Tim Menzies (NC State University, USA) firstname.lastname@example.org
Chakkrit (Kla) Tantithamthavorn (Monash University, Australia) email@example.com
Burak Turhan (Monash University, Australia) firstname.lastname@example.org
Any description of real-world “AI software” is a complex combination of AI modules (containing the algorithms) with much other conventional code for data accessing, data cleansing, data labelling, model deployment, model evaluation, federation, storage, reporting, configuration management, etc. That is:
This is a time of much change for SE. As Andrew Ng observes, the rise of SE required inventing processes like version control, code review, and agile, to help teams work effectively. What new processes and tools are needed now for SE in the age of AI (like how we split train/dev/test, model zoos, etc)? Many developers using AI tools now use new processes that haven’t been formalized or named yet, ranging from how we write product requirement docs to how we version data and AI pipelines. Now is the time to respect that experience and formalize those processes.
Note to potential authors: A general EMSE paper may discuss AI for SE; e.g., a case study where a data miner was applied to some SE data. But for this special issue we seek SE for AI papers; i.e. empirical evidence about the best and worst practices for the development, monitoring, and maintenance of software that uses AI components. Such papers might discuss issues like the following (and note that this list is a small subset of the space of possible papers):
Note that this list is just a small subset of the space of possible papers.
If unsure if your paper might be suitable, please contact the special issue editors.
Deadline for submission: October 15, 2019
Papers should be submitted through the Empirical Software Engineering website http://www.editorialmanager.com/emse/. Choose “SI: SE4AI” as the Article Type.
If you have questions/comments or would like to volunteer to be a reviewer of the papers, please contact the guest editors.