Abstract
Assessment is an important part of the educational process, playing a crucial role in student learning. The increase in the number of students in higher education has placed extreme pressure on assessment practices, often leading to a teacher having hundreds of assignments to correct, not only giving feedback too late, but also low quality feedback, as it is humanly impossible to correct all these assessments by giving quality feedback in such a short time. Due to the social confinement caused by the pandemic of COVID-19, there was the need to change the evaluation method initially associated with a thin exam, to a continuous evaluation method based on multiple weekly assignments. In order to deal with this situation, we developed AOCO, the first automatic correction tool for the ARMv8 AArch64 assembly language. This work presents the AOCO tool, as well as the results of the evaluation of a first use with students.

Abstract
The 2020/2021 academic year started full of uncertainties for new students of higher education in Portugal. The restrictions imposed by the COVID-19 pandemic, the fears of a new lockdown, all coupled with the well-known challenges that a university student faces in his first year, made this year a particularly challenging year in terms of the students' integration. In this paper, we present how the mentoring programme of the Integrated Master in Informatics and Computing Engineering at the Faculty of Engineering of the University of Porto was adapted to help the integration of first-year students in the university environment under the pandemic.

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Large language model (LLM)-powered assistants are increasingly used for generating program code and unit tests, but their application in acceptance testing remains underexplored. To help address this gap, this paper explores the use of LLMs for generating executable acceptance tests for web applications through a two-step process: (i) generating acceptance test scenarios in natural language (in Gherkin) from user stories, and (ii) converting these scenarios into executable test scripts (in Cypress), knowing the HTML code of the pages under test. This two-step approach supports acceptance test-driven development, enhances tester control, and improves test quality. The two steps were implemented in the AutoUAT and Test Flow tools, respectively, powered by GPT-4 Turbo, and integrated into a partner company's workflow and evaluated on real-world projects. The users found the acceptance test scenarios generated by AutoUAT helpful 95% of the time, even revealing previously overlooked cases. Regarding Test Flow, 92% of the acceptance test cases generated by Test Flow were considered helpful: 60% were usable as generated, 8% required minor fixes, and 24% needed to be regenerated with additional inputs; the remaining 8% were discarded due to major issues. These results suggest that LLMs can, in fact, help improve the acceptance test process, with appropriate tooling and supervision.

Abstract
As sports analytics evolve to include a broad spectrum of data from diverse sources, the challenge of integrating heterogeneous data becomes pronounced. Current methods struggle with flexibility and rapid adaptation to new data formats, risking data integrity and accuracy. This paper introduces PlayField, a framework designed to robustly handle diverse sports data through adaptable configuration and an automated API. PlayField ensures precise data integration and supports manual interventions for data integrity, making it essential for accurate and comprehensive sports analysis. A case study with ZeroZero demonstrates the framework's capability to improve data integration efficiency significantly, showcasing its potential for advanced analytics in sports.

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