Abstract
The research focuses on developing an improved system for generating and analyzing text by combining GPT-2, LSTM, and CNN models to address challenges in automated content creation for software engineering tasks. The system targets specific applications such as requirements engineering, software documentation, and code comment generation. It generates 150-token text samples based on over 100 user-provided prompts. These generated texts are first processed through an LSTM layer to capture semantic meaning, then passed through a CNN module to extract syntactic and semantic features. All outputs are stored in structured CSV files to support future analysis. Evaluation results demonstrate positive impacts on coherence, diversity, and domain relevance. A case study involving documentation generation for a real-world software project confirms the system’s practical value in streamlining development workflows. Our work formalizes the role of hybrid language models in explicitly engineered environments and offers concrete recommendations for advancing future research in NLP-driven software development.
Recommended Citation
Hussien, Nadia Mahmood; Farhan, Aumama Mohammed; Mohialden, Yasmin Makki; Jabbar, Qabas Abdal Zahraa; Abdulmaged, Shahbaa Mohammed; and Arif, Asmaa Hatem
(2025)
"Software Engineering-Oriented Text Generation and Analysis Using GPT-2,"
Iraqi Journal for Computer Science and Mathematics: Vol. 6:
Iss.
3, Article 32.
DOI: https://doi.org/10.52866/2788-7421.1312
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/32
