40,267 research outputs found
Adapting Quality Assurance to Adaptive Systems: The Scenario Coevolution Paradigm
From formal and practical analysis, we identify new challenges that
self-adaptive systems pose to the process of quality assurance. When tackling
these, the effort spent on various tasks in the process of software engineering
is naturally re-distributed. We claim that all steps related to testing need to
become self-adaptive to match the capabilities of the self-adaptive
system-under-test. Otherwise, the adaptive system's behavior might elude
traditional variants of quality assurance. We thus propose the paradigm of
scenario coevolution, which describes a pool of test cases and other
constraints on system behavior that evolves in parallel to the (in part
autonomous) development of behavior in the system-under-test. Scenario
coevolution offers a simple structure for the organization of adaptive testing
that allows for both human-controlled and autonomous intervention, supporting
software engineering for adaptive systems on a procedural as well as technical
level.Comment: 17 pages, published at ISOLA 201
Comparative Study on Agile software development methodologies
Today-s business environment is very much dynamic, and organisations are
constantly changing their software requirements to adjust with new environment.
They also demand for fast delivery of software products as well as for
accepting changing requirements. In this aspect, traditional plan-driven
developments fail to meet up these requirements. Though traditional software
development methodologies, such as life cycle-based structured and object
oriented approaches, continue to dominate the systems development few decades
and much research has done in traditional methodologies, Agile software
development brings its own set of novel challenges that must be addressed to
satisfy the customer through early and continuous delivery of the valuable
software. It is a set of software development methods based on iterative and
incremental development process, where requirements and development evolve
through collaboration between self-organizing, cross-functional teams that
allows rapid delivery of high quality software to meet customer needs and also
accommodate changes in the requirements. In this paper, we significantly
identify and describe the major factors, that Agile development approach
improves software development process to meet the rapid changing business
environments. We also provide a brief comparison of agile development
methodologies with traditional systems development methodologies, and discuss
current state of adopting agile methodologies. We speculate that from the need
to satisfy the customer through early and continuous delivery of the valuable
software, Agile software development is emerged as an alternative to
traditional plan-based software development methods. The purpose of this paper,
is to provide an in-depth understanding, the major benefits of agile
development approach to software development industry, as well as provide a
comparison study report of ASDM over TSDM.Comment: 25 pages, 25 images, 86 references used, with authors biographie
Ecosystem-Oriented Distributed Evolutionary Computing
We create a novel optimisation technique inspired by natural ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
genes which are distributed in a peer-to-peer network, operating continuously
in time; this process feeds a second optimisation based on evolutionary
computing that operates locally on single peers and is aimed at finding
solutions to satisfy locally relevant constraints. We consider from the domain
of computer science distributed evolutionary computing, with the relevant
theory from the domain of theoretical biology, including the fields of
evolutionary and ecological theory, the topological structure of ecosystems,
and evolutionary processes within distributed environments. We then define
ecosystem- oriented distributed evolutionary computing, imbibed with the
properties of self-organisation, scalability and sustainability from natural
ecosystems, including a novel form of distributed evolu- tionary computing.
Finally, we conclude with a discussion of the apparent compromises resulting
from the hybrid model created, such as the network topology.Comment: 8 pages, 5 figures. arXiv admin note: text overlap with
arXiv:1112.0204, arXiv:0712.4159, arXiv:0712.4153, arXiv:0712.4102,
arXiv:0910.067
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
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