Parsing techniques
A system and method for parsing. A method includes querying a language model based on a code sample in order to obtain a second file, wherein outputs of the language model are refined based on a comparison between a set of expected results output by the language model for at least one set of example code and at least one first parser output obtained by inputting at least one first file to a parser; providing the second file to the parser in order to obtain a second parser output; and identifying at least one endpoint based on the second parser output.
1 . A method for endpoint identification:
obtaining outputs, at each iteration of a one or more iterations, from a language model based on a set of example code for a respective iteration in the one or more iterations, where the outputs include a set of expected results for the respective iteration and a first file for the respective iteration;
obtaining, at each iteration of the one or more iterations, a parser output for the respective iteration based on the first file for the respective iteration;
comparing, at each iteration of the one or more iterations, the set of the expected results for the respective iteration with the parser output of the respective iteration;
determining the outputs of the language model are refined based on the comparison;
querying the language model based on a code sample in order to obtain a second file;
obtaining a second parser output, via the parser, based on the second file; and
identifying at least one endpoint based on the second parser output.
2 . The method of claim 1 , further comprising:
remediating a vulnerability based on the identified at least one endpoint.
3 . The method of claim 2 , wherein remediating the vulnerability further comprises altering a code deployment for the identified at least one endpoint.
4 . The method of claim 1 , further comprising:
correlating each endpoint in the at least one endpoint to a corresponding portion of the code sample, wherein the each endpoint in the at least one endpoint is identified based on a location of the corresponding portion of the code sample.
5 . The method of claim 1 , further comprising:
adding a postprocessing code to the second file, wherein the postprocessing code includes instructions for semantically analyzing a parse tree output, wherein the second file with the added postprocessing code is provided to the parser in order to obtain the second parser output.
6 . The method of claim 1 , wherein the parser is configured to parse streams of characters using grammar-based inputs.
7 . The method of claim 1 , further comprising:
identifying expected endpoints in the set of expected results for the respective iteration;
identifying parser endpoints in the parser output for the respective iteration; and
matching expected endpoints in the set of expected results for the respective iteration with the parser endpoints in the parser output for the respective iteration.
8 . The method of claim 7 , further comprising:
determining the outputs of the language model are refined based on the matching being above a threshold number.
9 . The method of claim 1 , wherein the one or more iterations comprises at least two iterations.
10 . A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process, the process comprising:
obtaining outputs, at each iteration of a one or more iterations, from a language model based on a set of example code for a respective iteration in the one or more iterations, where the outputs include a set of expected results for the respective iteration and a first file for the respective iteration;
obtaining, at each iteration of the one or more iterations, a parser output for the respective iteration based on the first file for the respective iteration;
comparing, at each iteration of the one or more iterations, the set of the expected results for the respective iteration with the parser output of the respective iteration;
determining the outputs of the language model are refined based on the comparison;
querying the language model based on a code sample in order to obtain a second file;
obtaining a second parser output, via the parser, based on the second file; and
identifying at least one endpoint based on the second parser output.
11 . A system for endpoint identification, comprising:
a processing circuitry; and
a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:
obtain outputs, at each iteration of a one or more iterations, from a language model based on a set of example code for a respective iteration in the one or more iterations, where the outputs include a set of expected results for the respective iteration and a first file for the respective iteration;
obtain, at each iteration of the one or more iterations, a parser output for the respective iteration based on the first file for the respective iteration;
compare, at each iteration of the one or more iterations, the set of the expected results for the respective iteration with the parser output of the respective iteration;
determine the outputs of the language model are refined based on the comparison;
query the language model based on a code sample in order to obtain a second file;
obtain a second parser output, via the parser, based on the second file; and
identify at least one endpoint based on the second parser output.
12 . The system of claim 11 , wherein the system is further configured to:
remediate a vulnerability based on the identified at least one endpoint.
13 . The system of claim 12 , wherein remediating the vulnerability further comprises altering a code deployment for the identified at least one endpoint.
14 . The system of claim 11 , wherein the system is further configured to:
correlate each endpoint in the at least one endpoint to a corresponding portion of the code sample, wherein the each endpoint in the at least one endpoint is identified based on a location of the corresponding portion of the code sample.
15 . The system of claim 11 , wherein the system is further configured to:
add a postprocessing code to the second file, wherein the postprocessing code includes instructions for semantically analyzing a parse tree output, wherein the second file with the added postprocessing code is provided to the parser in order to obtain the second parser output.
16 . The system of claim 11 , wherein the parser is configured to parse streams of characters using grammar-based inputs.
17 . The system of claim 11 , wherein the system is further configured to:
identify expected endpoints in the set of expected results for the respective iteration;
identify parser endpoints in the parser output for the respective iteration; and
match expected endpoints in the set of expected results for the respective iteration with the parser endpoints in the parser output for the respective iteration.
18 . The system of claim 17 , wherein the system is further configured to:
determine the outputs of the language model are refined based on the match being above a threshold number.
19 . The system of claim 11 , wherein the one or more iterations comprises at least two iterations.