In recent years, machine learning has transitioned from a field of academic research interest to a field capable of solving real-world business problems. However, the deployment of machine learning models in production systems can present a number of issues and concerns. This survey reviews published reports of deploying machine learning solutions in a variety of use cases, industries and applications and extracts practical considerations corresponding to stages of the machine learning deployment workflow. By mapping found challenges to the steps of the machine learning deployment workflow we show that practitioners face issues at each stage of the deployment process. The goal of this paper is to lay out a research agenda to explore approaches addressing these challenges.
Programming languages evolve just like programs. Language features are added and removed, for example when programs using them are shown to be error-prone. When language features are modified, deprecated, removed or even deemed unsuitable for the project at hand, it is necessary to analyse programs to identify occurrences to refactor.Source code query languages in principle provide a good way to perform this analysis by exploring codebases. Such languages are often used to identify code to refactor, bugs to fix or simply to understand a system better.This paper evaluates seven Java source code query languages: Java Tools Language, Browse-By-Query, SOUL, JQuery, .QL, Jackpot and PMD as to their power at expressing queries required by several use cases (such as code idioms to be refactored).
Programming languages evolve in response to external and internal factors. External factors include new hardware, new theory or foundational research, trends or fashions in languages, and applications. Internal factors include a change in the way a language is used or the discovery of problems or deficiencies with existing features. However, evolving programming languages can be problematic; evaluating the impact of changes is difficult and it is often unclear how to effectively co-evolve software written in the language. The PLE workshop was initiated to bring together researchers to discuss and tackle these problems.The papers and talks presented at this workshop provided an excellent start to this new workshop and raised many interesting issues relating to programming language evolution. This document briefly reports on the activities of the first workshop, which was co-located with ECOOP 2014 in Uppsala, Sweden, July 2014.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.