Browsing by Author "Mao, Chenyu"

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    DevCoach: Supporting Students in Learning the Software Development Life Cycle at Scale with Generative Agents
    Wang, Tianjia; Ramanujan, Ramaraja; Lu, Yi; Mao, Chenyu; Chen, Yan; Brown, Chris (ACM, 2024-07-09)
    Supporting novice computer science students in learning the software development life cycle (SDLC) at scale is vital for ensuring the quality of future software systems. However, this presents unique challenges, including the need for effective interactive collaboration and access to diverse skill sets of members in the software development team. To address these problems, we present “DevCoach”, an online system designed to support students learning the SDLC at scale by interacting with generative agents powered by large language models simulating members with different roles in a software development team. Our preliminary user study results reveal that DevCoach improves the experiences and outcomes for students, with regard to learning concepts in SDLC’s “Plan and Design” and “Develop” phases.We aim to use our findings to enhance DevCoach to support the entire SDLC workflow by incorporating additional simulated roles and enabling students to choose their project topics. Future studies will be conducted in an online Software Engineering class at our institution, aiming to explore and inspire the development of intelligent systems that provide comprehensive SDLC learning experiences to students at scale.
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    Team 3: Object Detection and Topic Modeling (Objects&Topics) CS 5604 F2022
    Devera, Alan; Sahu, Raj; Masrourisaadat, Nila; Amirthalingam, Nirmal; Mao, Chenyu (Virginia Tech, 2023-01-17)
    The CS 5604: Information Storage and Retrieval class (Fall 2022), led by Dr. Edward Fox, has been assigned the task of designing and implementing a state-of-the-art information retrieval and analysis system that will support Electronic Theses & Dissertations (ETDs). Given a large collection of ETDs, we want to run different kinds of learning algorithms to categorize them into logical groups, and by the end, be able to suggest to an end-user the documents which are strongly related to the one they are looking for. The overall goal for the project is to have a service that can upload, search, and retrieve ETDs with their derived digital objects, in a human-readable format. Specifically, our team is tasked with analyzing documents using object detection and topic models, with the final deliverable being the Experimenter web page for the derived objects and topics. The object detection team worked with Faster R-CNN and YOLOv7 models, and implemented post-processing rules for saving objects in a structured format. As the final deliverable for object detection, inference on 5k ETDs has been completed, and the refined objects have been saved to the Repository. The topic modeling team worked with clustering ETDs to 10, 25, 50, and 100 topics with different models (LDA, NeuralLDA, CTM, ProdLDA). As the final deliverable for topic modeling, we store the related topics and related documents for 5k ETDs in the Team 1 database, so that Team 2 could provide the related topic and documents on the documents page. By the end of the semester the team was able to deliver the Experimenter web page for the derived objects and topics, and the related objects and topics for 5k ETDs stored in the Team 1 database.
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    TextMining
    He, Chongyu; Wei, Jianchi; Mao, Chenyu (Virginia Tech, 2022-05-10)
    Electronic theses and dissertations (ETDs) contain valuable knowledge that can be useful in a wide range of research areas. Accordingly, we are building electronic infrastructure leveraging advanced work on digital libraries, for discovering and accessing the knowledge buried in ETDs. We focus on our work to incorporate topic modeling into digital libraries for ETDs. We present ETD-Topics, a framework that extracts topics from a large text corpus in an unsupervised way. The representations learnt from topic models can be useful for downstream tasks such as searching and/or browsing documents by topic, document recommendation, topic recommendation, and describing temporal topic trends (e.g., from the perspective of disciplines or universities). The characteristics of different models make the classification distinguished. We provide four modes (LDA, NeuralLDA, ProdLDA, and CTM) to serve user groups with different browsing and searching requirements. Our job was to import the preprocessed database and the trained models (four models with different topic numbers), and to accurately display key information (such as topics, document title, abstract, etc.) on web pages. We chose Python as the main language to implement the back-end, while using Flask as a bridge connecting the back-end and front-end. On the basis of using HTML for displaying data, we were able to use JavaScript and CSS to make the whole set of web pages look more fluent and comfortable by optimizing the UI, to include graphic bars, buttons (like “Submit”, “Show more”, etc.), and tables.