Automated Summarization of Service Workflows to Facilitate

Discovery and Composition

Panagiotis Kotsikoris, Theodore Chaikalis

, Apostolos Ampatzoglou

and Alexander Chatzigeorgiou

Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece

Keywords: BPMN, Business Processes, Service Discovery, Workflow Composition, Cloud Software Development.

Abstract: The last decade marked undeniably the leading role of web services and the establishment of service-oriented

architectures. Indeed, it is nowadays hard to find a contemporary software application that does not use at

least one third-party web service. The main driver for this paradigm shift, lies in the benefits that decoupled,

cloud-based services bring to software development, operation and maintenance as well as at the seamless

deployment, integration and scalability features those modern public clouds provide. Furthermore, the

widespread adoption of services has led to the consequent demand for a structured and accessible method for

automatic service categorization, documentation, and identification, so that all available web services can be

easily identified and used from possible clients. In the realm of web services, service compositions known as

workflows provide a natural way to automate existing business processes and bridge the gap between technical

and non-technical stakeholders. This work proposes an automatic documentation generator for business

processes which facilitates service discovery, based on automatic summarization of business processes

created through Business Process Model and Notation (BPMN)

1 INTRODUCTION

Rendering software functionality available on the

Internet through standardized messaging protocols

has become the primary method for developing

enterprise software systems. Web services are defined

as self-contained, modular and distributed

applications that can be deployed and invoked over

the network. More specifically, RESTful web

services are scalable and light-weight and are

commonly used to create APIs for web-based

applications (Richardson & Ruby, 2007). In the

REST architectural style, both data and functionality

are considered resources and are accessed

using Uniform Resource Identifiers (URIs). Services

can be composed to form workflows abstracting

existing business processes.

Business Process Model and Notation (BPMN) is

a widely established industry standard, capable of

graphically modelling business flows, whose

https://orcid.org/0000-0003-0501-3671

https://orcid.org/0000-0002-5764-7302

https://orcid.org/0000-0002-5381-8418

execution provides value to business stakeholders.

Through well-defined and intuitive semantics, BPMN

provides powerful capabilities that cover numerous

different business scenarios. Each process is divided

in multiple atomic and strictly defined steps which

occur between start and end nodes. For the visual

representation, BPMN uses nodes for modelling

actions and edges for connecting actions and thus

denoting the direction of the data and control flow.

The resulting process graph reflects also the

chronological order of execution.

BPMN has been initially released on 2007 and the

current official release is version 2.0.2 that has been

published in January 2014 (Geiger et al., 2016; OMG,

2007). The officially stated goal of the standard is to

provide a method for visualizing in a strict and

formal, yet intuitive and approachable way business

processes so that they can be developed from both

technical and non-technical stakeholders.

Kotsikoris, P., Chaikalis, T., Ampatzoglou, A. and Chatzigeorgiou, A.

Automated Summarization of Service Workﬂows to Facilitate Discovery and Composition.

DOI: 10.5220/0011036200003176

In Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2022), pages 317-326

ISBN: 978-989-758-568-5; ISSN: 2184-4895

317

While BPMN offers inherently a visual

representation of the modeled business process, it is

often convenient to have a brief, textual summary of

the underlying workflow. Apart from labeling

purposes, such summaries can be highly valuable for

identifying existing workflows in registries (through

keyword search). Furthermore, for platforms aiming

to assist web service developers in reusing similar

service compositions, textual summaries can be used

to enable service searching, identification and reuse.

In the SmartCLIDE H2020 project

we aim at

developing a smart cloud-native development

environment that will support creators of cloud

services in the discovery, creation, composition,

testing, and deployment of full-stack data-centered

services and applications in the cloud. Among other

features, SmartCLIDE facilitates workflow

development by recommending nodes, after seeking

similar workflows to the one that is being developed.

Workflow similarity is assessed based on the

similarity of their corresponding textual descriptions.

To this end, we have leveraged the power of Natural

Language Processing (NLP) techniques to

automatically extract a concise textual summary of an

existing workflow expressed in BPMN.

The rest of the paper is organized as follows.

