17 research outputs found
A Maturity Assessment Framework for Conversational AI Development Platforms
Conversational Artificial Intelligence (AI) systems have recently
sky-rocketed in popularity and are now used in many applications, from car
assistants to customer support. The development of conversational AI systems is
supported by a large variety of software platforms, all with similar goals, but
different focus points and functionalities. A systematic foundation for
classifying conversational AI platforms is currently lacking. We propose a
framework for assessing the maturity level of conversational AI development
platforms. Our framework is based on a systematic literature review, in which
we extracted common and distinguishing features of various open-source and
commercial (or in-house) platforms. Inspired by language reference frameworks,
we identify different maturity levels that a conversational AI development
platform may exhibit in understanding and responding to user inputs. Our
framework can guide organizations in selecting a conversational AI development
platform according to their needs, as well as helping researchers and platform
developers improving the maturity of their platforms.Comment: 10 pages, 10 figures. Accepted for publication at SAC 2021:
ACM/SIGAPP Symposium On Applied Computin
Seamless Variability Management With the Virtual Platform
Customization is a general trend in software engineering, demanding systems
that support variable stakeholder requirements. Two opposing strategies are
commonly used to create variants: software clone & own and software
configuration with an integrated platform. Organizations often start with the
former, which is cheap, agile, and supports quick innovation, but does not
scale. The latter scales by establishing an integrated platform that shares
software assets between variants, but requires high up-front investments or
risky migration processes. So, could we have a method that allows an easy
transition or even combine the benefits of both strategies? We propose a method
and tool that supports a truly incremental development of variant-rich systems,
exploiting a spectrum between both opposing strategies. We design, formalize,
and prototype the variability-management framework virtual platform. It bridges
clone & own and platform-oriented development. Relying on
programming-language-independent conceptual structures representing software
assets, it offers operators for engineering and evolving a system, comprising:
traditional, asset-oriented operators and novel, feature-oriented operators for
incrementally adopting concepts of an integrated platform. The operators record
meta-data that is exploited by other operators to support the transition. Among
others, they eliminate expensive feature-location effort or the need to trace
clones. Our evaluation simulates the evolution of a real-world, clone-based
system, measuring its costs and benefits.Comment: 13 pages, 10 figures; accepted for publication at the 43rd
International Conference on Software Engineering (ICSE 2021), main technical
trac
Variability Representations in Class Models: An Empirical Assessment. Replication Package
Questionnaires, example models, result data, analysis script
Variability representations in class models: an empirical assessment
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