Tipo de publicación: Conference Paper

Publicado en: Proceedings of the 27th International Workshop on Software Measurement and 12th International Conference on Software Process and Product Measurement

Tipo de publicación: Conference Paper

Publicado en: XX Ibero-American Conference on Software Engineering (CibSE 2017)

Tipo de publicación: Journal Article

Publicado en: Empirical Software Engineering

Tipo de publicación: Conference Paper

Publicado en: Convention of Central America and Panama

Tipo de publicación: Conference Paper

Publicado en: Ibero-American Conference on Software Engineering

Tipo de publicación: Conference Paper

Publicado en: Product-Focused Software Process Improvement

Tipo de publicación: Journal Article

Publicado en: ACM SIGSOFT Software Engineering Notes

Tipo de publicación: Conference Paper

Publicado en: Ibero-American Conference on Software Engineering

Tipo de publicación: Conference Proceedings

Publicado en: I Encuentro Académico de la Facultad de Medicina: “Integramos nuestro quehacer por una mejor salud”

Tipo de publicación: Conference Proceedings

Publicado en: International Florida Artificial Intelligence Research Society Conference

Descripción:

Software organizations are continuously looking for techniques to increase the effectiveness and efficiency of their testing processes. Model-based testing (MBT) is an approach that automates the design and generation of test cases based on a model that represents the system under test. MBT can reduce the cost of software testing and improve the systems quality. However, the introduction of the MBT approach could be complex for software development teams in the industry. This paper replicates a previous study that evaluated the use of MBT by software engineers in an industrial context. The goal of this replication is to evaluate the feasibility and acceptance of the MBT approach from the perspective of quality engineers testing a software application in the industry. We conducted a case study with four quality assurance engineers who modeled one module of the system under test, and then generated and executed a set of test cases using an MBT tool. Participants were able to use MBT to model and test the software system and provided several insights about the challenges and opportunities of using this approach.

Tipo de publicación: Conference Paper

Publicado en: 14th Iberian Conference on Information Systems and Technologies (CISTI)

Descripción:

Context: Model-based testing is one of the most studied approaches by secondary studies in the area of software testing. Aggregating knowledge from secondary studies on model- based testing can be useful for both academia and industry. 

Objective: The goal of this study is to characterize secondary studies in model-based testing, in terms of the areas, tools and challenges they have investigated. 

Method: We conducted a tertiary study following the guidelines for systematic mapping studies. Our mapping included 22 secondary studies, of which 12 were literature surveys and 10 systematic reviews, over the period 1996–2016. 

Results: A hierarchy of model-based testing areas and subareas was built based on existing taxonomies as well as data that emerged from the secondary studies themselves. This hierarchy was then used to classify studies, tools, challenges and their tendencies in a unified classification scheme. We found that the two most studied areas are UML models and transition-based notations, both being modeling paradigms. Regarding tendencies of areas in time, we found two areas with constant activity through time, namely, test objectives and model specification. With respect to tools, we only found five studies that compared and classified model-based testing tools. These tools have been classified into common dimensions that mainly refer to the model type and phases of the model-based testing process they support. We reclassified all the tools into the hierarchy of model-based testing areas we proposed, and found that most tools were reported within the modeling paradigm area. With regard to tendencies of tools, we found that tools for testing the functional behavior of software have prevailed over time. Another finding was the shift from tools that support the generation of abstract tests to those that support the generation of executable tests. For analyzing challenges, we used six categories that emerged from the data (based on a grounded analysis): efficacy, availability, complexity, professional skills, investment, cost & effort, and evaluation & empirical evidence. We found that most challenges were related to availability. Besides, we too classified challenges according to our hierarchy of model-based testing areas, and found that most challenges fell in the model specification area. With respect to tendencies in challenges, we found they have moved from complexity of the approaches to the lack of approaches for specific software domains. 

Conclusions: Only a few systematic reviews on model-based testing could be found, therefore some areas still lack secondary studies, particularly, test execution aspects, language types, model dynamics, as well as some modeling paradigms and generation methods. We thus encourage the community to perform further systematic reviews and mapping studies, following known protocols and reporting procedures, in order to increase the quality and quantity of empirical studies in model-based testing.

