Citas

Woods, L. T., & Rana, Z. A. (2023). Modelling Sign Language with Encoder-Only Transformers and Human Pose Estimation Keypoint Data. Mathematics, 11(9), 2129. https://doi.org/10.3390/math11092129.

Slama, F., Ismail, I., & Latrach, L. (2023). Exploring the Integration of Machine Learning Models in Programming Languages on GitHub: Impact on Compatibility to Address Them. https://doi.org/10.21203/rs.3.rs-2591510/v1.

Fernández-Gauna, B., Rojo, N., & Graña, M. (2023). Automatic Feedback and Assessment of Team-Coding Assignments in a DevOps Context. International Journal of Educational Technology in Higher Education, 20, 17.

Vargovich, J., Santos, F., Penney, J., Gerosa, M. A., & Steinmacher, I. (2023). GiveMeLabeledIssues: An Open Source Issue Recommendation System. arXiv, 2303, 13418.

Delima, R., Mustofa, K., & Sari, A. K. (2023). Automatic Requirements Engineering: Activities, Methods, Tools, and Domains: A Systematic Literature Review. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(3), 564-578. https://doi.org/10.29207/resti.v7i3.4924.

Виктор, М., ОЧАРАН-ЭРНАНДЕС, Х. О., ЛИМОН, К., & ПЕРЕС-АРРИАГА, Х. К. (2023). Развертывание микросервисов. Труды Института системного программирования РАН, 35(1), 57-72. https://doi.org/10.15514/ISPRAS-2023-35(1)-4.

Niño-Martínez, V. M., Ocharán-Hernández, J. O., Limón-Riaño, H. X., & Pérez-Arriaga, J. C. (2023). Microservice Deployment. Труды Института системного программирования РАН, 35(1), 57-72. https://doi.org/10.15514/ISPRAS-2023-35(1)-4.

Cañedo-Martínez, L. (2023). Análisis de seguimiento de requerimientos funcionales basado en ontologías para descubrir afectaciones por cambios en un producto de software. Instituto Politécnico Nacional.

Hernández-Molinos, M. J., Sánchez-García, Á. J., Barrientos-Martínez, R. E., Pérez-Arriaga, J. C., & Ocharán-Hernández, J. O. (2023). Software Defect Predicion with Bayesian Approaches. Mathematics, 11(11), 2524. https://doi.org/10.3390/math11112524.

Ahmad-Teridi, N., Kamarul-Adhzar, Z. A. A., Rahim, N. M., Kamis, J., Ridzuan, T., Adnan, Z., & Abdul-Rauf, M. F. (2023). The Approach Using Cumulative Voting and Spanning Tree Technique in Implementing Functional Requirement Priorization: A Case Study of Student’s Financial System Development. Journal of Theoretical and Applied Information Technology, 101(3), 1106-1117.

Bugayenko, Y., Bakare, A., Cheverda, A., Farina, M., Kruglov, A., Plaksin, Y., Predycz, W., & Succi, G. (2023). Prioritizing Tasks in Software Development: A Systematic Literature Review. Plos one, 18(4), e0293838. https://doi.org/10.1371/journal.pone.0283838.

Iftikhar, U., Ali, N. B., Börstler, J., & Usmad, M. (2023). A Catalog of Source Code Metrics: A Tertiary Study. In 2023 International Conference on Software Quality (SWQD): Software Quality: Higher Software Quality through Zero Waste Development (pp. 87-106). Springer International Publishing. https://doi.org/10.1007/978-3-031-31488-9_5.

Abbad-Andaloussi, A. (2023). On the Relationship Between Source-Code Metrics and Cognitive Load: A Systematic Tertiary Review. Journal of Systems and Software, 198, 111619. https://doi.org/10.1016/j.jss.2023.111619.

González-Aparicio, M. T., Younas, M., Tuya, J., & Casado, R. (2023). A Transaction Platform for Microservices-Based Big Data Systems. Simulation Modelling Practice and Theory, 123, 102709. https://doi.org/10.1016/j.simpat.2022.102709.

