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arXiv 2024年

作者： Kumar, Jahnavi Chimalakonda, Sridhar Research in Intelligent Software & Human Analytics Lab Department of Computer Science and Engineering Indian Institute of Technology Tirupati India

Code review is a crucial process before deploying code to production, as it validates the code, provides suggestions for improvements, and identifies errors such as missed edge cases. In projects with regular production releases, the effort required for peer code-reviews remains high. Consequently, there has been significant interest from software engineering (SE) researchers in automating the code review process. Previous research on code review automation has typically approached the task as three independent sub-tasks: review necessity prediction, review comment generation, and code refinement. Our study attempts to (i) leverage the relationships between the sub-tasks of code review automation, by developing a multi-task model that addresses all tasks in an integrated manner, and (ii) increase model robustness on unseen data via collaborative large language model (LLM) modeling, while retaining the proprietary nature of code, by using federated learning (FL). The study explores five simple techniques for multi-task training, including two sequential methods, one parallel method, and two cumulative methods. The results indicate that sequentially training a federated LLM (FedLLM) for our code review multi-task use case is less efficient in terms of time, computation, and performance metrics, compared to training separate models for each task. Because sequential training demonstrates catastrophic forgetting, alternatively cumulative fine-tuning for multi-task training performs better than training models for individual tasks. This study highlights the need for research focused on effective fine-tuning of multi-task FedLLMs for SE tasks. © 2024, CC BY.

关键词： Coding errors

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What Do Developers Feel About Fast-Growing Programming Languages? An Exploratory Study

What Do Developers Feel About Fast-Growing Programming Langu...

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IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Jahnavi Kumar Sridhar Chimalakonda Department of Computer Science and Engineering Research in Intelligent Software & Human Analytics Lab Indian Institute of Technology Tirupati India

ISBN: (数字)9798400705861

ISBN: (纸本)9798350352276

The developer community has witnessed an unprecedented surge in recent years, with over 100 million active developers on the GitHub platform in 2023. Along with it, there is a significant rise and adoption of new programming languages, frameworks and tools. The study aims to comprehend how developers perceive these fastgrowing programming languages by performing emotion analysis of developer’s comments posted in various software artifacts such as pull requests, issues and commits of GitHub repositories. In this regard, we employed a fine-tuned small ‘Large Language Model’ (sLLM) to detect emotions, leveraging a balanced dataset from existing literature complemented with additional manual annotations from our collected data. We have analyzed 10 fast-growing programming languages, examining 1.8 million comments from 4.1 million non-code artifacts. To further validate our findings, we have performed a qualitative survey and analysis with 28 developers. Our study reveals insights into the developers emotion associated with these fast-growing languages. Notably, “Surprise’’ is the predominant emotion associated with these languages. CCS CONCEPTS • Software and its engineering $\rightarrow$ General programming languages.

关键词： Surveys computer languages Annotations Software Data models Surges Software development management

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RJoules: An Energy Measurement Tool for R 23

RJoules: An Energy Measurement Tool for R

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Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering

作者： Rajrupa Chattaraj Sridhar Chimalakonda Research in Intelligent Systems and Human Analytics Lab Department of Computer Science and Engineering Indian Institute of Technology Tirupati India

ISBN: (纸本)9798350329964

With the exponential growth of data, the demand for effective data analysis tools has increased significantly. R language, known for its statistical modeling and data analysis capabilities, has become one of the most popular programming languages among data scientists and researchers. As the importance of energy-aware software systems continues to rise, several studies investigate the impact of source code and different stages of machine learning model training on energy consumption. However, existing studies in this domain primarily focus on programming languages like Python and Java, resulting in a lack of energy measuring tools for other programming languages such as R. To address this gap, we propose "RJoules", a tool designed to measure the energy consumption of R code snippets. We evaluate the correctness and performance of RJoules by applying it to four machine learning algorithms on three different systems. Our aim is to support developers and practitioners in building energy-aware systems in R. The demonstration of the tool is available at https://***/yMKFuvAM-DE and related artifacts at https://***/RJoules/.

关键词： energy measurement

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A Toolbox of Feminist Wonder: Theories and methods that can make a difference 26

A Toolbox of Feminist Wonder: Theories and methods that can ...

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26th ACM Conference on computer-Supported Cooperative Work and Social Computing, CSCW 2023

作者： Hansson, Karin Bardzell, Shaowen Bhandari, Aparajita Boulicault, Marion Doyle, Dylan Thomas Erete, Sheena Cerratto Pargman, Teresa Lazem, Shaimaa Muller, Michael Normark, Maria Petterson, Adrian Rosén, Anton Poikolainen Taylor, Alex S. Thomas, Jakita O. Watson, Julia Södertörn University The School of Natural Sciences Technology and Environmental Studies Sweden Georgia Institute of Technology The School of Interactive Computing United States Cornell University Department of Communication United States University of Edinburgh Department of Philosophy United States University of Colorado Boulder Department of Information Science United States University of Maryland College of Information United States Stockholm University The Department of Computer and Systems Sciences Sweden City for Scientific Research and Technological Applications The Informatics Research Institute Egypt Ibm Research Human Centered Ai United States Uppsala University Department of Informatics and Media Sweden University of Toronto Faculty of Information Canada Aalto University The School of Arts Design and Architecture Finland City University of London The Centre for Human Computer Interaction Design United Kingdom Auburn University Department of Computer Science and Software Engineering United States University of Toronto Department of Computer Science Canada