Tipo de publicación: Journal Article

Publicado en: CLEI Electronic Journal

Descripción:

Software testing is an essential activity in software development projects for delivering high quality products. In a previous study, we reported the results of a survey of software engineering practices in the Costa Rican industry. To analyze more in depth the specific software testing practices among practitioners, we replicated a previous survey conducted in South America. Our objective was to characterize the state of the practice based on practitioners use and perceived importance of software testing practices. This survey evaluated 42 testing practices grouped in three categories: processes, activities and tools. A total of 92 practitioners responded to the survey. The participants indicated that: (1) task for recording of the results of tests, documentation of test procedures and cases, and re-execution of tests when the software is modified are useful and important for software testing practitioners. (2) Acceptance and system testing are the two most useful and important testing types. (3) Tools for recording defects and the effort to fix them (bug tracking) and the availability of a test database for reuse are useful and important. Regarding the use of practices, the participants stated that (4) Planning and designing of software testing before coding and evaluating the quality of test artifacts are not a regular practice. (5) There is a lack of measurement of defect density and test coverage in the industry; and (6) tools for automatic generation of test cases and for estimating testing effort are rarely used. This study gave us a first glance at the state of the practice in software testing in a thriving and very dynamic industry that currently employs most of our computer science professionals. The benefits are twofold: for academia, it provides us with a road map to revise our academic offer, and for practitioners it provides them with a first set of data to benchmark their practices. © XXII Ibero-American Conference on Software Engineering, CIbSE 2019. All rights reserved.

Tipo de publicación: Conference Paper

Publicado en: XXII Ibero-American Conference on Software Engineering, CIbSE 2019

Descripción:

Technical debt is the metaphor used to describe the effect of incomplete or immature software artifacts that bring short-term benefits to projects, but may have to be paid later with interest. Software testing cost is proven to be high due to the time (and resource)-consuming activities involved. Test automation is a strategy that can potentially reduce this cost and provide savings to the software development process. The lack or poor implementation of a test automation approach derives in test automation debt. The goal of this paper is to report our experience using a model-based testing (MBT) approach on two industrial legacy applications and assess its impact on test automation debt reduction. We selected two legacy systems exhibiting high test automation debt, then used a MBT tool to model the systems and automatically generate test cases. We finally assessed the impact of this approach on the test automation technical debt by analyzing the code coverage attained by the tests and by surveying development team perceptions. Our results show that test automation debt was reduced by adding a suite of automated tests and reaching more than 75% of code coverage. Moreover, the development team agrees in that MBT could help reduce other types of technical debt present in legacy systems, such as documentation debt and design debt. Although our results are promising, more studies are needed to validate our findings.

Tipo de publicación: Conference Paper

Publicado en: Advances in Intelligent Systems and Computing

Descripción:

Los equipos ágiles enfrentan dificultades para poder realizar pruebas de software a profundidad, dadas las iteraciones cortas de desarrollo. En muchos casos, las pruebas para servicios web se realizan manualmente, consumen mucho tiempo y requieren la experiencia de los miembros del equipo. Un enfoque de pruebas basadas en modelos, que permita la automatización de estas pruebas, podría mejorar la eficiencia del proceso y la calidad de los productos sin embargo, su adopción no debería contravenir los valores, principios y prácticas de las metodologías ágiles. En este caso de estudio discutimos el proceso realizado para incorporar pruebas basadas en modelos para automatizar las pruebas de servicios web en un equipo que implementa prácticas ágiles, y analizamos su efectividad al usar la herramienta TestOptimal sobre servicios web RESTful. Asimismo, discutimos las percepciones de los miembros del equipo, los retos y oportunidades de uso de este tipo de enfoques en equipos ágiles. Los resultados indican que las pruebas basadas en modelos permiten aumentar la cantidad de casos de prueba y defectos encontrados. Por su parte, los miembros del equipo consideran que para aumentar la aceptación de estos enfoques durante el desarrollo de un proyecto ágil, son esenciales el conocimiento para el modelado y las herramientas de apoyo. A pesar de que se logra una mejora en la generación de casos de pruebas automatizados y en la detección de errores, las pruebas basadas en modelos se perciben como un enfoque complejo de aplicar.

