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The techniques currently used to match algorithmic features in a given source code are:
a conversion of its AST into a textual flattened representation;
regular expressions for low-level features;
SQL queries for derived features.
This is not quite satisfying for a number of reasons:
Although the creation of new regular expressions for Paroxython is partially automated (see suggest_regexp.py) and do not require advanced functionalities like recursion (thanks to the flattening of the AST), they remain notoriously difficult to read and maintain.
Using SQL to derive new features is highly unusual, and sometimes verbose.
No support for inference.
Not based on sound theoretical foundations, resulting in fragile patterns, false negative on corner cases, etc.
It would be interesting to explore third-party static analytic tools, and try to replace gradually my home-made patterns. The tools I am currently aware of are:
The techniques currently used to match algorithmic features in a given source code are:
This is not quite satisfying for a number of reasons:
suggest_regexp.py
) and do not require advanced functionalities like recursion (thanks to the flattening of the AST), they remain notoriously difficult to read and maintain.It would be interesting to explore third-party static analytic tools, and try to replace gradually my home-made patterns. The tools I am currently aware of are:
I have no experience in any of them, though.
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