11,676 research outputs found
Classifying and evaluating architecture design methods
The concept of software architecture has gained a wide popularity and is generally considered to play a fundamental role in coping with the inherent difficulties of the development of large-scale and complex software systems. This document first gives a definition of architectures. Second, a meta-model for architecture design methods is presented. This model is used for classifying and evaluating various architecture design approaches. The document concludes with the description of the identified problems
Grand Challenges of Traceability: The Next Ten Years
In 2007, the software and systems traceability community met at the first
Natural Bridge symposium on the Grand Challenges of Traceability to establish
and address research goals for achieving effective, trustworthy, and ubiquitous
traceability. Ten years later, in 2017, the community came together to evaluate
a decade of progress towards achieving these goals. These proceedings document
some of that progress. They include a series of short position papers,
representing current work in the community organized across four process axes
of traceability practice. The sessions covered topics from Trace Strategizing,
Trace Link Creation and Evolution, Trace Link Usage, real-world applications of
Traceability, and Traceability Datasets and benchmarks. Two breakout groups
focused on the importance of creating and sharing traceability datasets within
the research community, and discussed challenges related to the adoption of
tracing techniques in industrial practice. Members of the research community
are engaged in many active, ongoing, and impactful research projects. Our hope
is that ten years from now we will be able to look back at a productive decade
of research and claim that we have achieved the overarching Grand Challenge of
Traceability, which seeks for traceability to be always present, built into the
engineering process, and for it to have "effectively disappeared without a
trace". We hope that others will see the potential that traceability has for
empowering software and systems engineers to develop higher-quality products at
increasing levels of complexity and scale, and that they will join the active
community of Software and Systems traceability researchers as we move forward
into the next decade of research
Active learning in annotating micro-blogs dealing with e-reputation
Elections unleash strong political views on Twitter, but what do people
really think about politics? Opinion and trend mining on micro blogs dealing
with politics has recently attracted researchers in several fields including
Information Retrieval and Machine Learning (ML). Since the performance of ML
and Natural Language Processing (NLP) approaches are limited by the amount and
quality of data available, one promising alternative for some tasks is the
automatic propagation of expert annotations. This paper intends to develop a
so-called active learning process for automatically annotating French language
tweets that deal with the image (i.e., representation, web reputation) of
politicians. Our main focus is on the methodology followed to build an original
annotated dataset expressing opinion from two French politicians over time. We
therefore review state of the art NLP-based ML algorithms to automatically
annotate tweets using a manual initiation step as bootstrap. This paper focuses
on key issues about active learning while building a large annotated data set
from noise. This will be introduced by human annotators, abundance of data and
the label distribution across data and entities. In turn, we show that Twitter
characteristics such as the author's name or hashtags can be considered as the
bearing point to not only improve automatic systems for Opinion Mining (OM) and
Topic Classification but also to reduce noise in human annotations. However, a
later thorough analysis shows that reducing noise might induce the loss of
crucial information.Comment: Journal of Interdisciplinary Methodologies and Issues in Science -
Vol 3 - Contextualisation digitale - 201
Grand Challenges of Traceability: The Next Ten Years
In 2007, the software and systems traceability community met at the first
Natural Bridge symposium on the Grand Challenges of Traceability to establish
and address research goals for achieving effective, trustworthy, and ubiquitous
traceability. Ten years later, in 2017, the community came together to evaluate
a decade of progress towards achieving these goals. These proceedings document
some of that progress. They include a series of short position papers,
representing current work in the community organized across four process axes
of traceability practice. The sessions covered topics from Trace Strategizing,
Trace Link Creation and Evolution, Trace Link Usage, real-world applications of
Traceability, and Traceability Datasets and benchmarks. Two breakout groups
focused on the importance of creating and sharing traceability datasets within
the research community, and discussed challenges related to the adoption of
tracing techniques in industrial practice. Members of the research community
are engaged in many active, ongoing, and impactful research projects. Our hope
is that ten years from now we will be able to look back at a productive decade
of research and claim that we have achieved the overarching Grand Challenge of
Traceability, which seeks for traceability to be always present, built into the