Alulema, D., Criado, J., Iribarne, L., Fernández-García, A. J., & Ayala, R. (2023). SI4IoT: A Methodology Based on Models and Services for the Integration of IoT Systems. Future Generation Computer Systems, 143, 132-151. https://doi.org/10.1016/j.future.2023.01.023.

Santander, L., & Abisai, F. (2023). Visión robótica de baja resolución con recursos limitados. Thesis. Centro Nacional de Investigación y Desarrollo Tecnológico. Instituto Tecnológico Nacional de México.

Almeyda, S., & Dávila, A. (2022). Process Improvement in Software Requirements Engineering: A Systematic Mapping Study. Programming and Computer Software, 48, 513-533. https://doi.org/10.3390/math11092129.

Mammadov, A. (2022). Building a Prototype of Web API Honeypot for Electric Vehicle Charging Network Operators. Thesis. Norwegian University of Science and Technology (NTNU).

ОРТЕГА-ХИХОН, Й. Н., & МЕЗУРА-ГОДОЙ, К. (2022). Оценка пригодности к использованию нейрокомпьютерных интерфейсов: анализ состояния дел. Труды Института системного программирования РАН, 34(3), 145-158. https://doi.org/10.15514/ISPRAS-2022-34(3)-10.

Ortega-Gijón, Y. N., & Mezura-Godoy, C. (2022). Usability Evaluation of Brain Computer Interfaces: Analysis of State of Art. Труды Института системного программирования РАН, 34(3), 145-158. https://doi.org/10.15514/ISPRAS-2022-34(3)-10.

Niño-Martínez, V. M., Ocharán-Hernández, J. O., Limón-Riaño, H. X., & Pérez-Arriaga, J.C. (2022). A Microservice Deployment Guide. Programming and Computer Software, 48, 632-645. https://doi.org/10.1134/S0361768822080151.

Ortega-Gijón, Y. N., & Mezura-Godoy, C. (2022). Usability Evaluation of BCI Software Applications: A Systematic Review of the Literature. Programming and Computer Software, 48, 646-657. https://doi.org/10.1134/S0361768822080163.

Arrouch, I., Ahmad, N. S., Goh, P., & Mohamad-Saleh, J. (2022). Close Proximity Time-to-collision Prediction for Autonomous Robot Navigation: An Exponential GPR Approach. Alexandria Engineering Journal, 61(2), 11171-11183. https://doi.org/10.1016/j.aej.2022.04.041.

Ferreira-Santos, B. X. (2022). Explorando princípios da general data protection regulation, Lei Geral de Proteção de Dados e diretrizes éticas da inteligência artificial em repositórios open source. Monografía. Instituto de Ciências Exatas. Universidade de Brasília.

Li, B., & Nong, X. (2022). Automatically Classifying Non-Functional Requirements Using Deep Neural Network. Pattern Recognition, 132, 108948. https://doi.org/10.1016/j.patcog.2022.108948.

Raymond, R., & Savarimuthu, M. A. (2022). Identification of Data-Intensive Systems Requirements Using Semantic Similarity Search. SSRN, 4033078.

Cheligeer, C. (2022). An Ebd-Enabled Design Knowledge Acquisition Framework. Thesis. Concordia University.

Lymaylla-Lunarejo, M. I., Condori-Fernández, N., & Luaces, M. R. (2022). Towards An Automatic Requirements Classification in a New Spanish Dataset. In 2022 IEEE 30th International Requirements Engineering Conference (RE) (pp. 270-271). IEEE. https://doi.org/10.1109/RE54965.2022.00039.

Cheeliger, C., Huang, J., Wu, G., Bhuiyan, N., Xu, Y., & Zeng, Y. (2022). Machine Learning in Requirements Elicitation: A Literature Review. AI EDAM, 36, E32. https://doi.org/10.1017/S0890060422000166.

Rutagemwa, H., & Patenaude, F. (2022). Automated Data-Driven System for Compliance Monitoring. In Broadband Communications, Computing, and Control for Ubiquitous Intelligence (pp. 291-312). Springer International Publishing.