Section 2 discusses related work on service

composition and NLP. In Section 3 we elaborate on

the adopted methodological approach and in Section

4 we present details about the implemented tool.

Section 5 discussed results from the evaluation of the

tool in an industrial context. Finally, we conclude in

Section 6.

2 RELATED WORK

As described above, numerous technologies have

been combined (web applications, BPMN, NLP etc.),

to achieve the greater goal of workflow

summarization. Each one of these technologies forms

an entire field of research and practice in the science

of information technology and has evolved largely

over time.

Discovering appropriate services and components

on the network (intranet or internet), in order to

enhance and extend an application under

development, is one of the holy grails in today’s

software development. Leveraging the power of

existing services can help us escape from the

hardcoded model used in monolithic application

https://smartclide.eu/

development which leads to dysfunctional and hard to

maintain collections of software modules. Messina et

al. (2016) describe a dynamic and flexible model to

keep track of all the components using a service

registry focusing on consistency during the discovery

process while having each part isolated from the

outside world.

Assuming there is an existing set of available

components, the next step is to combine them in a

way to achieve the desired functionality. The

composition of those parts is a concept where instead

of dealing with the infrastructure and the location of

the components, we focus on bringing the pieces

together to achieve a greater goal. Wu et al. (2015)

describe two basic ways to create compositions of

services. The first one is a manual process where user

decides which components will be combined and in

which way, leading to more meaningful outcomes at

the cost of significant effort and time, taking into

consideration the existence of thousands of available

options. The second way is an automated process

where a list of components is evaluated using

artificial intelligence approaches. Next, heuristic

functions decide the best option to use. The latter

approach yields less accurate compositions but offers

substantially higher performance. Both options have

their drawbacks, for example, for a user to evaluate a

set of services, he first needs to execute them in a

controlled environment, requiring a set of resources

and tools. On the other side, the automated process

also requires resources regarding the collection and

the evaluation of the services in a parallel or even

distributed way.

In order to generate an accurate definition of a

flow, we first need to deconstruct its individual parts

and classify them. In this process Natural Language

Processing (NLP) comes in hand. Alan Turing back

in 1950 was the first to conceive the idea that

determining or not whether a computer is truly

intelligent involves the generation of natural language

as criterion of intelligence. Since then, multiple

efforts were made to create chatbots or other

conversational agents that can interact with the

content in the same way that a person does. Lo et al.

(2017) performed an analysis on various

implementations to clarify how far we have come

today and the potentials that lie ahead. Most of those

implementations are naturally based on the English

language. Fahad et. al. (2018) discuss the techniques

used today using neural networks as a pillar for multi

document summarization, spell checking, speech

recognition etc.

ENASE 2022 - 17th International Conference on Evaluation of Novel Approaches to Software Engineering

318

The generation of natural language, which follows

initial text processing, is the process in which an

artefact or model is presented in text form as output.

McBurney et al. (2016) suggest a five-step procedure

in order to create a final text that can describe

accurately a block of code and compare the results

with the respective human generated descriptions. At

first, the content that will be used is selected, which

is a very crucial step that defines the quality of the

result. After the content filtering comes the

structuring, in which the sequence of the content is

defined. The third step is the lexicalization, where

more suitable words are selected when necessary.

After that comes aggregation of similar phrases and

sentences in order to remove any duplicate content.

The final step is the realization, where the content to

be exported is created taking into consideration the

respective grammar rules. Since this is a standalone

approach working automatically, it is effective to any

input given in any language.

For the BPMN 2.0 specification, Geiger et al

(2016) performed an analysis of the evolution of the

standard, while Falcone et al. (2017) focus on the

potential of the model that originates from its

appealing graphical presentation and attempt to

integrate it with other modelling and simulation

systems.

3 BACKGROUND

3.1 BPMN

An example of a simplified business process is

depicted in Figure 1 where a subset of the BPMN

elements are demonstrated. The diagram models a

process that gets triggered by a message which

denotes that a working group is active. Next a

scheduled activity triggers a repeating set of tasks

every Friday at 6 PM: First a system task checks the

status of the working group. Then a decision node

determines the execution path depending on the result

of whether the working group is active or not. It

‘active=true’, the execution continues to the task that

sends a current issue list and then it goes back to

waiting mode until the next Friday @ 6 PM.