ISBN: (纸本)9798400701290

This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space for change. Since its inception over a decade ago, feminist HCI has progressed from the margins to mainstream HCI, with numerous references in the literature. Feminist HCI has also evolved to incorporate other critical HCI practices such as Queer HCI, participatory design, and speculative design. While feminist approaches have grown in popularity and become mainstream, it is getting more difficult to distinguish the feminist emancipatory core from other attempts of developing and improving society in various ways. In this workshop, we therefore want to revisit our feminist roots, where theory is a liberatory and creative practice, motivated by affect, curiosity, and wonder. From this standpoint, we consider which of our feminist tools can make a significant difference today, in a highly datafied world. The goal of this workshop is to;1) create an inventory of feminist theories and concepts that have had an impact on our work as designers, educators, researchers, and activists;2) develop a feminist toolbox for the CSCW community to strengthen our feminist literacy. © 2023 Owner/Author.

关键词： Computation theory

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Please don't answer out of context: Personalized Dialogue Generation Fusing Persona and Context

Please don't answer out of context: Personalized Dialogue Ge...

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International Joint Conference on Neural Networks (IJCNN)

作者： Fucheng Wang Yunfei Yin Faliang Huang Kaigui Wu College of Computer Science Chongqing University Chongqing China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China

In realistic conversations, “responses” are closely related to persona and context. However, current personalized dialogue generation methods only focus on the consistency of the persona of responses and yet ignore context coherence, as may produce low-quality responses. To address the issue, we propose a novel model, named PCF (Persona and Context Fusion), which builds two decoders for understanding personality consistency and context coherence respectively on a common encoder-decoder architecture. In this model, an inter-layer attention fusion mechanism is designed for the two decoders to effectively fuse persona and context, and a decoupled way of training is conducted on an additional large-scale non-dialogue inference dataset to enhance the consistent understanding ability of the two decoders. Furthermore, considering that generating responses may be tedious, the ScaleGrad loss function is applied to enhance the diversity of responses. Experimental results on two publicly available datasets show that the dialogues generated by our PCF are significantly higher in quality than strong baselines.

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Task-Agnostic Learning to Accomplish New Tasks

arXiv

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arXiv 2022年

作者： Zhang, Xianqi Wang, Xingtao Liu, Xu Wang, Wenrui Fan, Xiaopeng Zhao, Debin Research Center of Intelligent Interface and Human Computer Interaction the Department of Computer Science and Technology Harbin Institute of Technology Harbin150001 China

Reinforcement Learning (RL) and Imitation Learning (IL) have made great progress in robotic control in recent years. However, these methods show obvious deterioration for new tasks that need to be completed through new combinations of actions. RL methods heavily rely on reward functions that cannot generalize well for new tasks, while IL methods are limited by expert demonstrations which do not cover new tasks. In contrast, humans can easily complete these tasks with the fragmented knowledge learned from task-agnostic experience. Inspired by this observation, this paper proposes a task-agnostic learning method (TAL for short) that can learn fragmented knowledge from task-agnostic data to accomplish new tasks. TAL consists of four stages. First, the task-agnostic exploration is performed to collect data from interactions with the environment. The collected data is organized via a knowledge graph. Compared with the previous sequential structure, the knowledge graph representation is more compact and fits better for environment exploration. Second, an action feature extractor is proposed and trained using the collected knowledge graph data for task-agnostic fragmented knowledge learning. Third, a candidate action generator is designed, which applies the action feature extractor on a new task to generate multiple candidate action sets. Finally, an action proposal is designed to produce the probabilities for actions in a new task according to the environmental information. The probabilities are then used to select actions to be executed from multiple candidate action sets to form the plan. Experiments on a virtual indoor scene show that the proposed method outperforms the state-of-the-art offline RL method: CQL by 35.28% and the IL method: BC by 22.22%. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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In the Moment of Glitch: Engaging with Misalignments in Ethical Practice 25

In the Moment of Glitch: Engaging with Misalignments in Ethi...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Rachael Garrett Kat Hawkins Patrick Brundell Simon Castle-Green Paul Tennent Feng Zhou Airi Lampinen Kristina Höök Steve Benford Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden Centre for Dance Research Coventry University Coventry United Kingdom Mixed Reality Lab School of Computer Science University of Nottingham Nottingham Nottinghamshire United Kingdom Computer Science University of Nottingham Nottingham United Kingdom Mixed Reality Lab School of Computer Science University of Nottingham Nottingham United Kingdom Computer Science / Mixed Reality Lab the University of Nottingham Nottingham United Kingdom Department of Computer and Systems Sciences Stockholm University Stockholm Sweden

