Technology plays a crucial role in people's lives. However, softwareengineering discriminates against individuals from underrepresented groups in several ways, either through algorithms that produce biased outcom...
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ISBN:
(纸本)9798350337341
Technology plays a crucial role in people's lives. However, softwareengineering discriminates against individuals from underrepresented groups in several ways, either through algorithms that produce biased outcomes or for the lack of diversity and inclusion in software development environments and academic courses focused on technology. this reality contradicts the history of softwareengineering, which is filled with outstanding scientists from underrepresented groups who changed the world withtheir contributions to the field. Ada Lovelace, Alan Turing, and Clarence Ellis are only some individuals who made significant breakthroughs in the area and belonged to the population that is so underrepresented in undergraduate courses and the software industry. Previous research discusses that women, LGBTOIA+ people, and non-white individuals are examples of students who often feel unwelcome and ostracized in softwareengineering. However, do they know about the remarkable scientists that came before them and that share background similarities withthem? Can we use these scientists as role models to motivate these students to continue pursuing a career in softwareengineering? In this study, we present the preliminary results of a survey with 128 undergraduate students about this topic. Our findings demonstrate that students' knowledge of computer scientists from underrepresented groups is limited. this creates opportunities for investigations on fostering diversity in softwareengineering courses using strategies exploring computer science's history.
Over the last decade, we have witnessed a flourishing activity in the application of deep learning techniques to solve softwareengineering problems that were poorly addressed in the past, or not addressed at all. In ...
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ISBN:
(纸本)9798400717017
Over the last decade, we have witnessed a flourishing activity in the application of deep learning techniques to solve softwareengineering problems that were poorly addressed in the past, or not addressed at all. In this context, researchers put effort into creating specialized representations and models, hence giving a tangible, conceptual contribution beyond the simple application. Withthe advent of Large Language Models, such contributions were surpassed, and this was possible because big techs had the availability of data and infrastructure. As such models are pretty good at solving many softwareengineering problems, where would research in softwareengineering, and, specifically, in recommender systems go? Will artificial intelligence research kill it? Fortunately, we should not forget that softwareengineering is about people, and this is where I believe there will be a lot of room for novel research. softwareengineering researchers have the knowledge to understand how LLMs fit (or do not fit) in a development context, by properly pondering, for example, human, ethical, and legal factors. Also, softwareengineering researchers have a strong empirical background to evaluate the effectiveness of such models where state-of-the-art measurements might not suffice.
Bug-fix pattern detection has been investigated in the past in the context of classical software. However, while quantum software is developing rapidly, the literature is still lacking automated methods and tools to i...
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Zeiger is a pencil puzzle consisting of a rectangular grid, with each cell having an arrow pointing in horizontal or vertical direction. Some cells also contain a positive integer. the objective of this puzzle is to f...
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Dragon Fruit stem diseases significantly threaten crop yield and agricultural productivity. While multiple capable dragon fruit disease detection systems already exist, there is a scarcity of research investigating th...
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Distributed teams have gained prominence in software companies. However, studies indicate that Distributed software Development (DSD) companies often face challenges related to high developer turnover. Conversely, oth...
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In order to build a smart city and pursue more efficient city management, various industries have introduced intelligent question answering into process management. the intelligent question answering system based on t...
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Literature indicates that systems dynamics (SD) has the potential of modelling the behaviour of a system to understand enterprise behaviour and the effect of enterprise policies to address multiple performance areas. ...
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this paper presents NDAS (Noise-Decomposed Abnormal Segmentation), an innovative framework for robust medical image retrieval and segmentation. By explicitly decomposing noise and abnormal features, NDAS enhances retr...
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the intention of this research is to improve accuracy for the classification of multiple sclerosis systems from MRI images by using ResNetv2-50 compared with *** the accuracy of multiple sclerosis MRI images by compar...
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