| dc.description.abstract | Software bugs reported on platforms like GitHub, Google Play, and Stack Overflow
represent a vast, untapped source of real-world failure data. While automated test case
generation has advanced with large language models, existing approaches operate only on
source code or specifications and ignore user-reported defects. This thesis presents an end
to-end framework that automatically discovers, structures, and converts internet-reported
bugs into executable test cases. The framework comprises of four integrated phases: multi
source bug collection from GitHub Issues and Play Store reviews using label-based
filtering; NLP analysis using TF-IDF and a Naïve Bayes classifier with rule-based
extraction; test case generation via LLMs (GPT-4, GPT-3.5-turbo, LLaMA-2 7B) using
engineered prompts with an abstention mechanism; and export to standard formats. A key
feature is principled refusal to generate low-quality test cases, prioritizing precision over
recall. The Framework is evaluated on 120 annotated bug reports from three open-source
repositories and five mobile apps, results show collection precision of 0.86, classification
F1 of 0.83, and GPT-4 composite test-case quality of 0.93. Generation quality correlates
with input completeness, validating the abstention design. This research establishes
internet-reported bugs as a viable input for AI-driven testing. The modular open-source
implementation supports independent reuse of each pipeline phase | en_US |
