Q-Rapids: Quality-Aware Rapid Software Development: an H2020 Project

This work reports the objectives, current state, and outcomes of the Q-Rapids H2020 project. Q-Rapids (Quality-Aware Rapid Software Development) proposes a data-driven approach to the production of software following very short development cycles. The focus of Q-Rapids is on quality aspects, represented through quality requirements. The Q-Rapids platform, which is the tangible software asset emerging from the project, mines software repositories and usage logs to identify candidate quality requirements that may ameliorate the values of strategic indicators like product quality, time to market or team productivity. Four companies are providing use cases to evaluate the platform and associated processes.Peer ReviewedPostprint (author's final draft

Quality of Service (QoS) in SOA Systems. A Systematic Review

In the last recent years a new technology called Web Services has emerged. The main characteristic of a web service is that it is a piece of software that the user can utilize but doesn’t own, that is, the user doesn’t install the software but uses it through the internet and standard protocols. With this new technology, a new architecture paradigm called SOA (Service Oriented Architecture) has appeared. This architecture is based on combining several web services, each one responsible to develop a concrete task, in order to obtain full‐operational software. The web services that compose a SOA System might be able to perform a task in a certain time, might be unavailable in some cases, might have security policies, etc. All this attributes, named Quality attributes, are essential in order to choose the appropriate web service for a SOA System. The objective of this Master Thesis is focused on two different but related subjects: (1) The development of a review regarding to the Quality Attributes for web services in a systematic manner and the development of a tool for monitoring SOA Systems capable to be used in several frameworks such as for Self‐Adaptive SOA Systems and for Web Service Discovery Systems

Monitoring the quality of service to support the service based system lifecycle

Service Oriented Computing (SOC) has been established in the last recent years as a successful paradigm in Software Engineering. The systems built under this paradigm, known as Service Based System (SBS), are composed of several services, which are usually third-party software run by external service providers. SBS rely on these service providers to ensure that their services comply with the agreed Quality of Service (QoS). In contrast to other systems, the dynamic behaviour of SBS requires up-to-date QoS information for its proper management in the different stages of its lifecycle, from their initial construction until their decommission. Providing such QoS information has resulted in different technological solutions built around a monitor. Nonetheless, several research challenges in the field remain still open, ranging from theoretical aspects of quality assurance to architectonical challenges in decentralized monitoring. Based on the current research challenges for service monitoring, the research gaps in which we aim to contribute are twofold: - To investigate on the definition and structure of the different quality factors of services, and provide a framework of common understanding for the definition of what to monitor. - To investigate on the different features required to support the activities of the whole SBS lifecycle (i.e. how to monitor), and develop a monitoring framework that accomplishes such features. As a result of this thesis, we provide: What to monitor - A distribution of the quality models along the time dimension and the identification of their relationships. - An analysis of the size and definition coverage of the proposed quality models. - A quantified coverage of the different ISO/IEC 25010 quality factors given by the proposals. - The identification of the most used quality factors, and provided the most consolidated definitions for them. How to monitor - The elicitation of the requirements of the different activities in the SBS lifecycle. - The definition of the set of features that supports the elicited requirements. - A modular service-oriented monitoring framework, named SALMon, implementing the defined features. SALMon has been validated by including it in several frameworks supporting the different activities of the SBS lifecycle. Finally, we have conducted a performance evaluation of SALMon over real web services.La Computació Orientada a Serveis (SOC) ha esdevingut en els darrers anys un paradigma exitós en el camp de l'Enginyeria del Software. Els sistemes construïts sota aquest paradigma, coneguts com Sistemes Basats en Serveis (SBS), estan composats de diversos serveis, que són, usualment, programari de tercers executats per proveïdors de serveis externs. Els SBS depenen dels proveïdors dels serveis per garantir que els serveis compleixen amb la Qualitat del Servei (QoS) acordada. En contrast amb altres sistemes, el comportament dinàmic dels SBS requereix d'informació actualitzada del QoS per a la correcta administració de les diferents etapes del cicle de vida dels SBS: des de la seva construcció inicial fins a la seva clausura. Proveir d'aquesta informació de QoS ha resultat en diferents solucions tecnològiques construïdes al voltant d'un monitor. Malgrat això, diversos reptes de recerca en el camp encara romanen obertes, des d'aspectes teòrics de l'assegurança de qualitat, a reptes arquitectònics en la monitorització descentralitzada. Basat en els reptes de recerca actuals per a la monitorització de serveis, els forats de recerca en els que pretenem contribuir són dobles: - Investigar en la definició i estructura dels diferents factors de qualitat dels serveis, i proveir un marc de treball d'entesa comuna per a la definició de què monitoritzar. - Investigar en les diferents característiques requerides per donar suport a les activitats de tot el cicle de vida dels SBS (i.e. com monitoritzar), i desenvolupar una plataforma de monitorització que acompleixi aquestes característiques. Com a resultats de la tesis, proveïm: Què monitoritzar - Una distribució dels models de qualitat al llarg de la dimensió temporal i la identificació de les seves interrelacions. - Un anàlisi de la mida i definició de la cobertura dels models de qualitat proposats. - Una cobertura quantificada dels diferents factors de qualitat ISO/IEC 25010 donat en les diferents propostes. - La identificació dels factors de qualitat més utilitzats, i la definició dels termes més consolidats. Com monitoritzar - L'elicitació dels requeriments per a les diferents activitats en el cicle de vida dels SBS. - La definició del conjunt de característiques que donen suport als requeriments elicitats. - Una platforma modular orientada a serveis, anomenat SALMon, que implementa les característiques definides. SALMon ha estatvalidat incloent la plataforma en diversos marcs de treball donant suport a les diferents activitat