Tipo de publicación: Magazine Article

Publicado en: Revista Ibérica de Sistemas e Tecnologias de Informação

Descripción:

Decision support systems for agriculture allow to optimize crop processes by using the least amount of resources (land, water and fertilizers). In this study, we characterized decision support systems that use artificial intelligence (AI) techniques for precision agriculture processes. A total of 12 artificial intelligence techniques and 73 input variables were identified, with climate variables being the most used. Keywords: decision support systems; artificial intelligence; precision agriculture; greenhouses.

Tipo de publicación: Journal Article

Publicado en: Revista Ibérica de Sistemas e Tecnologias de Informação

Descripción:

The accuracy of functional size measuring is critical in software project management, because it is one of the key inputs for effort and cost estimation models. The functional size measurement (FSM) process is performed based on standardized methods; however, the accuracy of the FSM results is still based mostly on the knowledge of the measurers. In this paper, an empirical study was conducted to analyze the accuracy, reproducibility, and acceptance properties of the IFPUG FPA and COSMIC FFP functional size measurement methods. Results show that the performance of participants in measuring the requirement specifications using IFPUG FPA and COSMIC FFP did not differ significantly in terms of accuracy and reproducibility. Likewise, acceptance properties such as perceived ease of use, perceived usefulness, and intention to use did not present significant differences. Our results suggest that novice measurers could apply both methods with similar results.

Tipo de publicación: Conference Paper

Publicado en: Advances in Intelligent Systems and Computing

Descripción:

Project Based Learning (PBL) is a teaching and learning methodology where students develop projects under real conditions to acquire the knowledge and develop the skills required for their profession. In this article we discuss the lessons learned from two teachers who implemented different PBL approaches in three courses from the Bachelor in Computer and Informatics at the University of Costa Rica, during three semesters. We discuss the main considerations and decisions associated to project design elements and teaching practices. Among the main benefits identified by teachers when using PBL are the high motivation of students and the natural development of soft skills. Among the greatest challenges are (1) the design of learning assessment, considering technical aspects, soft skills, self- and peer- evaluations; (2) continuous feedback to students on their progress in the project and in team dynamics (and its consequent time investment); and (3) the necessary balance between designing a challenging and realistic project that enables the achievement of the course's learning objectives. We recommend the use of agile methodologies such as Scrum, which align very well with PBL principles, as well as the use of hybrid PBL methodologies, which include complementary didactic strategies to achieve the learning objectives of the course.

Tipo de publicación: Conference Paper

Publicado en: 2019 XLV Latin American Computing Conference (CLEI)

Descripción:

Over the past 15 years, there has been an increase in the use of information and communication technologies in the field of health (eHealth), in particular regarding mobile technologies (mHealth). However, little is known about how nutritionists have adopted such technologies in their practice in Costa Rica. The objective of this study was to identify how nutritionists use mobile technologies, as well as the perceived usefulness of different services that could be incorporated in their practice. We conducted an online survey, and the responses of 185 nutritionists were analyzed. Of the sample, 69.7% perceived that the use of mobile devices in nutrition is "very important" and "very useful" (77.3%) as a support to the nutrition care process. Although 79.5% showed their willingness to offer personalized mobile services, the use of such services in practice is scarce. According to our results, there are opportunities for the implementation of mobile technology services in areas such as the electronic health file, the self-monitoring of goal progress by the patient, as well as notifications and reminders by the nutritionist.

