Teaming is a core component in practically all professional softwareengineering careers, and as such, is a key skill taught in many undergraduate Computer Science programs. However, not all teams manage to work toget...
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ISBN:
(纸本)9781450391948
Teaming is a core component in practically all professional softwareengineering careers, and as such, is a key skill taught in many undergraduate Computer Science programs. However, not all teams manage to work together effectively, and in education, this can deprive some students of successful teaming experiences. In this work, we seek to gain insights into the characteristics of successful and unsuccessful undergraduate student teams in a softwareengineering course. We conduct semi-structured interviews with 18 students who have recently completed a team-based softwareengineering course to understand how they worked together, what challenges they faced, and how they tried to overcome these challenges. Our results show that common problems include communicating, setting and holding to deadlines, and effectively identifying tasks and their relative difficulty. Additionally, we find that self-reflection on what is working and not working or external motivators such as grades help some, but not all, teams overcome these challenges. Finally, we conclude with recommendations for educators on successful behaviours to steer teams towards, and recommendations for researchers on future work to better understand challenges that teams face.
Kindness is a psycho-social phenomenon that is also recognized as an important pro-social behaviour. The use of digital technology provides opportunities to promote kindness in various ways, such as in social media ca...
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ISBN:
(纸本)9798400705045
Kindness is a psycho-social phenomenon that is also recognized as an important pro-social behaviour. The use of digital technology provides opportunities to promote kindness in various ways, such as in social media campaigns and online communities. In principle, software engineers are well positioned to develop automated systems that can facilitate software-mediated kindness. However, in practice, incorporating kindness concerns explicitly in the development and use of software systems is challenging: kindness is highly context dependent, affected by a range of factors such as intentions and opportunity. In this paper, we explore systematic ways in which kindness concerns can be considered by software engineers. We propose a novel meta-model that captures essential entities and relations associated with kindness. The meta-model enables the representation of possible instances or opportunities for performing acts of kindness, by considering the actors involved (such as giver, receiver, and observer), their psychological and social attributes that promote kindness (such as emotional states and social relatedness), the acts needed to fulfil kindness opportunities (such as motivation, ability, and timeliness), and other contextual factors (such as location and time). Our meta-model is demonstrated through two software application scenarios that enable charitable donations and kindness in business. Overall, our proposal offers a first, tentative, but concrete step towards enabling kind computing, and promoting kindness in software systems.
The entire success of software is impacted by software bug prediction (SBP), a crucial part of the software development and maintenance life cycles. It is necessary to anticipate issues in order to increase the softwa...
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The field of softwareengineering has seen an increase in research aimed at predicting software defects. The importance of non-functional defects in this study can effectively predict software defects, thus simplifyin...
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We present NNVISR - an open-source filter plugin for the VapourSynth video processing framework, which facilitates the application of neural networks for various kinds of video enhancing tasks, including denoising, su...
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Predicting software defects early in the development process not only enhances the quality and reliability of the software but also decreases the cost of development. A wide range of machine learning techniques can be...
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ISBN:
(纸本)9798331541378
Predicting software defects early in the development process not only enhances the quality and reliability of the software but also decreases the cost of development. A wide range of machine learning techniques can be employed to create software defect prediction models, but the effectiveness and accuracy of these models are often influenced by the choice of appropriate feature subset. Since finding the optimal feature subset is computationally intensive, heuristic and metaheuristic approaches are commonly employed to identify near-optimal solutions within a reasonable time frame. Recently, the quantum computing paradigm quantum annealing (QA) has been deployed to find solutions to complex optimization problems. This opens up the possibility of addressing the feature subset selection problem with a QA machine. Although several strategies have been proposed for feature subset selection using a QA machine, little exploration has been done regarding the viability of a QA machine for feature subset selection in software defect prediction. This study investigates the potential of 11 -Wave QA system for this task, where we formulate a mutual information (NM -based filter approach as an optimization problem and utilize a 1) -Wave Quantum Processing Unit (QPU) solver as a QA solver for feature subset selection. We evaluate the performance of this approach using multiple software defect datasets from the AEEM, JIRA, and NASA projects. We also utilize a 1) -Wave classical solver for comparative analysis. Our experimental results demonstrate that QA -based feature subset selection can enhance software defect prediction. Although the 1) -Wave QPU solver exhibits competitive prediction performance with the classical solver in software defect prediction, it significantly reduces the time required to identify the best feature subset compared to its classical counterpart.
This paper introduces Speedcode, an online programming platform that aims to improve the accessibility of software performance-engineering education. At its core, Speedcode provides a platform that lets users gain han...
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ISBN:
(纸本)9798350364613;9798350364606
This paper introduces Speedcode, an online programming platform that aims to improve the accessibility of software performance-engineering education. At its core, Speedcode provides a platform that lets users gain hands-on experience in software performance engineering and parallel programming by completing short programming exercises. Speedcode challenges users to develop fast multicore solutions for short programming problems and evaluates their code's performance and scalability in a quiesced cloud environment. Speedcode supports parallel programming using OpenCilk, task-parallel computing platform that is open-source and easy to program, teach and use for research. Speedcode aims to reduce barriers to learning and teaching software performance engineering. It allows users to run and evaluate their code on modern multicore machines from their own computer without installing any software. This provides users an easy introduction to the topic, and enables teachers to more easily incorporate lessons on software performance engineering into their courses without incurring the onerous overhead of needing to setup computing environments for their students.
In this paper, we show use-case examples of the application of affordable programmable measurement hardware in circuit analysis as part of a graduate course in Electric-circuit theory. Besides basic measurement modes,...
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With the rapid development of the Internet of Things and cloud computing technology, the application of multimedia augmented reality (AR) intelligent tourism systems in the tourism industry has gradually increased. Tr...
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Modern cloud control plane infrastructure like Microsoft Azure has evolved into a complex one to serve customer needs for diverse types of services and adequate cloud-based resources. On such interconnected system, im...
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ISBN:
(纸本)9798350300376
Modern cloud control plane infrastructure like Microsoft Azure has evolved into a complex one to serve customer needs for diverse types of services and adequate cloud-based resources. On such interconnected system, implementing changes at one component can have an impact on other components, even across different hierarchical computing layers. As a result of the complexity and interconnected nature of the cloud-based services, it poses a challenge to correctly attribute service quality degradation to a control plane change, to infer causality between the two and to mitigate any negative impact. In this paper, we present Aegis, an end-to-end analytical service for attributing control plane change impact across computing layers and service components in large-scale real-world cloud systems. Aegis processes and correlates service health signals and control plane changes across components to construct the most probable causal relationship. Aegis at its core leverages a domain knowledge-driven correlation algorithm to attribute platform signals to changes, and a counterfactual projection model to quantify control plane change impact to customers. Aegis can mitigate the impact of bad changes by alerting service team and recommending pausing the bad ones. Since Aegis' inception in Azure Control Plane 12 months ago, it has caught several bad changes across service components and layers, and promptly paused them to guard the quality of service. Aegis achieves precision and recall around 80% on real-world control plane deployments.
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