engineering process, and for it to have "effectively disappeared without a
trace". We hope that others will see the potential that traceability has for
empowering software and systems engineers to develop higher-quality products at
increasing levels of complexity and scale, and that they will join the active
community of Software and Systems traceability researchers as we move forward
into the next decade of research
Working Notes from the 1992 AAAI Workshop on Automating Software Design. Theme: Domain Specific Software Design
The goal of this workshop is to identify different architectural approaches to building domain-specific software design systems and to explore issues unique to domain-specific (vs. general-purpose) software design. Some general issues that cut across the particular software design domain include: (1) knowledge representation, acquisition, and maintenance; (2) specialized software design techniques; and (3) user interaction and user interface
An Analysis of Service Ontologies
Services are increasingly shaping the world’s economic activity. Service provision and consumption have been profiting from advances in ICT, but the decentralization and heterogeneity of the involved service entities still pose engineering challenges. One of these challenges is to achieve semantic interoperability among these autonomous entities. Semantic web technology aims at addressing this challenge on a large scale, and has matured over the last years. This is evident from the various efforts reported in the literature in which service knowledge is represented in terms of ontologies developed either in individual research projects or in standardization bodies. This paper aims at analyzing the most relevant service ontologies available today for their suitability to cope with the service semantic interoperability challenge. We take the vision of the Internet of Services (IoS) as our motivation to identify the requirements for service ontologies. We adopt a formal approach to ontology design and evaluation in our analysis. We start by defining informal competency questions derived from a motivating scenario, and we identify relevant concepts and properties in service ontologies that match the formal ontological representation of these questions. We analyze the service ontologies with our concepts and questions, so that each ontology is positioned and evaluated according to its utility. The gaps we identify as the result of our analysis provide an indication of open challenges and future work
Designing Software Architectures As a Composition of Specializations of Knowledge Domains
This paper summarizes our experimental research and software development activities in designing robust, adaptable and reusable software architectures. Several years ago, based on our previous experiences in object-oriented software development, we made the following assumption: ‘A software architecture should be a composition of specializations of knowledge domains’. To verify this assumption we carried out three pilot projects. In addition to the application of some popular domain analysis techniques such as use cases, we identified the invariant compositional structures of the software architectures and the related knowledge domains. Knowledge domains define the boundaries of the adaptability and reusability capabilities of software systems. Next, knowledge domains were mapped to object-oriented concepts. We experienced that some aspects of knowledge could not be directly modeled in terms of object-oriented concepts. In this paper we describe our approach, the pilot projects, the experienced problems and the adopted solutions for realizing the software architectures. We conclude the paper with the lessons that we learned from this experience
AI for the Common Good?! Pitfalls, challenges, and Ethics Pen-Testing
Recently, many AI researchers and practitioners have embarked on research
visions that involve doing AI for "Good". This is part of a general drive
towards infusing AI research and practice with ethical thinking. One frequent
theme in current ethical guidelines is the requirement that AI be good for all,
or: contribute to the Common Good. But what is the Common Good, and is it
enough to want to be good? Via four lead questions, I will illustrate
challenges and pitfalls when determining, from an AI point of view, what the
Common Good is and how it can be enhanced by AI. The questions are: What is the
problem / What is a problem?, Who defines the problem?, What is the role of
knowledge?, and What are important side effects and dynamics? The illustration
will use an example from the domain of "AI for Social Good", more specifically
"Data Science for Social Good". Even if the importance of these questions may
be known at an abstract level, they do not get asked sufficiently in practice,
as shown by an exploratory study of 99 contributions to recent conferences in
the field. Turning these challenges and pitfalls into a positive
recommendation, as a conclusion I will draw on another characteristic of
computer-science thinking and practice to make these impediments visible and
attenuate them: "attacks" as a method for improving design. This results in the
proposal of ethics pen-testing as a method for helping AI designs to better
contribute to the Common Good.Comment: to appear in Paladyn. Journal of Behavioral Robotics; accepted on
27-10-201
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