Yitong Wang, Xue Han, WenChun Gao, Qian Hu, Jiahui Wang, Ziqing Wang, Lijun Mei, Junlan Feng. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Automatic business process discovery from textual process documentation is highly desirable to reduce the time and cost of Business Process Management (BPM) implementation in organizations. However, existing automatic process discovery approaches mainly focus on identifying activities out of the documentations. Deriving the hierarchical structural relationships between activities, which is important in the whole process discovery scope, requires great human labeling effort and is still a challenge. Facing this challenge, we propose to retrieve the latent hierarchical structure present in the textual process documentation by building a neural network without any extra human-labeled knowledge. The proposed neural network leverages a novel recurrent architecture, Ordered Neurons LSTM (ON-LSTM), with a process-level language model objective. On the base of this, we provide an automatic business process service (A-BPS) which could support the goal of automatically discovering business processes out of the uploaded documentation and generate Business Process Model and Notation (BPMN) scripts to represent the discovered process model through a service ecosystem. Experimental results show that 58.76% of the hierarchical structures could be correctly retrieved. A-BPS could generate 32% of the whole BPMN process model with average execution time 5-8 minutes. The time benefit of A-BPS is up to 40% reduction.

Natural Language Processing (NLP) is a key technique to automate Business Process Management (BPM) at different levels. The performance of existing NLP based BPM methods suffer from the limited accuracy of Part of Speech (POS) tagging, which is a key step in NLP pipelines. Note that the performance of POS tagging highly depends on the domain of annotated training data. However, most state-of-the-art POS taggers are trained from corpus in newswire domain which usually have different syntax features with business process description (BPD). The syntax features of BPD domain include usually starting with an imperative verb and containing numerous out-of-vocabulary (OOV) words. In this paper, we propose a novel POS tagging framework to tackle these problems. The main idea is that syntax feature of starting with imperative verb could be studied by enhancing the proportion of correctly POS-annotated imperative sentences in the training data. The trained POS tagger could reduce the overall POS tagging error by nearly 12% compared with newswire trained POS tagger. For verbs which are key words in BPD, the tagging precision could be increased by 27%. The lexical ambiguity caused by OOV words is solved by extracting local contextual knowledge out of images which are attached to help users understand the process better. Experimental results show that the overall POS tagging accuracy could be increased by nearly 10% with contextual OOV knowledge.

With the rapid growth of cloud computing, developing business applications on Platform-as-a-Service (PaaS) systems is increasingly popular among industry companies. Various services are developed to support different business requirements on PaaS systems. However, to the best of our knowledge, currently there is no service that provides mobile social messaging services to enable users of their apps to share messages in their social networks (e.g., We Chat, Whats App, Kaka Talk). Lack of such mobile social messaging services prevents industry companies from succeeding in drastic market competitions (e.g., Capture high customer satisfactory). In this paper, we propose a service-based framework to enable the mobile social messaging in PaaS systems (e.g., IBM Blue mix). Using this framework, developers can focus on the service encapsulation of existing applications, and define their business process flows via the conversation management in our platform (no coding work is needed). As such, our framework can effectively reduce the development workload for mobile social messaging in PaaS systems.

Application Maintenance Services (AMS) is essential for applications executed on servers to function properly. Its objective is to reduce the application incidents happened and quickly recover services from application failures/issues. The application incidents defined as events when there are some application failures/issues happened are major concerns of AMS, therefore we propose a second order-based anomaly detection method to describe and predict application incidents based on analysis of monitored server traffic metrics. The proposed method first detects anomalies for each metric, second builds the linkage between detected anomalies for all metrics of the server and application incidents, and then predicts potential application incidents. Through the experiments, we find that the presented method provides satisfactory results for identify application incident, which gives more than 90 percentage recall rate while about 65 percentage precision rate.

Many composite workflow services utilize non-imperative XML technologies such as WSDL, XPath, XML schema, and XML messages. Regression testing should assure the services against regression faults that appear in both the workflows and these artifacts. In this paper, we propose a refinement-oriented level-exploration strategy and a multilevel coverage model that captures progressively the coverage of different types of artifacts by the test cases. We show that by using them, the test case prioritization techniques initialized on top of existing greedy-based test case prioritization strategy form a subsumption hierarchy such that a technique can produce more test suite permutations than a technique that subsumes it. Our experimental study of a model instance shows that a technique generally achieves a higher fault detection rate than a subsumed technique, which validates that the proposed hierarchy and model have the potential to improve the cost-effectiveness of test case prioritization techniques.

