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A new paper titled "STALL+: Boosting LLM-based Repository-level Code Completion with Static Analysis" shows the benefits of integrating static analysis with LLMs. (
A new paper titled "STALL+: Boosting LLM-based Repository-level Code Completion with Static Analysis" shows the benefits of integrating static analysis with LLMs. (
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https://arxiv.org/abs/2406.10018
https://arxiv.org/abs/2406.10018
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Authors evaluate 4 key questions:
Authors evaluate 4 key questions:
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- How does each static analysis integration strategy perform in LLM-based repository-level code completion?
- How does each static analysis integration strategy perform in LLM-based repository-level code completion?
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> They found that integrating static analysis in the prompting phase (especially with file-level dependencies) can achieve the substantially larger improvements than other phases.
> They found that integrating static analysis in the prompting phase (especially with file-level dependencies) can achieve the substantially larger improvements than other phases.
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- How do different combinations of integration strategies affect LLM-based repository-level code completion?
- How do different combinations of integration strategies affect LLM-based repository-level code completion?
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> Languages that are easier to analyze like Java show more improvements compared to dynamic languages like Python.
> Languages that are easier to analyze like Java show more improvements compared to dynamic languages like Python.
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- How do static analysis integration strategies perform when compared or combined with RAG in LLM-based repository-level code completion?
- How do static analysis integration strategies perform when compared or combined with RAG in LLM-based repository-level code completion?
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> Static analysis and RAG are complementary and boost the overall accuracy.
> Static analysis and RAG are complementary and boost the overall accuracy.
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-  What are the online costs of different integration strategies in LLM-based repository-level code completion?
-  What are the online costs of different integration strategies in LLM-based repository-level code completion?
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> Combining prompting-phase static analysis and RAG is the best option for cost-effectiveness.
> Combining prompting-phase static analysis and RAG is the best option for cost-effectiveness.
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In my 
In my 
mention
@owasp
owasp
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 App Sec keynote last year, I had described how one can do static analysis augmented generation (SaAG) to boost the accuracy of LLM based patches for vulnerability remediation. (you can see the talk here - 
 App Sec keynote last year, I had described how one can do static analysis augmented generation (SaAG) to boost the accuracy of LLM based patches for vulnerability remediation. (you can see the talk here - 
link
https://www.youtube.com/watch?v=Cw4-ZnUNVLs
https://www.youtube.com/watch?v=Cw4-ZnUNVLs
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)
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A new paper titled "STALL+: Boosting LLM-based Repository-level Code Completion with Static Analysis" shows the benefits of integrating static analysis with LLMs. (https://arxiv.org/abs/2406.10018)

Authors evaluate 4 key questions:

- How does each static analysis integration strategy perform in LLM-based repository-level code completion?
> They found that integrating static analysis in the prompting phase (especially with file-level dependencies) can achieve the substantially larger improvements than other phases.

- How do different combinations of integration strategies affect LLM-based repository-level code completion?
> Languages that are easier to analyze like Java show more improvements compared to dynamic languages like Python.

- How do static analysis integration strategies perform when compared or combined with RAG in LLM-based repository-level code completion?
> Static analysis and RAG are complementary and boost the overall accuracy.

-  What are the online costs of different integration strategies in LLM-based repository-level code completion?
> Combining prompting-phase static analysis and RAG is the best option for cost-effectiveness.

In my @owasp App Sec keynote last year, I had described how one can do static analysis augmented generation (SaAG) to boost the accuracy of LLM based patches for vulnerability remediation. (you can see the talk here - https://www.youtube.com/watch?v=Cw4-ZnUNVLs)
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