Otherwise the process terminates.

Both system tasks in this process (i.e. checking the

working group’s status and sending the current issue

list) could be easily carried out by two respective Web

Services that can be triggered through special

configuration during the creation of the tasks.

Figure 1: Example BPMN process.

3.2 Text Summarization

Text summarization refers to any automated process

during which a program parses a set of sentences and

generates a meaningful summary. The main goal of

every summarization approach is to produce a precise

and concise summary that reflects the basic context

of the initial document and does not lack vital

information, neither includes unnecessary or

irrelevant details (Figure 2). Such approaches have

been extensively used in automatic text classification,

news article summarization and automatic title

production.

As a technical problem, automatic summarization

forms a challenging endeavour because unlike

humans who can scan through an article and quickly

grasp the context and the basic parts of the topic,

machine algorithms must overcome several non-

trivial problems for capturing the essence of a

document.

Figure 2: Text Summarization.

Three approaches are mainly used for automatic text

summarization (Widyassari et al., 2020).

 Extraction Based summarization, where content

only from the original document is being used. An

initial evaluation pass determines the parts that

should be kept and afterwards these parts are

merged for the final summary.

 Abstraction Based summarization, which attempts

to mimic the human approach. Initially, Natural

Language Understanding techniques are being used

for identifying the meaning of the document and

Automated Summarization of Service Workﬂows to Facilitate Discovery and Composition

319

afterwards the final summary is produced by

rephrasing versions of the basic parts.

 Aided summarization is a semi-automatic approach

which demands intervention by a human who will

pick the text parts that will form the final summary,

as well as the method that this should be performed.

This approach is used in occasions where high

accuracy is needed.

The first one, extraction-based approach is the one

that initially gained high popularity due to its

simplicity and the abundance of available

information. However, with the improvements in AI

and computing power a huge part of the

implementations has been move to the abstraction-

based approach (Widyassari et al., 2020), which can

lead to more meaningful summaries.

4 TOOL IMPLEMENTATION

The proposed tool forms part of a broader toolset

which aims at semi-automatic service discovery and

composition by using service descriptions that assist

users in selecting the service that suits their needs in

the most effective way. To this end, descriptions of

available service flows should automatically be

extracted by parsing BPMN diagrams. The

summative descriptions can be used as short textual

service documentation about the purpose and the

mechanics of each service composition. This process

produces a documented registry containing all

services/workflows that can be identified and reused.

The selection process takes place in two steps.

Initially users defined a set of keyword-based criteria

for a desired service (service composition) and the

system responds to this search by returning a list of

compositions that are the best possible fit the given

criteria. In the second step users read through returned

results and select the one that is the most suitable to

their demands.

The description of each service composition is

obtained by analysing the internal structure, the

underlying flow with all possible execution paths and

the naming of all BPMN elements (nodes). The

documentation is created by parsing the BPMN

representation files (XML structure) and yields a

textual description output using Natural Language

Processing and Natural Language Generation

techniques.

4.1 System Architecture

In this chapter a detailed analysis of all system

components is presented along with a description of

the steps that produce the service documentation.

The tool accepts one or more BPMN files as input,

processes them and outputs the produced

documentation in the form of text files in English

language (Figure 3). It becomes evident that for the

tool to operate correctly, the system should process

only BPMN processes described in English.

Figure 3: General analysis concept.

We opted for the Web as our deployment platform to

provide a multi-channel API that can be invoked both

by human users as well as from third-party web

services as shown in Figure 4. The proposed tool is

available both through a headless API and in the form

of a human reachable Website.

Figure 4: Application is available through both as a

headless API and as human reachable Website.

The backend has been developed using the Java

Spring Boot framework (Spring Boot, 2021) and the

public interface is exposed through a REST API.

Figure 5: Internal application Architecture.

