7,470 research outputs found
Opaque Service Virtualisation: A Practical Tool for Emulating Endpoint Systems
Large enterprise software systems make many complex interactions with other
services in their environment. Developing and testing for production-like
conditions is therefore a very challenging task. Current approaches include
emulation of dependent services using either explicit modelling or
record-and-replay approaches. Models require deep knowledge of the target
services while record-and-replay is limited in accuracy. Both face
developmental and scaling issues. We present a new technique that improves the
accuracy of record-and-replay approaches, without requiring prior knowledge of
the service protocols. The approach uses Multiple Sequence Alignment to derive
message prototypes from recorded system interactions and a scheme to match
incoming request messages against prototypes to generate response messages. We
use a modified Needleman-Wunsch algorithm for distance calculation during
message matching. Our approach has shown greater than 99% accuracy for four
evaluated enterprise system messaging protocols. The approach has been
successfully integrated into the CA Service Virtualization commercial product
to complement its existing techniques.Comment: In Proceedings of the 38th International Conference on Software
Engineering Companion (pp. 202-211). arXiv admin note: text overlap with
arXiv:1510.0142
A Short Survey on Data Clustering Algorithms
With rapidly increasing data, clustering algorithms are important tools for
data analytics in modern research. They have been successfully applied to a
wide range of domains; for instance, bioinformatics, speech recognition, and
financial analysis. Formally speaking, given a set of data instances, a
clustering algorithm is expected to divide the set of data instances into the
subsets which maximize the intra-subset similarity and inter-subset
dissimilarity, where a similarity measure is defined beforehand. In this work,
the state-of-the-arts clustering algorithms are reviewed from design concept to
methodology; Different clustering paradigms are discussed. Advanced clustering
algorithms are also discussed. After that, the existing clustering evaluation
metrics are reviewed. A summary with future insights is provided at the end
- …