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Record W2117321860 · doi:10.1109/sose.2011.6139121

Transforming dynamic behavior specifications from activity diagrams to BPEL

2011· article· en· W2117321860 on OpenAlexaff
Nasser Mustafa, Gregor von Bochmann

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicService-Oriented Architecture and Web Services
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBusiness Process Execution LanguageComputer scienceActivity diagramWeb serviceSoftware engineeringBusiness processUnified Modeling LanguageBusiness Process Model and NotationAsynchronous communicationService-oriented architectureContext (archaeology)Component (thermodynamics)Distributed computingProgramming languageBusiness process modelingSoftwareWork in process

Abstract

fetched live from OpenAlex

The Service-Oriented Architecture (SOA) provided by the Web Services standards supports Model-Driven Development, it allows global business process models described in the Business Process Modeling Notation (BPMN) or as UML Activity Diagrams to be transformed into Web Services components specified by WSDL and/or BPEL. We have experimented the transformation of UML Activity Diagrams to several BPEL processes using the IBM Rational Software Architect (RSA) tool. These diagrams were derived from the specification of global system behavior where each activity may represent some collaboration between several system components in distributed systems. The derived component behaviors assure that the global behavior will be realized by coordinating the actions of the components through the exchange of asynchronous messages. In this paper, we describe how this method can be adapted to the context where the system components will be implemented as BPEL processes. We found out that the IBM Rational tool does not support some important asynchronous message exchange scenarios, and we describe here how the generated BPEL processes can be manually adapted. We also discuss some difficulties that arise in relation with input message buffering. since we assume that the received messages remain in a buffer pool until they are required by the destination process. This message buffering is largely provided by the BPEL execution environment. We explain in this paper how all these problems can be resolved by simple modifications of the automatically generated component behaviors in BPEL.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.858
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.248
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2011
Admission routes1
Has abstractyes

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