The internal structure of the backend module is

depicted in Figure 5. The Controller module acts as

the gateway of the system as it handles external

requests, performs an initial input validation, and

returns responses to clients.

ENASE 2022 - 17th International Conference on Evaluation of Novel Approaches to Software Engineering

320

The Service component is responsible for

carrying out the actual business logic for the request.

It contains the source code that analyses a business

process and produces its documentation. Internally it

invokes the BPMN parser, which goes through all

XML nodes in the underlying XML file, models each

node as a Java Object, invokes the Natural Language

Processing Subsystem to semantically analyse the

retrieved information and finally generates output

with the Natural Language Generator. More details

about the core functionality of extracting a textual

summary are provided in the next subsection.

4.2 Analysis Engine

In this section a more in-depth analysis about the

automatic business process documentation engine is

presented. The overall flow which comprises three

sequential phases is depicted in Figure 6. Initially the

process is being parsed for the determination of all

nodes in the workflow. Then each node is visited and

documented, starting from the first node of the

process, the Start Event. Finally, all results are

collected and formatted to formulate the final

description.

Figure 6: Detailed analysis of processing flow.

Phase A - BPMN Parsing: This phase starts from the

cleaning task which removes redundant or

unnecessary parts of the process that would introduce

noise to the NLP algorithms later. The basic part of

this phase is the modelling of BPMN nodes into Java

Objects and the identification of connections among

them. This task ensures the validity of the given

process and facilitates the understanding of the

process flow.

Phase B – Documentation Generation: This phase

accepts the previously created objects with their

connections and creates a description for each one of

them. These descriptions are the parts that will

comprise the final documentation. The core algorithm

contains a recursive procedure which visits all

elements in specific order beginning from the Start

Event of the process.

Phase C – Response Preparation: After visiting all

nodes, all parts are aggregated to give the final result.

In the occurrence of back cycles in the flow, special

anchors in the form of markers are inserted to avoid

redundant text duplication. A second optimization

that is applied in this step is the removal of duplicated

phrases or sentences. Finally, the complete process

documentation is returned.

4.2.1 Documentation Process

The description of a process is created from the

concatenation of sentences produced from individual

parts. Apart from specific descriptions for each part

of the process, a general introductory sentence

offering a general overview of the process context, is

inserted at the begging of the documentation.

Table 1: Process characteristics.

Adjective Description

Scheduled

Process that contains a timer

event that controls the firing

time and date

Automatic No human interaction involve

Sin

le a

roval One ste

of a

roval onl

2-level approval Two approval steps

3-level approval Three approval steps

Multiple-level approval More than three approval steps

Short

(

sin

le-task

)

Process contains onl

one tas

Short

(

2-tasks

)

Process contains 2 tasks

Short

(

3-tasks

)

Process contains 3 tasks

Flat (no branching)

Process does not contain a

decision

atewa

Multi branched

Contains one or more decision

gateways.

Alternative endin

Contains multi

le endin

nodes

etitive A c

clic flow exists

Automated Summarization of Service Workﬂows to Facilitate Discovery and Composition

321

For the general description of the process

structure, the analysis engine searches for a set of

specific characteristics which map to adjectives that

are composed to form the desired phrase. Table 1

presents the characteristics along with a short

description.

The general description contains also a phrase

describing the process type which is deducted from

the analysis of the Start Node and the BPMN file

itself. Initially the algorithm attempts the retrieval of

the description of the Start Node, however if

description does not exist or is not usable (no noun),

the algorithm utilizes the name of the BPMN file that

contains the process. The reason for selecting these 2

elements is because BPMN process developers

describe them in a way that differentiates one process

from the others.

5 USE CASES AND EVALUATION

This section presents several use cases with results of

the application of the proposed approach on synthetic

examples. Next the results from the empirical

evaluation of the results are presented. To validate the

quality of the extracted summaries we resorted to

industry professionals having experience in

creating/understanding process workflows as part of

their job.

5.1 Use Cases

Figure 7 depicts a simple book reservation process

with one human task and one system/notification

task.

Figure 7: Simple book reservation use case.

The automatic documentation of the process is as

follows:

Thisis a single approval, short (2‐task),

flatprocessaboutbookreservation.