Tipo de publicación: Conference Paper

Publicado en: 2019 IV Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI)

Descripción:

Automating functional size measurement (FSM) for software applications that use specific development frameworks is a challenge for the industry. Although FSM automation brings benefits such as savings in time and costs, and better measure reliability, it is difficult to implement. In this paper, we present a multi-case study that evaluates the accuracy of an automated procedure for software size estimation in the context of a software development company. This procedure is implemented by a tool called FastWorks FPA, which obtains the IFPUG FPA function point estimation of software applications modeled in the company’s FastWorks framework. We describe the measurement process used by the tool, and discuss the results of the case studies. The accuracy (magnitude of relative error) of the measurements computed by the tool ranged between 3.9% and 12.9%, based on the total unadjusted function points. These results provide evidence for the feasibility of automating the counting process, as the tool’s estimated functional size reasonably approximates the result of specialists’ manual counting.

Tipo de publicación: Conference Paper

Publicado en: Product-Focused Software Process Improvement

Tipo de publicación: Conference Paper

Publicado en: 2020 15th Iberian Conference on Information Systems and Technologies (CISTI)

Tipo de publicación: Conference Paper

Publicado en: 2020 15th Iberian Conference on Information Systems and Technologies (CISTI)

Tipo de publicación: Conference Paper

Publicado en: Proceedings of the XXI Iberoamerican Conference on Software Engineering, Bogota, Colombia, April 23-27, 2018

Descripción:

Software processes improvement is the sequence of tasks and techniques that are carried out for the planning and application of the activities necessary for the effective process definition in a technology unit. Models such as the CMMI and standards such as ISO/IEC 15504 establish the quality requirements quality models that serve as guidelines for performing process improvement. However, the implementation of a process improvement initiative in a public sector institution, with limited resources, a rigid structure, and legislation to comply with, must be carried out according to the specific characteristics of the technology unit as to be accepted by the development teams. This article presents a report on the experience of applying a combination of strategies to carry out a process improvement project focused on quality management in a technology unit of a public educational. Our objective is to provide evidence and discuss the lessons learned related to the combination of strategies and models in the planning and implementation of process improvement. Our study shows how the IDEAL model and the GQM, NTCGR, COBIT, SQA, BPMN and TAM strategies were combined to define and implement a program for achieving continuous process improvement in the institution.

Tipo de publicación: Conference Paper

Publicado en: 2018 7th International Conference On Software Process Improvement (CIMPS)

Descripción:

Studies in software effort estimation (SEE) have explored the use of hyper-parameter tuning for machine learning algorithms (MLA) to improve the accuracy of effort estimates. In other contexts random search (RS) has shown similar results to grid search, while being less computationally-expensive. In this paper, we investigate to what extent the random search hyper-parameter tuning approach affects the accuracy and stability of support vector regression (SVR) in SEE. Results were compared to those obtained from ridge regression models and grid search-tuned models. A case study with four data sets extracted from the ISBSG 2018 repository shows that random search exhibits similar performance to grid search, rendering it an attractive alternative technique for hyper-parameter tuning. RS-tuned SVR achieved an increase of 0.227 standardized accuracy (SA) with respect to default hyper-parameters. In addition, random search improved prediction stability of SVR models to a minimum ratio of 0.840. The analysis showed that RS-tuned SVR attained performance equivalent to GS-tuned SVR. Future work includes extending this research to cover other hyper-parameter tuning approaches and machine learning algorithms, as well as using additional data sets.

Tipo de publicación: Conference Paper

Publicado en: Proceedings of the 16th ACM International Conference on Predictive Models and Data Analytics in Software Engineering

Descripción:

Classification and regression trees (CART) have been reported to be competitive machine learning algorithms for software effort estimation. In this work, we analyze the impact of hyper-parameter tuning on the accuracy and stability of CART using the grid search, random search, and DODGE approaches. We compared the results of CART with support vector regression (SVR) and ridge regression (RR) models. Results show that tuning improves the performance of CART models up to a maximum of 0.153 standardized accuracy and reduce its stability radio to a minimum of 0.819. Also, CART proved to be as competitive as SVR and outperformed RR.

