In 2007, OASIS finalized their Business Process
Execution Language 2.0 (BPEL) specification which defines
an XML-based language for building orchestrations of Web
Services. As the validation of BPEL processes against the
official BPEL XML schema leaves room for a plethora of static
errors, the specification contains 94 static analysis rules to cover
all static errors. According to the specification, any violations
of these rules are to be checked by a standard conformant
engine at deployment time. When a violation is not detected
in BPEL processes during deployment, such errors remain
unnoticed until runtime, making them expensive to find and fix.
In this work, we investigate whether mature BPEL engines that
claimed standard conformance implement these static rules.
To answer this question, we formalize the static rules and
derive test cases based on these formalizations to evaluate
the degree of support for static analysis of six open source
BPEL engines using the BPEL Engine Test System (betsy). In
addition, we propose a method to get more accurate static
analysis conformance results by taking the feature conformance
of engines into account to exclude false positives in contrast
to the classic approach. The results reveal that support for
static analysis in these engines varies greatly, ranging from
nonexistent to full support. Furthermore, our proposed method
outperforms the classic one in terms of accuracy.
The selection of the best fitting process engine for
a specific project requires the evaluation of engines according
to various requirements. We focus on the non-functional
requirement robustness, which is critical in production environments
but hard to determine. Thus, we propose an evaluation
framework to reveal important robustness criteria of process
engines. In this work, we focus on message robustness, i.e., the
ability to handle the receipt of invalid messages appropriately.
In a case study comprising five open source BPEL engines, we
determine message robustness by injecting faults into robustly
designed processes as a reply to a previously sent request from
an external virtual service and assert their behavior. The results
show that the degree of message robustness significantly differs,
hence, robustly designed processes do not necessarily lead to
robust runtime behavior, the selected engines still play a major
The Web Services Business Process Execution language (BPEL) is a standard
for modeling and executing automated processes and is tailor-made for service
orchestration. BPEL specifies a serialization format which every BPEL implementation
has to understand, thus allowing for the portability of processes among runtime engines.
Although the modeling and execution of BPEL processes is portable between engines
to a large degree, the lifecycle management of BPEL processes is not standardized and
varies a lot for different engines. This paper presents a first approach for a uniform
and cloud-based lifecycle management of BPEL processes and engines. We infer a
uniform interface for the lifecycle management from the capabilities of current engines
and provide a prototypic implementation of a tool that manages processes and engines
on a TOSCA-compliant infrastructure.
Nowadays, business processes and their execution are corner stones in
modern IT landscapes, as multiple process languages and corresponding engines
for these languages have emerged. In practice, it is not feasible to select the best
fitting engine, as engine capabilities are mostly hidden in the engine implementation
and a comparison is hampered by the large differences and high adoption
costs of the engines. We aim to overcome these problems by a) introducing an
abstract layer to access the functionality of the engines uniformly, b) by revealing
the engine capabilities through automated and isolated tests for typical requirements,
and c) support the user in their selection of a process engine by determining
and explaining the fitness of the engines for a single process or a given set of
processes using policy matching against previously revealed engine capabilities.
Early results show the general feasibility of our approach for BPEL engines for a
Keywords: BPM, process engines, engine selection, execution requirements,
Despite the popularity of BPEL engines to orchestrate complex and executable processes, there are still only few approaches available to help find the most appropriate engine for individual requirements.
One of the more crucial factors for such a middleware product in industry are the performance characteristics of a BPEL engine.
There exist multiple studies in industry and academia testing the performance of BPEL engines, which differ in focus and method.
We aim to compare the methods used in these approaches and provide guidance for further research in this area.
Based on the related work in the field of performance testing, we created a process engine specific comparison framework, which we used to evaluate and classify nine different approaches that were found using the method of a systematical literature survey.
With the results of the status quo analysis in mind, we derived directions for further research in this area.
Today, a plethora of enterprise middleware solutions are available, leading to the problem of choosing the right tool for a specific use case.
Automated tests can support the selection of such software by determining decision relevant metrics, like e.g., throughput or the degree of standard conformance.
To avoid side effects between tests, test isolation, i.e., to provide fresh instances of the software for each test execution, is essential.
However, middleware suites are inherently complex, provide a large range of configuration options, have tedious or sometimes manual installation procedures, and long startup times.
These idiosyncrasies aggravate the creation of fresh instances of such middleware suites, leading to slower turnaround times and increasing the cost for ensuring test isolation.
We aim to overcome these issues with methods and tools from the area of virtualization and devops.
In this work, we focus on BPEL engines which are common middleware components in Web Service based SOAs.
We applied our proposed method to the BPEL Engine Test System (betsy), a conformance test suite and testing tool for BPEL engines.
Results reveal that our method a) enables automatic creation of fresh instances of software without manual installation steps, b) reduces the time to create these fresh instance dramatically, and c) introduces only a neglectable performance overhead, therefore, reducing the overall costs of testing complex software.
It is a long-standing debate, whether software that is developed as open source is generally of higher quality than proprietary software.
Although the open source community has grown immensely during the last decade, there is still no clear answer.
Service-oriented software and middleware tends to rely on highly complex and interrelated standards and frameworks.
Thus, it is questionable if small and loosely coupled teams, as typical in open source software development, can compete with major vendors.
Here, we focus on a central part of service-oriented software systems, i.e., process engines for service orchestration, and compare open source and proprietary solutions.
We use the Web Services Business Process Execution Language (BPEL) and compare standard conformance and its impact on language expressiveness in terms of workflow pattern support of eight engines.
The results show that, although the top open source engines are on par with their proprietary counterparts, in general proprietary engines perform better.
The errors in BPEL processes that are only detected at runtime are expensive to fix. Several modelers and process engines for BPEL exist, and the standard defines basic static analysis (SA) rules as a detection mechanism for invalid processes, but the actual conformance of BPEL modelers and engines regarding these rules is unknown. We propose to develop test cases to evaluate the conformance of BPEL modelers and engines regarding static analysis. The evaluation results enable decision makers to identify and use the most conformant engine and modeler that detect errors before runtime and therefore reduce costs