GRAZIELA SIMONE TONIN

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2021 - 20233
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    Active learning approaches applied in teaching agile methodologies
    (2023) GRAZIELA SIMONE TONIN; FABIO ROBERTO DE MIRANDA; ANDREW TOSHIAKI NAKAYAMA KURAUCHI; Montagner, Igor; Agena, Barbara; Barth, Fabrício J.
    We need to modernize education to form adaptable leaders who can tackle evolving challenges in our dynamic world. Insper's computer science program is designed to reflect this need with an innovative infrastructure, curriculum, and industry partnerships. We use active learning methodologies to teach agile methodologies and develop soft skills to solve real-world problems. Our focus is on non-violent communication, feedback techniques, and teamwork, along with constant interaction with industry professionals who share their experiences with students. Our goal is to provide students with a well-rounded education that equips them for success in the digital age. This work-in-progress research project describes our approach to teaching and our objective to prepare students for the future in the context of an innovative first semester experience on a CS program.
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    Shedding Light on How Intelligent Techniques can Support Technical Debt Management and Influence Software Quality Attributes
    (2022) Albuquerque, Danyllo; GRAZIELA SIMONE TONIN; Chagas, Ferdinandy; Perkusich, Mirko; Guimaraes, Everton; Hyggo Almeida; Perkusich, Angelo
    Technical Debt (TD) is a consequence of decision-making in the development process that can negatively impact Software Quality Attributes (SQA) in the long term. Technical Debt Management (TDM) is a complex task to minimize TD that relies on a decision process based on multiple and heterogeneous data that are not straightforward to synthesize. Recent studies show that Intelligent Techniques can be a promising opportunity to support TDM activities since they explore data for knowledge discovery, reasoning, learning, or supporting decision-making. Although these techniques can improve TDM activities, there is a need to identify and analyze solutions based on Intelligent Techniques to support TDM activities and their impact on SQA. For doing so, a Systematic Mapping Study was performed, covering publications between 2010 and 2020. From 2276 extracted studies, we selected 111 unique studies. We found a positive trend in applying Intelligent Techniques to support TDM activities being Machine Learning and Reasoning Under Uncertainty the most recurrent ones. Design and Code were the most frequently investigated TD types. TDM activities supported by intelligent techniques impact different characteristics of SQA, mainly Maintainability, Reliability, and Security. Although the research area is up-and-coming, it is still in its infancy, and this study provides a baseline for future research.