Cumbal, R., Herrera, J., Sandoval, J., Hincapié, R., & Arévalo, G. (2022). Dynamic Coverage Infraestructure of Vehicles in VANET with Scalable Enviroments on Demand. In 2022 International Conference on Algorithms, Data Mining, and Information Technology (ADMIT) (pp. 68-73). IEEE. https://doi.org/10.1109/ADMIT57209.2022.00020.

한상곤, 구은희, & 최정인. (2022). V2X 환경에서 Event-Sourcing 과 CQRS 패턴을 사용한 BSM 프레임워크..한국컴퓨터정보학회논문지, 27(8), 169-176.

Töyrylä, M. (2022). Datan Eheyden Hallinta Virhetilanteissa Mikropalveluarkkitehtuurissa. Thesis. Faculty of Engineering and Natural Sciences.

Mohan-Koyva, K., & Muthukumar, B. (2022). A Survey of Saga Frameworks for Distributed Transactions in Event-driven Microservices. In 2022 Third International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE) (pp. 1-6). IEEE. https://doi.org/10.1109/ICSTCEE56972.2022.10099533.

Dürr, K., Lichtenthäler, R., & Wirtz, G. (2022). Saga Pattern Technologies: A Criteria-based Evaluation. In CLOSER (pp. 141-148).

Candela-Uribe, C. Á. (2022). Modelos Transaccionales Avanzados como Alternativa para la Implementación de Transacciones de Larga Duración en Microservicios. In Encuentro Internacional de Educación en Ingeniería. https://doi.org/10.26507/paper.2565.

Söylemez, M., Tekinerdogan, B., & Kolukısa Tarhan, A. (2022). Feature-Driven Characterization of Microservice Architectures: A Survey of the State of the Practice. Applied Sciences, 12(9), 4424. https://doi.org/10.3390/app12094424.

Daraghmi, E., Zhang, C. P., & Yuan, S. M. (2022). Enhancing Saga Pattern for Distributed Transactions Within a Microservices Architecture. Applied Sciences, 12(12), 6242. https://doi.org/10.3390/app12126242.

Ghansah, B., Benuwa, B. B., Essel, D. D., Sarkodie, A. P., & Agbeko, M. (2022). A Review of Non-Linear Kalman Filtering for Target Tracking. International Journal of Data Analytics (IJDA), 3(1), 1-25. https://doi.org/10.4018/IJDA.294864.

Pérez-Verdejo, J. M., Sánchez-García, Á. J., Ocharán-Hernández, J. O., Mezura-Montes, E., & Cortés-Verdín, M. K. (2021). Requirements and GitHub Issues: An Automated Approach for Quality Requirements Classification. Programming and Computer Software, 47, 704-721. https://doi.org/10.1134/S0361768821080193.

Robles-Aguilar, A., Ocharán-Hernández, J. O., Sánchez-García, Á. J., & Limón-Riaño, H. X. (2021). Software Design and Artificial Intelligence: A Systematic Mapping Study. In 2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT) (pp. 132-141). IEEE. https://doi.org/10.1109/CONISOFT52520.2021.00028.

Lopéz-Hernández, D. A. (2021). Clasificación de Requisitos de Software Mediante un Enfoque Neuroevolutivo. Thesis. Facultad de Estadística e Informática. Universidad Veracruzana.

Binkhonain, M. K. (2021). Using Machine Learning Algorithms for Classifying Non-Functional Requeriments: Research and Evaluation. Thesis. Faculty of Science and Engineering. Univesity of Manchester.

López-Hernández, D. A., Ocharán-Hernández, J. O., Mezura-Montes, E., & Sánchez-García, Á. J. (2021). Automatic Classification of Software Requirements using Artificial Neural Networks: A Systematic Literature Review. In 2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT) (pp. 152-160). IEEE. https://doi.org/10.1109/CONISOFT52520.2021.00030.

Shahzad, U. (2021). Application of Supervised Machine Learning for Prediction of Probabilistic Transient Stability. Australian Journal of Electrical and Electronics Engineering, 19(1), 65-78. https://doi.org/10.1080/1448837X.2021.2013418.