Auserdecidesaboutthebookselection. A

sendtaskisusedtonotifyuser.

Thentheprocessends.

Bold Highlighted Text in Italics designates the

general description of the process from which we can

deduct that a user task exists (this enables the

characterization of the process as one with “approval

step”), that is a flat process (without alternative paths)

and that it contains two individual tasks and is about

a book reservation. Next, the process steps are

described in chronological order described in clear

text with zero formatting as it is deduced from the

order of nodes on the directed graph. Underlined

phrases indicate the description about the process

terminating node.

Figure 8: Example Business Process with decision

branching.

An example of a business process which contains

a decision node, which leads to two branches is

depicted in Figure 8. After book selection, an

availability check takes place and if the book is

available, the process notifies the user, otherwise it

immediately terminates.

The produced description follows. Note that the

general description here lacks the characterization

“flat” due to the branch existence.

This is a  single approval, short (2‐task)

processaboutbookreservation.

Auserdecidesaboutthebookselection. A

decisionistakendependingifreserved.

If available: A send task is used to

notifyuser.Thentheprocessends.

Ifunavailable:Thentheprocessends.



We proceed with a few more complicated

examples to highlight the strengths and weakness of

the proposed approach.

Figure 9: Process example with two decision branches.

Figure 9 presents a business process with two

decision gateways, which create three decision

branches. The produced description is as follows:

ENASE 2022 - 17th International Conference on Evaluation of Novel Approaches to Software Engineering

322

Thisis a single approval, short (3‐task),

alternateending(3‐ways)processaboutbook

reservation.

Auserdecidesaboutthebookselection.A

decisionistakendependingifreserved.

Ifavailable:Asendtaskisusedto

notifyuser.Thentheprocessends.

Ifunavailable:Adecisionistaken

dependingifreservedinbranchlibrary.

Ifavailableinbranchlibrary:Asend

taskisusedtonotifyuser.Thenthe

processends.

Ifunavailableinbranchlibrary:Then

theprocessends.



The process in Figure 10 contains an intermediate

throw signal event which means that when this node

is executed, a special signal is emitted to notify other

interested (subscribed) processes.

Figure 10: Process with an intermediate Catch Event.

The automatic description for the process with

intermediate signal throw as shown in Figure 10 is as

follows:

Thisis an automatic (not involving any

human task), short (2‐task), flat process

aboutbookreturn.

Amethodiscalledtosetbookstatus. The

processbroadcastsa signal. A  manual task

isusedtoupdatebookposition.

Thentheprocessends.

The process in Figure 11 introduces the concept

of End Message. This is a signal with a message

payload that is emitted at the end of the process to

notify any other interested processes (Message Start

events or intermediate message-catch events). The

same approach is being followed for all signal types

(Message, Error, Cancel, Link etc).

The automatically generated description follows.

Figure 11: Example Process with End Message Event.

Thisis an automatic (not involving any

human task), short (2‐task), flat process

aboutbookreturn.

A method is called to set book status. A

manualtaskisusedtoupdatebookposition.

The process concludes messaging a

participant.

Next, in Figure 12 a special process with a timer

controlled segment is depicted. A timer event is a

special type of node on which the execution flow

blocks until the timer expression that is declared

during node creation is fulfilled. The automatically

generated description follows.

Figure 12: Example process with Timer Event.

This is a scheduled, automatic (not

involving any humantask), short (2‐task),

repetitive process about book statuses.

[1]A decision istakendependingif books

exist.

Iftrue:Adailytimerisusedtorepeat

thefollowingprocess.Amethodiscalledto

get books. A method is called to set

statuses.Thesameflowisrepeated[1].

Iffalse:Thentheprocessends.

As it is readily observed, special anchors have been

used for the correct description of the flow circle in

order to avoid redundant phrase repetitions.

Figure 13 depicts a process with multiple levels

of approval and multiple endings. The corresponding

automatic documentation follows.

Figure 13: Example process with multiple approval levels

and multiple endings.

