{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1942"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1942","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Automated goal model generation from user stories using Large Language Models","abstract":"In agile software development, user stories capture stakeholder needs but often fail to represent complex requirement relationships. Goal modeling addresses this by linking high-level goals to specific requirements, but manually transforming user stories into goal models is challenging. This research explores using Large Language Models (LLMs) to automate goal model generation through multi-step prompt engineering. LLMs extract intentional elements—goals, tasks, actors, and resources—and generate Goal-oriented Requirements Language (GRL) models compatible with tools like jUCMNav. The study evaluates GPT-4, Llama, and Cohere, focusing on syntactic completeness and correctness. GPT-4 outperforms others, particularly in extracting implicit goals and soft goals, but struggles with intricate relationships like means-end and contribution links. Despite limitations, LLMs show promise in automating labor-intensive aspects of goal modeling, making the process more efficient. This research highlights their potential to support requirements engineers and integrate goal modeling into agile workflows.","abstract_html":"In agile software development, user stories capture stakeholder needs but often fail to represent complex requirement relationships. Goal modeling addresses this by linking high-level goals to specific requirements, but manually transforming user stories into goal models is challenging. This research explores using Large Language Models (LLMs) to automate goal model generation through multi-step prompt engineering. LLMs extract intentional elements—goals, tasks, actors, and resources—and generate Goal-oriented Requirements Language (GRL) models compatible with tools like jUCMNav. The study evaluates GPT-4, Llama, and Cohere, focusing on syntactic completeness and correctness. GPT-4 outperforms others, particularly in extracting implicit goals and soft goals, but struggles with intricate relationships like means-end and contribution links. Despite limitations, LLMs show promise in automating labor-intensive aspects of goal modeling, making the process more efficient. This research highlights their potential to support requirements engineers and integrate goal modeling into agile workflows.","abstract_has_math":false,"creators":["Siddeshwar, Vaishali"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Alwidian, Sanaa","Makrehchi, Masoud"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-01","date_published":"2025-04-01","updated_at":"2026-07-24T05:35:26Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1942","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Alwidian, Sanaa","Makrehchi, Masoud"]},{"key":"dc:creator","label":"Author","values":["Siddeshwar, Vaishali"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-04-29T19:20:49Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-04-29T19:20:49Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-04-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/1942"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In agile software development, user stories capture stakeholder needs but often fail to represent complex requirement relationships. Goal modeling addresses this by linking high-level goals to specific requirements, but manually transforming user stories into goal models is challenging. This research explores using Large Language Models (LLMs) to automate goal model generation through multi-step prompt engineering. LLMs extract intentional elements—goals, tasks, actors, and resources—and generate Goal-oriented Requirements Language (GRL) models compatible with tools like jUCMNav. The study evaluates GPT-4, Llama, and Cohere, focusing on syntactic completeness and correctness. GPT-4 outperforms others, particularly in extracting implicit goals and soft goals, but struggles with intricate relationships like means-end and contribution links. Despite limitations, LLMs show promise in automating labor-intensive aspects of goal modeling, making the process more efficient. This research highlights their potential to support requirements engineers and integrate goal modeling into agile workflows."]},{"key":"dc:title","label":"Title","values":["Automated goal model generation from user stories using Large Language Models"]}]}],"canonical_facts":{"dc:contributor.advisor":["Alwidian, Sanaa","Makrehchi, Masoud"],"dc:creator":["Siddeshwar, Vaishali"],"dc:date.accessioned":["2025-04-29T19:20:49Z"],"dc:date.available":["2025-04-29T19:20:49Z"],"dc:date.issued":["2025-04-01"],"dc:description.abstract":["In agile software development, user stories capture stakeholder needs but often fail to represent complex requirement relationships. Goal modeling addresses this by linking high-level goals to specific requirements, but manually transforming user stories into goal models is challenging. This research explores using Large Language Models (LLMs) to automate goal model generation through multi-step prompt engineering. LLMs extract intentional elements—goals, tasks, actors, and resources—and generate Goal-oriented Requirements Language (GRL) models compatible with tools like jUCMNav. The study evaluates GPT-4, Llama, and Cohere, focusing on syntactic completeness and correctness. GPT-4 outperforms others, particularly in extracting implicit goals and soft goals, but struggles with intricate relationships like means-end and contribution links. Despite limitations, LLMs show promise in automating labor-intensive aspects of goal modeling, making the process more efficient. This research highlights their potential to support requirements engineers and integrate goal modeling into agile workflows."],"dc:identifier.uri":["https://hdl.handle.net/10155/1942"],"dc:language.iso":["en"],"dc:title":["Automated goal model generation from user stories using Large Language Models"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:26Z"}