Tipo de publicación: Book Chapter

Publicado en: Advances in Intelligent Systems and Computing

Descripción:

Many courses in the software engineering area are centered around team-based project development. Evaluating these projects is a challenge due to the difficulty of measuring individual student contributions versus team contributions. The adoption of distributed version control systems like Git enables the measurement of students' and teams' contributions to the project. In this work, we analyze the contributions within five software development projects from undergraduate courses that used project-based learning. For this, we generate visualizations of aggregated Git metrics using inequality indexes and inter-decile ratios, which offer insights into the practices and processes followed by students and teams throughout the project development. This approach allowed us to identify both inequality among students' contributions and development processes with a non-steady pace, rendering a useful feedback tool for instructors and students during the development of the project. Further studies can be conducted to assess the complexity and value of students' contributions by analyzing their source code commits and other software artifacts.

Tipo de publicación: Conference Paper

Publicado en: 2020 XLVI Latin American Computing Conference (CLEI)

Descripción:

Microservices has emerged as a architectural style that provides several benefits but also poses some challenges. One such challenge is testability, since an application may have hundreds or thousands of services operating together, and each of them needs to be tested as they evolve. To overcome this challenge, test automation is key, and together with it, the use of effective and efficient testing tools. Hence, we aim to contribute to this area by evaluating two tools that support end-to-end (E2E) testing of microservices. E2E tests allow to verify if the system works well as a whole (particularly relevant for systems made up of microservices). In this work, we first surveyed E2E testing tools reported in academic literature and by industry practitioners. Then, we applied the IEEE 14102-2010 standard to evaluate those tools. The two top-rated tools, Jaeger and Zipkin, were selected for further evaluation of their effectiveness and efficiency. Results from our case study reveal that Jaeger is more efficient and effective than Zipkinin terms of execution and failure detection times, as well as information provided to detect faults, severity and coverage.

Tipo de publicación: Book Chapter

Publicado en: Advances in Intelligent Systems and Computing

Descripción:

Software effort estimation models based on functional size allow software organizations to plan their development projects. A large number of organizations have adopted agile processes, but there is little evidence on the adoption of functional sizing methods to support software effort estimation in agile contexts. In this study, we compare four functional size estimation methods as the basis for effort estimation in the context of a startup company that develops mobile applications using an agile methodology. Measurements of software size, expressed in User Story Points (USP), Use Case Points (UCP), IFPUG Function Points (UFP), and COSMIC Function Points (CFP), were taken for a set of requirements from one project in the company. Effort estimation models were then derived from these measurements, using regression, and their accuracy was determined by the Mean Magnitude of Relative Error (MMRE) and Mean Balanced Relative Error (MBRE). We obtained the following MMRE results for each functional sizing method: 0,86 for UCP, 0,36 for USP, 0,36 for UFP and 0,22 for CFP, and the following MBRE results: 0,98 for UCP, 0,45 for USP, 0,53 for UFP and 0,35 for CFP. The effort estimation model based on COSMIC function points turned out to be the most accurate in the context of the software organization under study. Additionally, convertibility models between sizing measurements were generated to allow the organization to convert its historical measurements into any other software size measure, without having to perform the counting process of the target method.

Tipo de publicación: Conference Paper

Publicado en: CIbSE

Descripción:

Las herramientas utilizadas para automatizar las pruebas de seguridad en aplicaciones Web son esenciales para detectar vulnerabilidades y prevenir ataques cibernéticos. En este estudio identificamos herramientas reportadas entre el 2006 y el 2019 para probar la seguridad de aplicaciones Web. Cada una de las herramientas es clasificada en términos de los tipos de vulnerabilidades que prueban. Para ello, realizamos un mapeo sistemático de la literatura en el que se analizaron 63 estudios primarios, de los cuales identificamos 66 herramientas utilizadas para realizar pruebas automatizadas de seguridad. Las herramientas se clasificaron según los tipos de la metodoloǵıa de pruebas de seguridad para determinar vulnerabilidades del proyecto abierto de seguridad en aplicaciones Web (OWASP). La categoŕıa de pruebas para detectar vulnerabilidades más común fue la de Input Validation Testing (4.8) con 55 herramientas, seguido de las pruebas de Configuration and Deployment Management Testing (4.3), Session Management Testing (4.7), y Client Side Testing (4.12) con 15 herramientas utilizadas cada una. Los tipos de pruebas más reportados fueron los de la categoŕıa Input Validation Testing (4.8). En este caso SQL Injection (4.8.5) con 40 herramientas, Cross-Site Scripting (4.8.2) con 30 herramientas, y Testing for HTTP Incoming Requests (4.8.17) con 19 herramientas utilizadas.

