  Semantic and Cross-language code clone generation may be useful for code
reuse, code comprehension, refactoring and benchmarking. OpenAI's GPT model has
potential in such clone generation as GPT is used for text generation. When
developers copy/paste codes from Stack Overflow (SO) or within a system, there
might be inconsistent changes leading to unexpected behaviours. 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. Our findings shed light on GPT-3's strengths in code generation,
offering insights into the potential applications and challenges of using
advanced language models in software development. Our quantitative analysis
yields compelling results. In the realm of semantic clones, GPT-3 attains an
impressive accuracy of 62.14% and 0.55 BLEU score, achieved through few-shot
prompt engineering. Furthermore, the model shines in transcending linguistic
confines, boasting an exceptional 91.25% accuracy in generating cross-language
clones