An external web service may evolve without prior notification. In the course of the regression testing of a workflow-based web service, existing test case prioritization techniques may only verify the latest service composition using the not-yet-executed test cases, overlooking high-priority test cases that have already been applied to the service composition before the evolution. In this paper, we propose Preemptive Regression Testing (PRT), an adaptive testing approach to addressing this challenge. Whenever a change in the coverage of any service artifact is detected, PRT recursively preempts the current session of regression test and creates a sub-session of the current test session to assure such lately identified changes in coverage by adjusting the execution priority of the test cases in the test suite. Then, the sub-session will resume the execution from the suspended position. PRT terminates only when each test case in the test suite has been executed at least once without any preemption activated in between any test case executions. The experimental result confirms that testing workflow-based web service in the face of such changes is very challenging; and one of the PRT-enriched techniques shows its potential to overcome the challenge.

In real life, a tester can only afford to apply one test case prioritization technique to one test suite against a service-oriented workflow application once in the regression testing of the application, even if it results in an adverse scenario such that the actual performance in the test session is far below the average. It is unclear whether the factors of test case prioritization techniques known to be significant in terms of average performance can be extrapolated to adverse scenarios. In this paper, we examine whether such a factor or technique may consistently affect the rate of fault detection in both the average and adverse scenarios. The factors studied include prioritization strategy, artifacts to provide coverage data, ordering direction of a strategy, and the use of executable and non-executable artifacts. The results show that only a minor portion of the 10 studied techniques, most of which are based on the iterative strategy, are consistently effective in both average and adverse scenarios. To the best of our knowledge, this paper presents the first piece of empirical evidence regarding the consistency in the effectiveness of test case prioritization techniques and factors of service-oriented workflow applications between average and adverse scenarios.

Many web services represent their artifacts in the semi-structural format. Such artifacts may or may not be structurally complex. Many existing test case prioritization techniques however treat test cases of different complexity generically. In this paper, we exploit the insights on the structural similarity of XML-based artifacts between test cases, and propose a family of test case prioritization techniques that iteratively selects test case pairs without replacement. The validation experiment shows that these techniques can be more cost-effective than the studied existing techniques in exposing faults.

A workflow-based web service may use ultra-late binding to invoke external web services to concretize its implementation at run time. Nonetheless, such external services or the availability of recently used external services may evolve without prior notification, dynamically triggering the workflow-based service to bind to new replacement external services to continue the current execution. Any integration mismatch may cause a failure. In this paper, we propose Preemptive Regression Testing (PRT), a novel testing approach that addresses this adaptive issue. Whenever such a late-change on the service under regression test is detected, PRT preempts the currently executed regression test suite, searches for additional test cases as fixes, runs these fixes, and then resumes the execution of the regression test suite from the preemption point.

Immersive Web (IW for short) applications such as Second Life and SimCity are increasingly popular among individual users and companies. However, it is difficult to build a hybrid Immersive Web application, because of the different architectures used for building such IW applications. In this paper, we propose a service-oriented framework to support the mashup of heterogeneous IW applications for hybrid IW applications. We first model each object in IW (e.g., a virtual album or a virtual room) as an IW Object Service (IWOS) located by a unique URL with standardized service interfaces. As such, one IW application can use an IWOS instance in another IW application. Moreover, non-IW applications (e.g., an online album system) can also access an IWOS instance via its service interface. We further propose a service-oriented framework to manipulate these IWOS instances, to support the transition of these objects mainly between different IW applications, and to enable the transition between IW applications and non-IW applications. Our framework also includes the fundamental services to build hybrid IW applications. Finally, we provide examples to demonstrate the advantages of our framework, and conduct a qualitative analysis to show the effectiveness.