ISBN: (纸本)9798400713941

Glitches - moments when technologies do not work as desired - will become increasingly common as industrially-designed robots move into complex contexts. Taking glitches to be potential sites of critical ethical reflection, we examine a glitch that occurred in the context of a collaborative research project where professional dancers with different disabilities improvised with a robotic arm. Through a first-person account, we analyse how the dancer, the robot, and the rest of the research team enacted ethics in the moment of glitch. Through this analysis, we discovered a deep and implicit ethical misalignment wherein our enactments of ethics in response to the glitch did not align with the values of the project. This prompted a critical re-engagement with our research process through which we forged a dialogue between different ethical perspectives that acted as an invitation to bring us back into ethical alignment with the project's values. © 2025 Copyright held by the owner/author(s).

关键词： ethics

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From Reader to Experiencer: Design and Evaluation of an Interactive Vr Story for Promoting the Situation Awareness of Public Health Threats

SSRN

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SSRN 2023年

作者： Zhu, Qian Yuan, Linping Xu, Zian Yang, Leni Xia, Meng Wang, Zhuo Liang, Hai-Ning Ma, Xiaojuan The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Carnegie Mellon University Human-Computer Interaction Institute PittsburghPA United States Xi’an Jiaotong Liverpool University Department of Computing Suzhou China

Informing people of health threats is crucial as they may lack situation awareness (SA) of risky situations when they do not have personal experiences or lessons learned from dangerous encounters. In this work, we explored the potential of using an interactive VR (Virtual Reality) story to raise people's SA of health risks. We first worked with seven participants to iteratively design the immersive story and summarized five design considerations. Then, we implemented the VR story with five design features referring to the design considerations. Using a between-subjects study (N=62), we evaluated the effects of the VR story on raising SA and investigated the role of each design feature. Our results show that the VR story can promote SA by enhancing people's connection to risky situations. Design features such as immersive visualizations, multiple perspectives, and embodied interactions contribute to this connection. Drawing on qualitative and quantitative findings, we discuss the implications of designing VR stories for promoting public health. © 2023, The Authors. All rights reserved.

关键词： Virtual reality

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Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks

arXiv

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arXiv 2022年

作者： Haghpanah, Mohammad Amin Saeedizade, Ehsan Masouleh, Mehdi Tale Kalhor, Ahmad Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Computer Science and Engineering University of Nevada Reno United States

This paper presents a lightweight algorithm for feature extraction, classification of seven different emotions, and facial expression recognition in a real-time manner based on static images of the human face. In this regard, a Multi-Layer Perceptron (MLP) neural network is trained based on the foregoing algorithm. In order to classify human faces, first, some pre-processing is applied to the input image, which can localize and cut out faces from it. In the next step, a facial landmark detection library is used, which can detect the landmarks of each face. Then, the human face is split into upper and lower faces, which enables the extraction of the desired features from each part. In the proposed model, both geometric and texture-based feature types are taken into account. After the feature extraction phase, a normalized vector of features is created. A 3-layer MLP is trained using these feature vectors, leading to 96% accuracy on the test set. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Exploring Trade-Offs for Online Mental Health Matching: Agent-Based Modeling Study

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JMIR Formative Research 2024年 8卷 e58241页

作者： Liu, Yuhan Fang, Anna Moriarty, Glen Firman, Cristopher Kraut, Robert E. Zhu, Haiyi Department of Computer Science Princeton University Princeton NJ United States Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh PA United States 7 Cups Wilmington DE United States

Background: Online mental health communities (OMHCs) are an effective and accessible channel to give and receive social support for individuals with mental and emotional issues. However, a key challenge on these platforms is finding suitable partners to interact with given that mechanisms to match users are currently underdeveloped or highly naive. Objective: In this study, we collaborated with one of the world’s largest OMHCs;our contribution is to show the application of agent-based modeling for the design of online community matching algorithms. We developed an agent-based simulation framework and showcased how it can uncover trade-offs in different matching algorithms between people seeking support and volunteer counselors. Methods: We used a comprehensive data set spanning January 2020 to April 2022 to create a simulation framework based on agent-based modeling that replicates the current matching mechanisms of our research site. After validating the accuracy of this simulated replication, we used this simulation framework as a “sandbox” to test different matching algorithms based on the deferred acceptance algorithm. We compared trade-offs among these different matching algorithms based on various metrics of interest, such as chat ratings and matching success rates. Results: Our study suggests that various tensions emerge through different algorithmic choices for these communities. For example, our simulation uncovered that increased waiting time for support seekers was an inherent consequence on these sites when intelligent matching was used to find more suitable matches. Our simulation also verified some intuitive effects, such as that the greatest number of support seeker–counselor matches occurred using a “first come, first served” protocol, whereas relatively fewer matches occurred using a “last come, first served” protocol. We also discuss practical findings regarding matching for vulnerable versus overall populations. Results by demographic group reveal

关键词： agent-based modeling algorithmic matching mental health online communities social computing

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