Shehadeh, K., Arman, N., & Khamayseh, F. (2021). Semi-Automated Classification of Arabic User Requirements into Functional and Non-Functional Requirements using NLP Tools. In 2021 International Conference on Information Technology (ICIT) (pp. 527-532. IEEE. https://doi.org/10.1109/ICIT52682.2021.9491698.

Somohano-Murrieta, J. C. B., Ocharán-Hernández, J. O., Sánchez-García, Á. J., Limón-Riaño, H. X, & Árenas-Valdés, M. Á. (2021). Improving the Analytic Hierarchy Process for Requirements Prioritization Using Evolutionary Computing. Programming and Computer Software, 47, 746-756. https://doi.org/10.1134/S0361768821080235.

Hossein-Nejat, M., Motameni, H., Vahdat-Nejad, H., & Barzegar, B. (2021). Efficient Cloud Service Ranking Based on Uncertain User Requirements. Cluster Computing, 25, 485-502. https://doi.org/10.1007/s10586-021-03418-w.

Yahia, M. H., Amro, M. I., & Alshayeb, M. R. (2021). An Empirical Study of Evaluating the Correlation between Class Stability and Bad Smells. In 2021 22nd International Arab Conference on Information Technology (ACIT) (pp. 1-5). IEEE. https://doi.org/10.1109/ACIT53391.2021.9677443.

МАЛЮГА, К., & ПЕРЛ, И. (2021). ИНФОКОММУНИКАЦИОННЫЕ ТЕХНОЛОГИИ. ИНФОКОММУНИКАЦИОННЫЕ ТЕХНОЛОГИИ Учредители: Поволжский государственный университет телекоммуникаций и информатики, 19(4), 425-435. https://doi.org/10.18469/ikt.2021.19.4.06.

Christian, A., & Björn, F. (2021). Message Brokers in a Microservice Architecture. Thesis. School of Electrical Engineering and Computer Science. Institute of Technology.

Maliuga, K. V., & Perl, I. A. (2021). Aspects of Microservices Communication When Using Saga Template. Infokommunikacionnye tehnologii, 19(4), 425-435. https://doi.org/10.18469/ikt.2021.19.4.06.

Sokolova, M., Mamedova, N., Starerova, O., & Urintsov, A. (2021). Designing a Software Solution to Increase the Conversion Rate of Transactions Through a Payment Platform. In CEUR Workshop Proceedings (pp. 81-90).

Dürr, K., Lichtenthäler, R., & Wirtz, G. (2021). An Evaluation of Saga Pattern Implementation Technologies. In ZEUS (pp. 74-82).

Wang, S., Zhang, Z., Cao, Y., Liu, X., Zhang, K., & Chen, J. (2021). An Obstacle Avoidance Method for Indoor Flaw Detection Unmanned Robot Based on Transfer Neural Network. In Earth and Space (pp. 484-493).

Sánchez-García, Á. J., Ríos-Figueroa, H. V., Sánchez-García, J. Á., & Pérez-Arriaga, J. C. (2020). Towards Obstacle Identification by Markovian-Evolutionary Segmentation. In 2020 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE) (pp. 9-12). IEEE. https://doi.org/10.1109/ICMEAE51770.2020.00009.

Wu, X., & Zhang, M. (2020). An Empirical Assessment of the Predictive Quality of Internal Product Metrics to Predict Software Maintainability in Practice. Thesis. Faculty of Computing, Blekinge. Institute of Technology.

한상곤, 최정인, & 우균. (2020). 이벤트 소싱과 CQRS 패턴을 활용한 데이터 재현 및 분산처리 사례 및 연구 동향. 정보과학회논문지, 47(12), 1101-1110.

Munonye, K., & Martinek, P. (2020). Enhancing Performance of Distributed Transactions in Microservices via Buffered Serialization. Journal of Web Engineering, 19(5-6), 647-684, https://doi.org/10.13052/jwe1540-9589.19564.

Herpich-Muller, R., Meinhardt, C., & Machado-Medizabal, O. (2020). Microsserviços Aplicados No Gerenciamento de Dados de Vistorias Imobiliárias: Um Estudo de Caso. In Anais do XVIII Workshop em Clouds e Aplicações (pp. 41-54). SBC. https://doi.org/10.5753/wcga.2020.12443.