Automated Summarization of Service Workﬂows to Facilitate Discovery and Composition

323

This is an automatic (not involving any

human task), short (3‐task), alternate

ending (4‐ways) process about book

existence.

Amethodiscalledtocheckbook.

Adecisionistakendependingifexists.

Ifyes:Thentheprocessends.

Ifno:Amethodiscalledtocheckbook.

Adecisionistakendependingifexists.

Ifyes:Thentheprocessends.

Ifno:Amethodiscalledtocheckbook.

Adecisionistakendependingifexists.

Ifyes:Thentheprocessends.

Ifno:Thentheprocessends.

5.2 Evaluation

To evaluate the effectiveness of the proposed

methodology in capturing the essence of the actual

business process through the extracted summary, an

online questionnaire-based study was conducted. The

participants were nine industry professionals, all of

them highly skilled on BPMN process creation and

understanding.

Each participant was presented with 6 processes

along with the corresponding automatically generated

documentation and was asked to evaluate the extent

to which the documentation captured the actual

process and is meaningful. The responses were

provided in Likert scale (Joshi et al., 2015) as

depicted in Figure 14.

Figure 14: Indicative evaluation question.

The responses indicate a generally positive

opinion about the extracted summaries. A pie chart

with an overview of the evaluation results is

presented in Figure 15. It should be noted that none

of the participants voted with a value equal to 1. The

majority of evaluations correspond to values 4 and 5

(reaching a total 80%), highlighting the

expressiveness of the approach, at least for the used

case studies.

Figure 15: Distribution of evaluation responses.

The next section presents separate results for each

case along with the distribution of the evaluations

received, depicted as a bar chart. It is reasonable to

expect variations in the user acceptance of the

summaries, as some processes are more complex than

others. The results confirm this hypothesis, since

some of the most complex scenarios, such as case 6

(Fig. 21) and case 4 (Fig. 19) received fewer ‘5’

scores.

Figure 16: Evaluation case 1.

Figure 17: Evaluation case 2.

ENASE 2022 - 17th International Conference on Evaluation of Novel Approaches to Software Engineering

324

Figure 18: Evaluation case 3.

Figure 19: Evaluation case 4.

Figure 20: Evaluation case 5.

The evaluation results from this pilot evaluation on

the 6 presented cases are positive and demonstrate the

potential of the approach. Extracting a meaningful

summary becomes more challenging for processes

having multiple paths and more nodes. A possible

explanation of this phenomenon might be the fact that

longer processes produce longer documentation

segments which inevitably are less cohesive and more

difficult to express in natural language (even if a

human attempts to extract a summary for a highly

complex workflow the resulting text would seem less

‘natural’). Through the free text responses that were

allowed, the participants identified syntactic errors

that should be avoided.

We remind that the goal of extracting a summary

for a given workflow is not only the ‘labeling’ of a

process with text but to enable the searching for

similar workflows through similarity of their

corresponding description. While this experiment is

planned as future work we believe that the extracted

summaries capture both key aspects of the process

(such as decision nodes and timed events) and all

necessary terms that characterize nodes. Thus, we are

optimistic that performing similarity checks between

the textual summary of BPMN process will allow the

efficient identification of ‘similar’ workflows.

Figure 21: Evaluation case 6.

6 LIMITATIONS AND THREATS

TO VALIDITY

The proposed approach and accompanying tool

suffers from specific limitations which can be

addressed by more advanced NLP techniques. First of

all, the tool cannot parse workflows containing

descriptions in languages other than English, but if

such a need exists, the approach can be adapted

accordingly. The tool will also yield inaccurate

Automated Summarization of Service Workﬂows to Facilitate Discovery and Composition

325

summaries in case of missing verbs in BMPN

elements or for structures which exhibit substantial

complexity (e.g. in the case of tens of paths between

the start and end node). While there is no trivial way

to address such issues, human intervention to abstract

entire blocks of BPMN elements by tagging them

with an appropriate high-level description, might be

promising in this direction.

