PathOCL: Path-Based Prompt Augmentation for OCL Generation with GPT-4

no code implementations • 21 May 2024 • Seif Abukhalaf, Mohammad Hamdaqa, Foutse khomh

Moreover, the average prompt size crafted using PathOCL significantly decreases when scaling the size of the UML class models.

On Codex Prompt Engineering for OCL Generation: An Empirical Study

no code implementations • 28 Mar 2023 • Seif Abukhalaf, Mohammad Hamdaqa, Foutse khomh

We investigate the reliability of OCL constraints generated by Codex from natural language specifications.

Few-Shot Learning Prompt Engineering +5

EnHMM: On the Use of Ensemble HMMs and Stack Traces to Predict the Reassignment of Bug Report Fields

no code implementations • 15 Mar 2021 • Md Shariful Islam, Abdelwahab Hamou-Lhadj, Korosh K. Sabor, Mohammad Hamdaqa, Haipeng Cai

Finally, when comparing EnHMM to Im. ML. KNN, a recent approach in the field, we found that the average F-measure score of EnHMM improves the average F-measure of Im. ML. KNN by 6. 80% and improves the average recall of Im. ML. KNN by 36. 09%.

Bug fixing