Automatic feedback and assessment of team-coding assignments in a DevOps context

We describe an automated assessment process for team-coding assignments based on DevOps best practices. This system and methodology includes the definition of Team Performance Metrics measuring properties of the software developed by each team, and their correct use of DevOps techniques. It tracks the progress on each of metric by each group. The methodology also defines Individual Performance Metrics to measure the impact of individual student contributions to increase in Team Performance Metrics. Periodically scheduled reports using these metrics provide students valuable feedback. This process also facilitates the process of assessing the assignments. Although this method is not intended to produce the final grade of each student, it provides very valuable information to the lecturers. We have used it as the main source of information for student and team assessment in one programming course. Additionally, we use other assessment methods to calculate the final grade: written conceptual tests to check their understanding of the development processes, and cross-evaluations. Qualitative evaluation of the students filling relevant questionnaires are very positive and encouraging.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature

Web-Scraping Teknikan Oinarritutako Azpiegitura Informatikoak. Xerka Online eta Minerva aplikazioak

Erabiltzailearen zereginak errazten dituzten sistema informatiko ugari erabiltzen dira, bai arlo profesionalean, bai eta arlo pertsonalean ere. Hala ere, kasu batzuetan erabiltzaileen beharren eta sistema informatikoak eskaintzen duenaren arteko distantzia handia da. Artikulu honetan web-scraping teknikarekin sortutako bi azpiegitura informatiko deskribatu dira, jatorrizko beste azpiegitura batzuen funtzionalitatea hobetu dutenak. Alde batetik, Xerka Online aplikazioak ikertzaileen curriculum vitaearen (CVaren) sortze- eta mantentze-lana errazten du, ikertzaileek egin behar izaten duten ataza nagusia modu automatizatuan eginez: argitalpenak bilatu eta haiei dagozkien kalitate-adierazle (eragin-faktore eta aipamen kopuru) eguneratuak ezarri. Minerva aplikazioak, ordea, Vitoria-Gasteizko Ingeniaritza Eskolan egiten diren kalitate-txostenak kudeatzen ditu. Horretarako, Euskal Herriko Unibertsitateko (UPV/EHUko) GAUR web-aplikaziotik automatikoki jaisten ditu itxitako aktak, jarritako kalifikazioen estatistikak kalkulatzen ditu, eta maila ezberdinetan egiten diren txostenak batzen ditu. Bi aplikazioen abantaila nagusiak lan horiek egiteko behar den denboraren eta giza akatsen murrizpena dira.; Computational systems that facilitate the tasks of the customer are frequently used, both for professional and personal purposes. However, in some cases, the computer system does not meet the users’ needs. In this article, two computational infrastructures based on the use of web-scraping are described, which improve the functionality of the original infrastructures.Xerka Online allows the creation and maintenance of a researcher’s curriculum vitae (CV) by searching his/her publications and their updated quality indicators (impact factor and cites). Minerva manages the quality assessment reports in the Faculty of Engineering of Vitoria-Gasteiz. To that end, it downloads the grade records from GAUR (a web-application of the University of the Basque Country UPV/EHU), calculates statistics, and merges reports generated in different levels of the quality assessment process. The main advantages of these applications are time reduction and avoidance of human errors

Actor-critic continuous state reinforcement learning for wind-turbine control robust optimization

[EN] The control of Variable-Speed Wind-Turbines (VSWT) extracting electrical power from the wind kinetic energy are composed of subsystems that need to be controlled jointly, namely the blade pitch and the generator torque controllers. Previous state of the art approaches decompose the joint control problem into independent control subproblems, each with its own control subgoal, carrying out separately the design and tuning of a parameterized controller for each subproblem. Such approaches neglect interactions among subsystems which can introduce significant effects. This paper applies Actor-Critic Reinforcement Learning (ACRL) for the joint control problem as a whole, carrying out the simultaneous control parameter optimization of both subsystems without neglecting their interactions, aiming for a globally optimal control of the whole system. The innovative control architecture uses an augmented input space so that the parameters can be fine-tuned for each working condition. Validation results conducted on simulation experiments using the state-of-the-art OpenFAST simulator show a significant efficiency improvement relative to the best state of the art controllers used as benchmarks, up to a 22% improvement in the average power error performance after ACRL training.This work has been partially supported by FEDER funds through MINECO project TIN2017-85827-P, MCIN project PID2020-116346 GB-I00, and project KK-202000044 of the Elkartek 2020 funding program of the Basque Government