Tipo de publicación: Conference Paper

Publicado en: CIbSE

Tipo de publicación: Journal Article

Publicado en: Revista Ibérica de Sistemas e Tecnologias de Informação (RISTI)

Tipo de publicación: Conference Paper

Publicado en: XXIII Ibero-American Conference on Software Engineering (CibSE 2021)

Descripción:

Empirical studies on software effort estimation have employed hyper-parameter tuning algorithms to improve model accuracy and stability. While these tuners can improve model performance, some might be overly complex or costly for the low dimensionality datasets used in SEE. In such cases a method like random search can potentially provide similar benefits as some of the existing tuners, with the advantage of using low amounts of resources and being simple to implement. In this study we evaluate the impact on model accuracy and stability of 12 state-of-the-art hyper-parameter tuning algorithms against random search, on 9 datasets of the PROMISE repository and 4 sub-datasets from the ISBSG R18 dataset. This study covers 2 traditional exhaustive tuners (grid and random searches), 6 bio-inspired algorithms, 2 heuristic tuners, and 3 model-based algorithms. The tuners are used to configure support vector regression, classification and regression trees, and ridge regression models. We aim to determine the techniques and datasets for which certain tuners were 1) more effective than default hyper-parameters, 2) more effective than random search, 3) which models(s) can be considered "the best" for which datasets. The results of this study show that hyper-parameter tuning was effective (increased accuracy and stability) in 862 (51%) of the 1,690 studied scenarios. The 12 state-of-the-art tuners were more effective than random search in 95 (6%) of the 1,560 studied (non-random search) scenarios. Although not effective in every dataset, the combination of flash tuning, logarithm transformation and support vector regression obtained top ranking in accuracy on the highest amount (8 out of 13) of datasets. Hyperband tuned ridge regression with logarithm transformation obtained top ranking in accuracy on the highest amount (10 out of 13) of datasets. We endorse the use of random search as a baseline for comparison for future studies that consider hyper-parameter tuning.

Tipo de publicación: Conference Paper

Publicado en: International Conference on Predictable Models and Data Analytics in Software Engineering (PROMISE 21).

Descripción:

Many software engineering courses are centered around team-based project development. Analyzing the source code contributions during the projects’ development could provide both instructors and students with constant feedback to identify common trends and behaviors that can be improved during the courses. Evaluating course projects is a challenge due to the difficulty of measuring individual student contributions versus team contributions during the development. The adoption of distributed version control sys-tems like git enable the measurement of students’ and teams’ contributions to the project.In this work, we analyze the contributions within eight software development projects,with 150 students in total, from undergraduate courses that used project-based learning.We generate visualizations of aggregated git metrics using inequality measures and the contribution per module, which offer insights into the practices and processes followed by students and teams throughout the project development. This approach allowed us to identify inequality among students’ contributions, the modules where students con-tributed, development processes with a non-steady pace, and integration practices render-ing a useful feedback tool for instructors and students during the project’s development.Further studies can be conducted to assess the quality, complexity, and ownership of the contributions by analyzing software artifacts.

Tipo de publicación: Journal Article

Publicado en: CLEI Electronic Journal

Descripción:

Data mining and machine learning techniques analyze and extract useful information from data sets in order to solve problems in different areas. For the banking sector, knowing the characteristics of customers entails a business advantage since more personalized products and services can be offered. The goal of this study is to identify and characterize data mining and machine learning techniques used for bank customer segmentation, their support tools, together with evaluation metrics and datasets. We performed a systematic literature mapping of 87 primary studies published between 2005 and 2019. We found that decision trees and linear predictors were the most used data mining and machine learning paradigms in bank customer segmentation. From the 41 studies that reported support tools, Weka and Matlab were the two most commonly cited. Regarding the evaluation metrics and datasets, accuracy was the most frequently used metric, whereas the UCI Machine Learning repository from the University of California was the most used dataset. In summary, several data mining and machine learning techniques have been applied to the problem of customer segmentation, with clear tendencies regarding the techniques, tools, metrics and datasets.