Malygua, K., Perl, O., Slapoguzov, A., & Perl. I (2020). Fault Tolerant Central Saga Orchestrator in RESTful Architecture. In 2020 26th Conference of Open Innovations Association (FRUCT) (pp. 278-283). IEEE. https://doi.org/10.23919/FRUCT48808.2020.9087389.

Alulema, D., Criado, J., Iribarne, L., Fernández-García, A. J., & Ayala, R. (2020). A Model-Driven Engineering Approach for the Service Integration of IoT Systems. Cluster Computing, 23, 1937-1954. https://doi.org/10.1007/s10586-020-03150-x.

Amrouche, M., Marihno, T., & Stipanović, D. (2020). Vision Based Collision Avoidance For Multi-Agent Systems Using Avoidance Functions. In 2020 European Control Conference (ECC) (pp. 1683-1688). https://doi.org/10.23919/ECC51009.2020.9143739.

Sánchez-García, Á. J., Ríos-Figueroa, H. V., Limón-Riaño, H. X., Sánchez-García, J. Á., & Cortés-Verdín, M. K. (2019). Towards Learning Obstacles to Avoid Collisions in Autonomous Robot Navigation. In 2019 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE) (pp. 24-27). IEEE. https://doi.org/10.1109/ICMEAE.2019.00012.

Hansun, S., Charles, V., & Indrati, C. R. (2019). Revisiting the Holt-Winters’ Additive Method for Better Forecasting. International Journal of Enterprise Information Systems (IJEIS), 15(2), 43-57.

Kikuchi, S., & Bhalla, S. (2019). Optimizing a Long-Lived Transaction with Verification Function. Journal of Software Engineering and Applications, 12(9), 339-364. https://doi.org/10.4236/jsea.2019.129021.

Marinho, T. (2019). Bio-Inspired Vision-Based Evasion Control: Collision Avoidance Without Distance Measurement. Thesis. University of Illinois at Urbana-Champaign.

Kenneth-Topham, L. (2019). Biologically Inspired Guidance for Autonomous Systems. Thesis. University of Liverpool.

Shi, C., Dong, Z., Pundlik, S., & Luo, G. (2019). A Hardware-Friendly Optical Flow-Based Time-to-Collision Estimation Algorithm. Sensors, 19(4), 807. https://doi.org/10.3390/s19040807.

Udita, P. (2018). Efficient Access Network Selection and Data Demand Prediction for 5G Systems. Thesis. University of Cape Town.

Sánchez-García, Á. J., Ríos-Figueroa, H. V., Quintana-Caraía, G., & Marín-Hernández, A. (2018). Predicting Collisions in Mobile Robot Navigation by Kalman Filter. Kalman Filters: Theory for Advanced Applications, 151.

Sánchez-García, Á. J., Ríos-Figueroa, H. V., Garnier, H., Quintana-Carapia, G., Rechy-Ramírez, E. J., & Marín-Hernandez, A. (2018). Predicting Collisions: Time-to-Contact Forecasting Based on Probabilistic Segmentation and System Identification. Advanced Robotics, 32(8), 426-442. https://doi.org/10.1080/01691864.2018.1455604.

Converse, L. (2018). Perceptual Aliasing in Vision Based Robot Navigation. Thesis. College of Engineering. Boston University. University of Rhode Island.

Simoes-Teixeira, M. A., Dalmedico, N., Barbosa-Santos, H., Schneider-Olveira, A., Ramos-Arruda, L. V., & Neves, F. (2017). Enhancing Robot Capabilities of Environmental Perception through Embedded GPU. In 2017 VII Brazilian Symposium on Computing Systems Engineering (SBESC) (pp. 217-224). IEEE. https://doi.org/10.1109/SBESC.2017.37.

Sánchez-García, Á. J., Ríos-Figueroa, H. V., Quintana-Carapia, G., Montano-Rivas, A., & Marín-Hernández, A. (2017). Time-to-Contact Forecasting by Modeling the Apparent Size of Obstacles. In 2017 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE) (pp. 3-7). IEEE. https://doi.org/10.1109/ICMEAE.2017.12.

