for papers that are shorter than the page limit.
For Reproduction Studies: Empirical Evaluation
Common in other sciences, reproduction means independently reconstructing an experiment in a different context (e.g. virtual machine, platform, class of applications) in order to validate or refute important results of earlier work. A good reproduction study will include thorough empirical evaluation, meeting high statistical standards. It will contain a detailed comparison with the previous results, seeking reasons for possible disagreements.