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Found 4 papers, 1 papers with code
Unveiling the potential of large language models in generating semantic and cross-language clones
Similarly, if someone possesses a code snippet in a particular programming language but seeks equivalent functionality in a different language, a semantic cross-language code clone generation approach could provide valuable assistance. In this study, using SemanticCloneBench as a vehicle, we evaluated how well the GPT-3 model could help generate semantic and cross-language clone variants for a given fragment. We have comprised a diverse set of code fragments and assessed GPT-3s performance in generating code variants. Through extensive experimentation and analysis, where 9 judges spent 158 hours to validate, we investigate the model's ability to produce accurate and semantically correct variants.
Leveraging Structural Properties of Source Code Graphs for Just-In-Time Bug Prediction
A graph is one of the most commonly used representations for understanding relational data.
A Systematic Review of Automated Query Reformulations in Source Code Search
Developers consult these requests and often choose a few keywords from them as an ad hoc query.
The Forgotten Role of Search Queries in IR-based Bug Localization: An Empirical Study
We confirmed that the state-of-the-art query construction approaches are indeed not sufficient for constructing appropriate queries (for bug localization) from certain natural language-only bug reports although they contain such queries.