SALMon: A SOA system for monitoring service level agreements

In this paper we present SALMon, a tool assessing the satisfaction of service level agreement (SLA) clauses by service-oriented systems. SALMon itself is organized as a service-oriented system that offers two kind of services: 1) the Monitor service that measures the values in execution time of dynamic quality attributes (like response time or availability), and 2) the Analyzer service that detects and reports violations of SLA clauses from the values obtained with the Monitor. The SALMon tool is highly versatile, allowing: 1) both active testing and passive monitoring as strategies, 2) different types of technologies for the monitored/tested systems (e.g., Web services, RESTful services), 3) agile definition of measure instruments for new quality attributes. The service-oriented nature of SALMon makes it scalable and easy to integrate with other services that need its functionalities.Postprint (published version

End-user driven feedback prioritization

End-user feedback is becoming more important for the evolution of software systems. There exist various communication channels for end-users (app stores, social networks) which allow them to express their experiences and requirements regarding a software application. End-users communicate a large amount of feedback via these channels which leads to open issues regarding the use of end-user feedback for software development, maintenance and evolution. This includes investigating how to identify relevant feedback scattered across different feedback channels and how to determine the priority of the feedback issues communicated. In this research preview paper, we discuss ideas for enduser driven feedback prioritization.Peer ReviewedPostprint (published version

Monitoring the service-based system lifecycle with SALMon

Los Sistemas Basados en Servicios (SBS) son sistemas software altamente dinámicos compuestos por un conjunto de servicios web provenientes de distintos, y posiblemente heterogéneos, proveedores. En contraste con otros sistemas software tradicionales, el comportamiento dinámico de los SBS requiere de información actualizada sobre la calidad de servicio (QoS) para poder actuar i administrar correctamente las actividades en las distintas fases del ciclo de vida de los SBS (p.e., selección de servicios, despliegue, evaluación de niveles de acuerdo de servicio –SLA–, y adaptación). [...] Para cubrir esta brecha de investigación, presentamos SALMon, una plataforma de monitorización de servicios versátil que provee información acerca de la QoS según la forma y enfoque adecuado para las distintas actividades del ciclo de vida.Peer ReviewedPostprint (published version

