  In recent years, the use of automated source code generation utilizing
transformer-based generative models has expanded, and these models can generate
functional code according to the requirements of the developers. However,
recent research revealed that these automatically generated source codes can
contain vulnerabilities and other quality issues. Despite researchers' and
practitioners' attempts to enhance code generation models, retraining and
fine-tuning large language models is time-consuming and resource-intensive.
Thus, we describe FRANC, a lightweight framework for recommending more secure
and high-quality source code derived from transformer-based code generation
models. FRANC includes a static filter to make the generated code compilable
with heuristics and a quality-aware ranker to sort the code snippets based on a
quality score. Moreover, the framework uses prompt engineering to fix
persistent quality issues. We evaluated the framework with five Python and Java
code generation models and six prompt datasets, including a newly created one
in this work (SOEval). The static filter improves 9% to 46% Java suggestions
and 10% to 43% Python suggestions regarding compilability. The average
improvement over the NDCG@10 score for the ranking system is 0.0763, and the
repairing techniques repair the highest 80% of prompts. FRANC takes, on
average, 1.98 seconds for Java; for Python, it takes 0.08 seconds.