What is a cloud application precisely? In this paper, we formulate a computing cloud as a kind of graph, a computing resource such as services or intellectual property access rights as an attribute of a graph node, and the use of a resource as a predicate on an edge of the graph. We also propose to model cloud computation semantically as a set of paths in a subgraph of the cloud such that every edge contains a predicate that is evaluated to be true. Finally, we present algorithms to compose cloud computations and a family of model-based testing criteria to support the testing of cloud applications.

Service computing has increasingly been adopted by the industry, developing business applications by means of orchestration and choreography. Choreography specifies how services collaborate with one another by defining, say, the message exchange, rather than via the process flow as in the case of orchestration. Messages sent from one service to another may require the use of different XPaths to manipulate or extract message contents. Mismatches in XML manipulations through XPaths (such as to relate incoming and outgoing messages in choreography specifications) may result in failures. In this paper, we propose to associate XPath Rewriting Graphs (XRGs), a structure that relates XPath and XML schema, with actions of choreography applications that are skeletally modeled as labeled transition systems. We develop the notion of XRG patterns to capture how different XRGs are related even though they may refer to different XML schemas or their tags. By applying XRG patterns, we successfully identify new data flow associations in choreography applications and develop new data flow testing criteria. Finally, we report an empirical case study that evaluates our techniques. The result shows our techniques are promising in detecting failures in choreography applications.

Regression testing assures the quality of modified service-oriented business applications against unintended changes. However, a typical regression test suite is large in size. Earlier execution of those test cases that may detect failures is attractive. Many existing prioritization techniques order test cases according to their respective coverage of program statements in a previous version of the application. On the other hand, industrial service-oriented business applications are typically written in orchestration languages such as WS-BPEL and integrated with workflow steps and web services via XPath and WSDL. Faults in these artifacts may cause the application to extract wrong data from messages, leading to failures in service compositions. Surprisingly, current regression testing research hardly considers these artifacts. We propose a multilevel coverage model to capture the business process, XPath, and WSDL from the perspective of regression testing. We develop a family of test case prioritization techniques atop the model. Empirical results show that our techniques can achieve significantly higher rates of fault detection than existing techniques.

Cloud computing is an emerging computing paradigm. It aims to share data, calculations, and services transparently among users of a massive grid. Although the industry has started selling cloud-computing products, research challenges in various areas, such as UI design, task decomposition, task distribution, and task coordination, are still unclear. Therefore, we study the methods to reason and model cloud computing as a step toward identifying fundamental research questions in this paradigm. In this paper, we compare cloud computing with service computing and pervasive computing. Both the industry and research community have actively examined these three computing paradigms. We draw a qualitative comparison among them based on the classic model of computer architecture. We finally evaluate the comparison results and draw up a series of research questions in cloud computing for future exploration.

WS-BPEL applications are a kind of service-oriented application. They use XPath extensively to integrate loosely-coupled workflow steps. However, XPath may extract wrong data from the XML messages received, resulting in erroneous results in the integrated process. Surprisingly, although XPath plays a key role in workflow integration, inadequate researches have been conducted to address the important issues in software testing. This paper tackles the problem. It also demonstrates a novel transformation strategy to construct artifacts. We use the mathematical definitions of XPath constructs as rewriting rules, and propose a data structure called XPath Rewriting Graph (XRG), which not only models how an XPath is conceptually rewritten but also tracks individual rewritings progressively. We treat the mathematical variables in the applied rewriting rules as if they were program variables, and use them to analyze how information may be rewritten in an XPath conceptually. We thus develop an algorithm to construct XRGs and a novel family of data flow testing criteria to test WS-BPEL applications. Experiment results show that our testing approach is promising.

In service computing, the behavior of a service may evolve. When an organization develops a service-oriented application in which certain services are provided by external partners, the organization should address the problem of uninformed behavior evolution of external services. This paper proposes an adaptive framework that bars problematic external services to be used in the service-oriented application of an organization. We use dynamic WSDL information in public service registries to approximate a snapshot of a network of services, and apply link analysis on the snapshot to identify services that are popularly used by different service consumers at the moment. As such, service composition can be strategically formed using the highly referenced services. We evaluate our proposal through a simulation study. The results show that, in terms of the number of failures experienced by service consumers, our proposal significantly outperforms the random approach in selecting reliable services to form service compositions.