Applying sentiment analysis on Spanish tweets using BETO

Emotion analysis of messages using machine learning techniques is a difficult and cumbersome task requiring a major effort to obtain reliable results. This challenge is even more pronounced when the target language is not English, but Spanish. To overcome this challenge, this paper describes how UPC Team applied sentiment analysis on social media messages (in particular, on Twitter) written in Spanish and, related to events that took place in April 2019 from different domains. To this aim, we present a machine learning model based on BERT and describe the results obtained to reach an accuracy of 65% approx. and the 12th position in the ranking, for this second edition of the contest for emotion detection of Spanish tweets [email protected] ReviewedPostprint (published version

Merging datasets for emotion analysis

Context. Applying sentiment analysis is in general a laborious task. Furthermore, if we add the task of getting a good quality dataset with balanced distribution and enough samples, the job becomes more complicated. Objective. We want to find out whether merging compatible datasets improves emotion analysis based on machine learning (ML) techniques, compared to the original, individual datasets. Method. We obtained two datasets with Covid-19-related tweets written in Spanish, and then built from them two new datasets combining the original ones with different consolidation of balance. We analyzed the results according to precision, recall, F1-score and accuracy. Results. The results obtained show that merging two datasets can improve the performance of ML models, particularly the F1-score, when the merging process follows a strategy that optimizes the balance of the resulting dataset. Conclusions. Merging two datasets can improve the performance of ML models for emotion analysis, whilst saving resources for labeling training data. This might be especially useful for several software engineering activities that leverage on ML-based emotion analysis techniques.This paper has been funded by the Spanish Ministerio de Ciencia e Innovación under project / funding scheme PID2020-117191RB.Peer ReviewedPostprint (author's final draft

Applying transfer learning to sentiment analysis in social media

Context: Sentiment analysis is an NLP technique that can be used to automatically obtain the sentiment of a crowd of end-users regarding a software application. However, applying sentiment analysis is a difficult task, especially considering the need of obtaining enough good quality data for training a Machine Learning (ML) model. To address this challenge, transfer learning can help us save time and get better performance results with a limited amount of data. Objective: In this paper, we aim at identifying to which degree transfer learning improves the results of sentiment analysis of messages shared by end-users in social media. Method: We propose a tool-supported framework able to monitor and analyze the sentiment of tweets with different ML models and settings. Using the proposed framework, we apply transfer learning and conduct a set of experiments with multiple datasets. Results: The performance of different ML models with transfer learning from different datasets are obtained and discussed, showing how different factors affect the results, and discussing how they have to be considered when applying transfer learning.This work has been partially supported by the Spanish project DOGO4ML (contract PID2020-117191RB-I00).Peer ReviewedPostprint (author's final draft

A context-aware monitoring architecture for supporting system adaptation and reconfiguration

Modern services and applications need to react to changes in their context (e.g. location, memory consumption, number of users) to improve the user’s experience. To obtain this context, a monitoring infrastructure with adequate functionality and quality levels is required. But this monitoring infrastructure needs to react to the context as well, raising the need for context-aware monitoring tools. Provide a generic solution for context-aware monitoring able to effectively react to contextual changes. We have designed CAMA, a service-oriented Context-Aware Monitoring Architecture that can be easily configured, adapted and evolved according to contextual changes. CAMA implements a decoupled architecture and manages a context domain ontology for modelling the inputs, outputs and capabilities of monitoring tools. CAMA has been demonstrated in three real use cases. We have also conducted different evaluations, including an empirical study. The results of the evaluations show that (1) the overhead introduced by the architecture does not degrade the behavior of the system, except in extreme conditions; (2) the use of ontologies is not an impediment for practitioners, even when they have little knowledge about this concept; and (3) the reasoning capabilities of CAMA enable context-aware adaptations. CAMA is a solution useful for both researchers and practitioners. Researchers can use this architecture as a baseline for providing different extensions or implementing new approaches on top of CAMA that require context-aware monitoring. Practitioners may also use CAMA in their projects in order to manage contextual changes in an effective way.This work was partially supported by the Spanish project GENESIS TIN2016-79269-R, and SUPERSEDE project, funded by the European Union’s Information and Communication Technologies Programme (H2020) under Grant Agreement No 644018.Peer ReviewedPostprint (author's final draft