Sánchez-García, Á. J., Ríos-Figueroa, H. V., Marín-Hernández, A., Rechy-Ramírez, E., & Oliva-Uribe, D. (2017). Finding Learned Obstacles to Avoid Collisions in Autonomous Robotic Navigation. In 2017 International Conference on Image and Vision Computing New Zealand (IVCNZ) (pp. 1-5). IEEE. https://doi.org/10.1109/IVCNZ.2017.8402489.

于乃功, 郑宇凌, 徐丽, & 蔡建羡. (2017). 基于光流的非结构化环境中移动机器人避障方法. 北京工业大学学报, 43(1), 65-69.

Jin, Z., Yan, B., & Ye, R. (2017). The Obstacle Avoidance for UAV Based on Improved Frames Difference and Optical Flow. In 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI) (pp. 318-323). Atlantis Press. https://doi.org/10.2991/caai-17.2017.72.

Naingon, Y., Yuling, Z., & Li, X., & Jianxian, C. (2017). Optical Flow Based Mobile Robot Obstacle Avoidance Method in Unstructured Enviroment. Rhhz Test, 43(1), 65-69. https://doi.org/10.11936/bjutxb2016050002.

Matus-Perdomo, D., Sánchez-García, Á. J., Pérez-Arriaga, J. C., & Ríos-Figueroa, H. V. (2016). Calculation of Optical Flow Using Color Models to Improve the Accuracy of the Identification of Objects in Motion. In 2016 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE) (pp. 9-13). IEEE. https://doi.org/10.1109/ICMEAE.2016.011.

Sánchez-García, Á. J., Ríos-Figueroa, H. V., Hoyos-Rivera, G. J., & Marín-Hernández, A. (2016). Estimation of Time-to-Contact for Navigation of Autonomous Robots Using Parallel Processing. In 2016 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE) (pp. 26-31). IEEE. https://doi.org/10.1109/ICMEAE.2016.014.

Sánchez-García, Á. J., Ríos-Figueroa, H. V., Marín-Hernández, A., Cortés-Verdín, M. K., & Contreras-Vega, G. (2016). Estimation of Time-to-Contact from Tau-Margin and Statistical Analysis of Behavior. In 2016 International Conference on Systems, Signals and Image Processing (IWSSIP) (pp. 1-6). IEEE. https://doi.org/10.1109/IWSSIP.2016.7502702.

Sánchez-García, Á. J., Velasco-Vásquez, M. L., Ríos-Figueroa, H. V., Marín-Hernández, A., & Contreras-Vega, G. (2014). Comparison and Analysis of Models to Predict the Motion of Segmented Regions by Optical Flow. In 2014 Mexican International Conference of Artificial Intelligence (MICA): Human-Inspired Computing and Its Applications (pp. 293-303). Springer International Publishing. https://doi.org/10.1007/978-3-319-13647-9_27.

Sánchez-García, Á. J., Ríos-Figueroa, H. V., Marín-Hernández, A., & Acosta-Mesa, H. G. (2014). Tracking and Prediction of Motion of Segmented Regions Using the Kalman Filter. In 2014 International Conference on Electronics, Communications and Computers (CONIELECOMP) (pp. 88-93). IEEE. https://doi.org/10.1109/CONIELECOMP.2014.6808573.

Sánchez-García, Á. J., Ríos-Figueroa, H. V., Velasco-Vásquez, M. L., Contreras-Vega, G., & Marín-Hernández, A. (2014). Motion Prediction of Regions Through the Statistical Temporal Analysis Using an AutoRegressive Moving Average (ARMA) Model. Research in Computing Science, 77, 9-20.

贺成龙, 秦洪, & 于永生. (2014). 一种空中目标航迹的自适应跟踪算法. 深圳大学学报: 理工版, 31(4), 361-366.

Sánchez-García, Á. J. (2013). Segmentación, descripción y modelado de regiones en movimiento por flujo óptico. Thesis. Facultad de Física e Inteligencia Artificial.