Artificial intelligence in service-oriented software design

Rodríguez, Guillermo; Soria, Álvaro; Campo, Marcelo

doi:10.1016/j.engappai.2016.03.009

Cited by 32 publications

(15 citation statements)

References 79 publications

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“…SOA aim at integrating open software standards into firm specific solutions. Rodriguez et al [75] refer to service-oriented architectures (SOA) to explain how AI can support this process. AI assists developers in integrating different platforms into service-oriented designs and enhances the management of generic quality attributes.…”

Section: Ai At the Stage Of Software Testing And Integrationmentioning

confidence: 99%

“…AI captures conversation semantics prevalent in different web -based architectures and identifies unifying elements by pattern recognition. AI discovers similar architectures and eliminates redundant code units in the SOA and thus supports developers in clearing up software interfaces so that a contingent SOA tailored to the requirements of specific businesses results [75]. Fuzzing is an automated software testing technique that is not itself based on AI but sometimes combined with AI elements.…”

Section: Ai At the Stage Of Software Testing And Integrationmentioning

confidence: 99%

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Applications of AI in classical software engineering

Barenkamp¹,

Rebstadt

Thomas

2020

AI Perspect

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Although Artificial Intelligence (AI) has become a buzzword for self-organizing IT applications, its relevance to software engineering has hardly been analyzed systematically. This study combines a systematic review of previous research in the field and five qualitative interviews with software developers who use or want to use AI tools in their daily work routines, to assess the status of development, future development potentials and equally the risks of AI application to software engineering. The study classifies the insights in the software development life cycle. The analysis results that major achievements and future potentials of AI are a) the automation of lengthy routine jobs in software development and testing using algorithms, e.g. for debugging and documentation, b) the structured analysis of big data pools to discover patterns and novel information clusters and c) the systematic evaluation of these data in neural networks. AI thus contributes to speed up development processes, realize development cost reductions and efficiency gains. AI to date depends on man-made structures and is mainly reproductive, but the automation of software engineering routines entails a major advantage: Human developers multiply their creative potential when using AI tools effectively.

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Section: Ai At the Stage Of Software Testing And Integrationmentioning

confidence: 99%

Section: Ai At the Stage Of Software Testing And Integrationmentioning

confidence: 99%

Applications of AI in classical software engineering

Barenkamp¹,

Rebstadt

Thomas

2020

AI Perspect

View full text Add to dashboard Cite

show abstract

“…The use of such semantic descriptions enables a more flexible and expressive capability for discovery, composition, and execution of WS. Many research works have aimed at techniques of discovering, composing, or developing services as reviewed in Rao and Su [ 26 ], Lemos et al [ 27 ], and Rodriguez et al [ 28 ]. The need still exists for automatic WS composition to solve the problems within various domains.…”

Section: Literature Reviewmentioning

confidence: 99%

Graph-Based Semantic Web Service Composition for Healthcare Data Integration

Arch-int

Sonsilphong³

et al. 2017

Journal of Healthcare Engineering

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Within the numerous and heterogeneous web services offered through different sources, automatic web services composition is the most convenient method for building complex business processes that permit invocation of multiple existing atomic services. The current solutions in functional web services composition lack autonomous queries of semantic matches within the parameters of web services, which are necessary in the composition of large-scale related services. In this paper, we propose a graph-based Semantic Web Services composition system consisting of two subsystems: management time and run time. The management-time subsystem is responsible for dependency graph preparation in which a dependency graph of related services is generated automatically according to the proposed semantic matchmaking rules. The run-time subsystem is responsible for discovering the potential web services and nonredundant web services composition of a user's query using a graph-based searching algorithm. The proposed approach was applied to healthcare data integration in different health organizations and was evaluated according to two aspects: execution time measurement and correctness measurement.

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“…Service-Oriented Architecture (SOA) has been chosen by the software industry for building distributed and enterprise-wide software applications, which should be both high quality and adaptable to market changes. 1 SOA is widely adopted to realize efficient software development by reusing loosely coupled and standard interface-based services. 2 Typical service-oriented software development involves a complex suite of software and tools.…”

Section: Introductionmentioning

confidence: 99%

Flexible model specification and application for service-oriented software

et al. 2018

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Service-Oriented Architecture (SOA) has gained considerable popularity for the development of large-scale distributed software systems. The SOA paradigm promotes the reusability and integrability of software in heterogeneous environments by means of open standards. The existing problem is that every service-oriented software development project often requires a customized development process that provides specific service-oriented software in support of requirements unique to that project. To resolve this problem, this study proposes universal service-oriented software (USOS). USOS focuses on the general simulation technology and integrates many features, which are necessary to build a correct and efficient simulation system. It is aimed at providing a professional environment to reduce the cost of modeling as well as the execution time of the simulation for multi-type models. USOS supports many advanced features such as dynamic model template generation, multi-formalism model debugging, flexible model-driven application, etc. Developers may use USOS as an open framework to build, store, and verify models very efficiently. Based on USOS, we have developed a large-scale parallel simulation platform called SIM, which is oriented to test parallel and distributed architectures, and applications in the aerospace domain. This study introduces the distinctive features of USOS for practical implementation of SIM and validates the performance by a prototype system simulation.

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Artificial intelligence in service-oriented software design

Cited by 32 publications

References 79 publications

Applications of AI in classical software engineering

Applications of AI in classical software engineering

Graph-Based Semantic Web Service Composition for Healthcare Data Integration

Flexible model specification and application for service-oriented software

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