STS: AI-Driven Smart Test Scenario Generation Tool
| dc.contributor.author | Bağlum, Cem | |
| dc.contributor.author | Yayan, Uǧur | |
| dc.date.accessioned | 2025-10-10T16:06:32Z | |
| dc.date.available | 2025-10-10T16:06:32Z | |
| dc.date.issued | 2025 | |
| dc.description | Isik University | en_US |
| dc.description.abstract | One of the most critical steps in the software testing lifecycle, test scenario generation, reduces process efficiency due to its high time and resource requirements. As an innovative solution to this issue, the Smart Test Scenario Tool (STS) has been developed. Smart Test Scenario Tool (STS) enhances contextual accuracy and automation in test scenario generation by analyzing documents in xlsx, py, cpp, txt, and docx formats using large language models. This approach minimizes time loss, and the risk of errors encountered in traditional manual testing processes while transforming test procedures into a context-driven and systematic framework, offering an innovative contribution to the literature. Strengthened with a Streamlit interface, MongoDB-supported database management, and Ollama integration, the system enables the test scenario generation process, a critical component of the software testing cycle, to be conducted more efficiently and reliably. The validity of the study was confirmed through two distinct projects, the first implemented in Python and the second in C++. © 2025 Elsevier B.V., All rights reserved. | en_US |
| dc.identifier.doi | 10.1109/SIU66497.2025.11112297 | |
| dc.identifier.isbn | 9798331566555 | |
| dc.identifier.scopus | 2-s2.0-105015371823 | |
| dc.identifier.uri | https://doi.org/10.1109/SIU66497.2025.11112297 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14627/1196 | |
| dc.language.iso | tr | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.ispartof | -- 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 -- Istanbul; Isik University Sile Campus -- 211450 | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | AI-Driven Testing | en_US |
| dc.subject | Large Language Models | en_US |
| dc.subject | Software Test Automation | en_US |
| dc.subject | Test Scenario Generation | en_US |
| dc.subject | Computer-Aided Software Engineering | en_US |
| dc.subject | Life Cycle | en_US |
| dc.subject | Model Checking | en_US |
| dc.subject | AI-Driven Testing | en_US |
| dc.subject | Critical Steps | en_US |
| dc.subject | Generation Tools | en_US |
| dc.subject | Language Model | en_US |
| dc.subject | Large Language Model | en_US |
| dc.subject | Scenarios Generation | en_US |
| dc.subject | Software Test Automation | en_US |
| dc.subject | Software Testing | en_US |
| dc.subject | Test Scenario | en_US |
| dc.subject | Test Scenario Generation | en_US |
| dc.subject | Integration Testing | en_US |
| dc.title | STS: AI-Driven Smart Test Scenario Generation Tool | en_US |
| dc.title.alternative | STS: Yapay Zeka Destekli Akıllı Test Senaryosu Olusturma Aracı | en_US |
| dc.type | Conference Object | en_US |
| dspace.entity.type | Publication | |
| gdc.author.scopusid | 58503905300 | |
| gdc.author.scopusid | 46061619200 | |
| gdc.description.department | Fenerbahçe University | en_US |
| gdc.description.departmenttemp | [Bağlum] Cem, Fenerbahçe University, Istanbul, Turkey; [Yayan] Uǧur, Yazilim Mühendisliǧi Bölümü, Eskişehir Osmangazi Üniversitesi, Eskisehir, Turkey | en_US |
| gdc.description.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| gdc.description.scopusquality | N/A | |
| gdc.description.wosquality | N/A |