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ResumenAutomated Testing in Robotic Process Automation ProjectsJiménez Ramírez, Andrés; Montero Jesus, Chacon; Wojdynsky, Tomasz; González Enríquez, José. Actas de las XVI Jornadas de Ingeniería de Ciencia e Ingeniería de Servicios (JCIS 2021), 2021-09-22.Robotic Process Automation (RPA) has received increasing attention in recent years. It enables task automation by software components which interact with user interfaces in a similar way to that of humans. An RPA project lifecycle is closely resembling a software project one. However, in certain contexts (e.g., business process outsourcing), a testing environment is not always available. Thus, deploying the robots in the production environment entails high risk. To mitigate it, an innovative approach to automatically generate a testing environment and a test suite for an RPA project is presented. The activities of the humans whose processes are to be robotized are monitored and a UI log is confirmed. On one side, the test environment is generated as a fake application, which mimics the real environment by leveraging the UI log information. The control flow of the application is governed by an invisible control layer which decides which image to show depending on the interface actions that it receives. On the other side, the test case checks whether the robot can reproduce the behaviour of the UI log. Promising results were obtained and a number of limitations were identified such that it may be applied in more realistic domains. ResumenMethod to Improve the Early Stages of the Robotic Process Automation LifecycleJiménez Ramírez, Andrés; Reijers, Hajo A.; Barba, Irene; del Valle, Carmelo. Actas de las XVI Jornadas de Ingeniería de Ciencia e Ingeniería de Servicios (JCIS 2021), 2021-09-22.The robotic automation of processes is of much interest to organizations. A common use case is to automate the repetitive manual tasks (or processes) that are currently done by back-office staff through some information system (IS). The lifecycle of any Robotic Process Automation (RPA) project starts with the analysis of the process to automate. This is a very time-consuming phase, which in practical settings often relies on the study of process documentation. Such documentation is typically incomplete or inaccurate. To deploy robots in a production environment that are designed on such a shaky basis entails a high risk. This paper describes and evaluates a new proposal for the early stages of an RPA project: the analysis of a process and its subsequent design. The idea is to leverage the knowledge of back-office staff, which starts by monitoring them in a non-invasive manner. This is done through a screen-mouse-key-logger, i.e., a sequence of images, mouse actions, and key actions are stored along with their timestamps. The log which is obtained in this way is transformed into a UI log through image-analysis techniques (e.g., fingerprinting or OCR) and then transformed into a process model by the use of process discovery algorithms. We evaluated this method for two real-life, industrial cases. The evaluation shows clear and substantial benefits in terms of accuracy and speed. This paper presents the method, along with a number of limitations that need to be addressed such that it can be applied in wider contexts. ResumenA microservice composition approach based on the choreography of BPMN fragmentsValderas, Pedro; Torres, Victoria; Pelechano, Vicente. Actas de las XVI Jornadas de Ingeniería de Ciencia e Ingeniería de Servicios (JCIS 2021), 2021-09-22.This paper faces the challenge of defining a microservice composition approach that provides the benefits of orchestration and choreography composition mechanisms. The main goal is to provide a solution that allows developers to have a centralized model that describes the big picture of a microservice composition and also to have the possibility of executing the composition defined in this model through an event-based choreography. The modeling language used to create such centralized model is the one provided by the BPMN process diagram. In particular, we introduce a proposal that provides the possibility of 1) defining the microservice composition in a BPMN model to have the big picture of the whole composition, which facilitates further analysis and maintenance when requirements change, and 2) executing the BPMN model by following an event-based choreography to provide a high degree of decoupling and independence to implement and maintain microservices. To this end, the paper presents (1) a set of guidelines to create microservice compositions in BPMN models, split them into fragments, and distribute these fragments among microservices to be executed through an event-based choreography, (2) a microservice architecture defined to support the coexistence of the two descriptions of a composition (i.e., the big picture and the split one), and (3) tool support in order to imple-ment the proposed microservice architecture in Java/Spring technology. ResumenDMN4DQ: When data quality meets DMNValencia Parra, Álvaro; Varela Vaca, Ángel Jesús; Parody, Luisa; Caballero Muñoz-Reja, Ismael; Gómez-López, María Teresa. Actas de las XVI Jornadas de Ingeniería de Ciencia e Ingeniería de Servicios (JCIS 2021), 2021-09-22.Data is the cornerstone of critical business processes in most organisations. In this context, organisations are responsible