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AutoScrum: Automating Project Planning Using Large Language Models

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arxiv 2306.03197 v1 pith:CIC3RCVO submitted 2023-06-05 cs.AI cs.CL

classification cs.AIcs.CL
keywords languageapproachstateautoscrumdesiredmodelsprogramstask
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Recent advancements in the field of large language models have made it possible to use language models for advanced reasoning. In this paper we leverage this ability for designing complex project plans based only on knowing the current state and the desired state. Two approaches are demonstrated - a scrum based approach and a shortcut plan approach. The scrum based approach executes an automated process of requirements gathering, user story mapping, feature identification, task decomposition and finally generates questions and search terms for seeking out domain specific information to assist with task completion. The shortcut approach looks at most recent snapshot of the current and desired state and generates the next most reasonable task to do in order to get to the desired state as quickly as possible. In this paper we automate everything using a novel concept of "Language Programs". These are programs written in natural language designed to process input data through the language model. Guidance language is used for all LLM programs. All demo source code for this paper is available at https://github.com/autoscrum/autoscrum

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 8 citations worldwide. Full citation record

  1. More AI Assistance Reduces Cognitive Engagement: Examining the AI Assistance Dilemma in AI-Supported Note-Taking

    cs.HC 2025-09 conditional novelty 6.0 of 10

    In a 30-person within-subject study, moderate AI note assistance produced the highest comprehension, while fully automated notes produced the lowest despite being preferred.

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