The performed study to evaluate the effectiveness

of the approach and tool is also subject to validity

threats. The number of employed evaluation cases in

limited, both in number and in terms of the underlying

domain. As a result, any claims about the potential of

the approach are subject to external validity threats

and the results cannot be generalized to other

domains. Furthermore, we acknowledge that using a

simple rating to capture the correctness,

meaningfulness and attractiveness of the extracted

summary might be insufficient to assess the pros and

cons of the approach. The relevant construct validity

threat can be addressed by more systematic and

larger-scale validations in the future. The low number

of participants did not allow any systematic statistical

analysis (e.g. to investigate inter-rater agreement) of

the findings. We plan to advance the statistical

conclusion validity during the case study on the

ability of using summaries to identify similar

processes.

7 CONCLUSIONS

Service-based software systems have become

mainstream in various domains as the benefits of

using and composing individual services towards

reduced development time, better scalability and

easier maintainability are well acknowledged and

documented. Modeling real business processes as

BPMN workflows where individual nodes

correspond to invoked services has great potential to

lower the entry barriers to system development.

However, findings workflows which are similar to the

targeted one, so as to reuse previous services is

challenging.

To address this problem, as part of the

SmartCLIDE H2020 project, we have developed an

approach and accompanying tool that automatically

extracts summaries from a BPMN process. By

providing as input the process file a textual summary

is extracting leveraging NLP techniques. A pilot

evaluation with 9 industry professionals revealed a

positive reception of the generated summaries. As a

next step, we plan to evaluate the efficacy of textual

summaries as a means for finding similar workflows.

ACKNOWLEDGEMENTS

Work reported in this paper has received funding

from the European Union’s Horizon 2020 research

and innovation programme under grant agreement No

871177 (project: SmartCLIDE).

REFERENCES

Fahad, S. A., & Yahya, A. E. (2018). Inflectional Review

of Deep Learning on Natural Language Processing.

2018 International Conference on Smart Computing

and Electronic Enterprise (ICSCEE). https://doi.

org/10.1109/ICSCEE.2018.8538416

Falcone, A., Garro, A., D’Ambrogio, A., & Giglio, A.

(2017). Engineering systems by combining BPMN and

HLA-based distributed simulation. 2017 IEEE

International Systems Engineering Symposium (ISSE),

1–6. https://doi.org/10.1109/SysEng.2017.8088302

Geiger, M., Harrer, S., Lenhard, J., & Wirtz, G. (2016). On

the Evolution of BPMN 2.0 Support and

Implementation. 2016 IEEE Symposium on Service-

Oriented System Engineering (SOSE), 101–110.

https://doi.org/10.1109/SOSE.2016.39

Joshi, A., Kale, S., Chandel, S., & Pal, D. K. (2015). Likert

scale: Explored and explained. British Journal of

Applied Science & Technology, 7(4), 396.

McBurney, P. W., & McMillan, C. (2016). Automatic

Source Code Summarization of Context for Java

Methods. IEEE Transactions on Software Engineering,

42(2), 103–119. https://doi.org/10.1109/TSE.2015.24

65386

Messina, A., Rizzo, R., Storniolo, P., & Urso, A. (2016,

June 26). A Simplified Database Pattern for the

Microservice Architecture. https://doi.org/10.13140/R

G.2.1.3529.3681

OMG. (2007). BPMN Specification—Business Process

Model and Notation. https://www.bpmn.org/

Richardson, L., & Ruby, S. (2007). RESTful Web Services

(1st ed.). O’Reilly Media.

Spring Boot. (2021). https://spring.io/projects/spring-boot

Widyassari, A. P., Rustad, S., Shidik, G. F., Noersasongko,

E., Syukur, A., Affandy, A., & Setiadi, D. (2020).

Review of automatic text summarization techniques &

methods. https://doi.org/10.1016/j.jksuci.2020.05.006

Wu, Z., Deng, S., & Wu, J. (2015). Chapter 7—Service

Composition. In Z. Wu, S. Deng, & J. Wu (Eds.),

Service Computing (pp. 177–227). Academic Press.

https://doi.org/10.1016/B978-0-12-802330-3.00007-2

ENASE 2022 - 17th International Conference on Evaluation of Novel Approaches to Software Engineering

326