Browsing by Author "Ramanujan, Ramaraja"

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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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    Improving Rainfall Index Insurance: Evaluating Effects of Fine-Scale Data and Interactive Tools in the PRF-RI Program
    Ramanujan, Ramaraja (Virginia Tech, 2024-06-04)
    Since its inception, the Pasture, Rangeland, and Forage Rainfall Index (PRF-RI) insurance program has issued a total of $8.8 billion in payouts. Given the program's significance, this thesis investigates methodologies to help improve it. For the first part, we evaluated the impact of finer-scale precipitation data on insurance payouts by comparing how the payout distribution differs between the program's current dataset and the finer-scale precipitation dataset by creating a simulated scenario where all parameters are constant except the rainfall index computed by the respective dataset. The analysis for Texas in 2021 revealed that using the finer-scale dataset to compute the rainfall index would result in payouts worth $27 million less than the current dataset. The second part of the research involved the development of two interactive decision-support tools: the "Next-Gen PRF" web tool and the "AgInsurance LLM" chatbot. These tools were designed to help users understand complex insurance parameters and make informed decisions regarding their insurance policies. User studies for the "Next-Gen PRF" tool measured usability, comprehension decision-making efficiency, and user experience, showing that it outperforms traditional methods by providing insightful visualizations and detailed descriptions. The findings suggest that using fine-scale precipitation data and advanced decision-support technologies can substantially benefit the PRF-RI program by reducing spatial basis risk and promoting user education, thus leading to higher user engagement and enrollment.
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    Team 2 for End Users
    Paidiparthy, Manoj Prabhakar; Ramanujan, Ramaraja; Teegalapally, Akshita; Muralikrishnan, Madhuvanti; Balar, Romil Khimraj; Juvekar, Shaunak; Murali, Vivek (Virginia Tech, 2023-01-11)
    A huge collection of Electronic Theses and Dissertations (ETDs) has valuable information. However, accessing the information from these documents has proven to be challenging as the process is mostly manual. We propose to build a unique Information Retrieval System that will support searching, ranking, browsing, and recommendations for a large collection of ETDs. The system indexes the digital objects related to the ETD, like documents, chapters, etc. The user can then query the indexed objects through a carefully designed web interface. The web interface provides users with utilities to sort, filter, and query specific fields. We have incorporated machine learning models to support semantic search. To enhance user engagement, we provide the user with a list of recommended documents based on the user's actions and topics of interest. A total of 57,130 documents and 21,537 chapters were indexed. The system was tested by the Fall 2022 CS 5604 class, which had 28 members, and was found to fulfill most of the goals set out at the beginning of the semester.