for determining the characteristics that data must follow to decide whether or not to use it. The usability of data strongly depends on the context in which the data is to be employed, and on the context in which the data is generated. Bearing this in mind, we defined a methodology (DMN4DQ) to automatically generate recommendations on the usability of the data supported by a decision process, including a context-dependent data-quality assessment based on Business Rules for Data Decisions, modelled in a hierarchical structure. In order to support the decision process, and to enable data quality stewards to define their decision rules, we rely on Decision Model and Notation (DMN). This standard provides a declarative mechanism to define a decision logic model that is understandable by non-expert users. Our proposal is validated in a case study. We developed a tool (dmn4spark) to apply the decision logic to the dataset. After the execution, data stewards can filter non-usable data, reducing the risks associated with bad-quality data in their business process, and identifying the root cause of non-usable records. The usability of data depends on the data quality and on the context in which the data is used. Our methodology provides a hierarchy to integrate decision rules about (i) data values+ADs (ii) measurements of data quality dimensions+ADs (iii) assessment through the aggregation of dimensions, and (iv) data usability. The use of DMN makes the transformation of knowledge into a formal model easier, facilitating the automation of the generation of recommendations. ResumenContext-Aware Process Performance Indicator PredictionMárquez-Chamorro, Alfonso E.; Revoredo, Kate; Resinas Arias de Reyna, Manuel; Del Río Ortega, Adela; Santoro, Flavia; Ruiz Cortés, Antonio. Actas de las XVI Jornadas de Ingeniería de Ciencia e Ingeniería de Servicios (JCIS 2021), 2021-09-22.It is well-known that context impacts running instances of a process. Thus, defining and using contextual information may help to improve the predictive monitoring of business processes, which is one of the main challenges in process mining. However, identifying this contextual information is not an easy task because it might change depending on the target of the prediction. In this paper, we propose a novel methodology named CAP3 (Context-aware Process Performance indicator Prediction) which involves two phases. The first phase guides process analysts on identifying the context for the predictive monitoring of process performance indicators (PPIs), which are quantifiable metrics focused on measuring the progress of strategic objectives aimed to improve the process. The second phase involves a context-aware predictive monitoring technique that incorporates the relevant context information as input for the prediction. Our methodology leverages context-oriented domain knowledge and experts+IBk feedback to discover the contextual information useful to improve the quality of PPI prediction with a decrease of error rates in most cases, by adding this information as features to the datasets used as input of the predictive monitoring process. We experimentally evaluated our approach using two-real-life organizations. Process experts from both organizations applied CAP3 methodology and identified the contextual information to be used for prediction. The model learned using this information achieved lower error rates in most cases than the model learned without contextual information confirming the benefits of CAP3. This paper was published in IEEE Access, 2020, Vol. 8, pp. 222050 - 222063, doi: 10.1109/ACCESS.2020.3044670 ResumenMeasuring Performance in Knowledge-intensive ProcessesEstrada-Torres, Bedilia; Piccoli Richetti, Pedro Henrique; Del Río Ortega, Adela; Araujo Baião, Fernanda; Resinas Arias de Reyna, Manuel; Santoro, Flávia Maria; Ruiz Cortés, Antonio. Actas de las XVI Jornadas de Ingeniería de Ciencia e Ingeniería de Servicios (JCIS 2021), 2021-09-22.Knowledge-intensive Processes (KIPs) can be defined as a type of process that comprises sequences of activities based on intensive acquisition, sharing, storage, and (re)use of knowledge, whereby the amount of value added to the organization depends on the knowledge of the actors involved. Among other characteristics, KIPs are usually non-repeatable, collaboration-oriented, unpredictable, and, in many cases, driven by implicit knowledge, derived from the capabilities and previous experiences of participants. Despite the growing body of research focused on understanding KIPs and on proposing systems to support these KIPs, the research question on how to define performance measures in this context remains open. In this article, we address this issue with a proposal to enable the performance management of KIPs. Our approach comprises an ontology that allows us to define process performance indicators (PPIs) in the context of KIPs, and a methodology that builds on the ontology and the concepts of lead and lag indicators to provide process participants with actionable guidelines that help them conduct the KIP in a way that fulfills a set of performance goals. Both the ontology and the methodology were applied to a case study of a real organization in Brazil to manage the performance of an Incident management process within a communication technology outsourcing company. The insights provided by our approach were considered highly valuable by the company.