Tipo de publicación: Book Chapter

Publicado en: Advances in Intelligent Systems and Computing

Descripción:

Model-Based Testing (MBT) aims to automate test case generation and execution from abstract models representing the behavior of the system under test (SUT). MBT stages parallelization and monitoring could improve the overall process performance in complex systems and resource shortages. Agent-based software testing (ABST) consists of intelligent agents applied to complex software testing tasks. ABST approaches could enhance software testing due to multi-agent systems autonomy, independence, parallel activation, and decision-making features. This study presents an agent-based tool for MBT case generation and execution. The tool comprises two components: the MBT component based on the MBT4J tool and a JADE multi-agent component. The multi-agent component implements a coordinator agent (CA), a monitor agent (MA), and several test case generation and execution agents (TGEA). The responsibilities of agents include test case planning, generation and execution, and results synthesis. The results suggest that low levels of TGEA achieve acceptable coverage metrics in straightforward models. Productivity provides the best results in the first execution cycles.

Tipo de publicación: Conference Paper

Publicado en: 2021 IEEE V Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI)

Descripción:

Developers contribute to different technical and non-technical tasks throughout the software development life cycle, such as implementing and refactoring code, reviewing changes, documenting, testing, communicating, and collaborating. Contribution assessment research can help understand development phenomena better, motivate developers, and improve software engineering projects in industrial, open-source, and academic settings. As the body of work grows, it becomes worthwhile to aggregate evidence to standardize classifications, consolidate findings, and identify research gaps. This paper characterizes how software engineering research has studied developers’ contributions to projects. We conducted a systematic mapping study of 166 primary papers that assessed developer contributions characterizing the contribution types, research topics, research design practices, measurement constructs, assessment approaches, contexts under study, threats to validity, and challenges. Our findings show that contribution research is broad in software engineering but mainly focused on product-related contributions that mine measures from activities. Yet, research needs to consider more comprehensive construct measures and robust approaches to recognize the multi-faceted, nuanced, and complex nature of development work, without overlooking contributions. At the same time, there is a need for approach applicability to help future research and technology transfer. Researchers, tool designers, and adopters can leverage our results to motivate future software contribution-related work.

Tipo de publicación: Journal Article

Descripción:

El cáncer ha emergido como un problema de salud pública. Uno de los tipos más comunes de cáncer en mujeres es el de mama. Los tratamientos modernos de cáncer han mejorado dramáticamente las tasas de supervivencia y han cambiado la naturaleza del cuidado del cáncer. La telerehabilitación es una herramienta innovadora que ha demostrado mejoras en la eficiencia de los servicios de salud. Dadas las limitaciones de acceso a los servicios de rehabilitación, la telerehabilitación presenta oportunidades para ampliar la cobertura de estos servicios. La caracterización de las plataformas de servicios de telerehabilitación para el cáncer de mama permite identificar el alcance de los servicios de estas plataformas en la atención de pacientes y los posibles retos asociados a su uso. En este trabajo se realiza un mapeo de literatura que caracteriza las plataformas de servicios de telerehabilitación para el cáncer de mama. El artículo presenta la caracterización de las plataformas reportadas en 22 estudios identificados entre el 2014 y el 2021, identificando las funcionalidades que ofrecen, cómo han sido implementadas y evaluadas, y los retos reportados como mapa de ruta para futuras investigaciones.

Tipo de publicación: Conference Paper

Publicado en: 2023 18th Iberian Conference on Information Systems and Technologies (CISTI)

Tipo de publicación: Conference Paper

Publicado en: VI Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI)

Tipo de publicación: Conference Paper

Publicado en: XLIX Latin American Computing Conference (CLEI 2023)