Hai Huang

80 papers B 4C 1Misc 2Journal 15Unranked 58
YearRankTypeTitle / Venue / Authors
2026 J jnl
Neurocomputing
Junchang Zhang, Yucai Shi, Hong Chen, Qing Wang, Hai Huang
2026 J jnl
Expert Syst. Appl.
Junchang Zhang, Yucai Shi, Hong Chen, Qing Wang, Hai Huang
2025 J jnl
IEEE Internet Things J.
Junchang Zhang, Guang Wang, Hong Chen, Hai Huang, Yucai Shi, Qing Wang
2025 conf
WASA (3)
Wei Yang, Hai Huang, Yuan Wang, Lei Ning, Xiaojun Jing
2025 B conf
IJCNN
Zhou Fang, Hai Huang, Hong Chen, Shan Yue, Zhenqi Tang
2025 conf
CVM (2)
Shan Yue, Hai Huang, Zhenqi Tang, Yutong Zheng, Zhou Fang
2024 conf
PRCV (13)
Shilin Wang, Hai Huang, Yueyan Zhu, Zhenqi Tang
2024 conf
ICIC (7)
Yueyan Zhu, Hai Huang, Shan Yue, Shu Zhang, Aoran Chen
2024 B conf
IJCNN
Junsheng Xue, Hai Huang, Zhong Zhou, Shibiao Xu, Aoran Chen
2024 J jnl
Sensors
Aoran Chen, Hai Huang, Yueyan Zhu, Junsheng Xue
2023 J jnl
IET Signal Process.
Huayan Yu, Hai Huang, Yueyan Zhu, Aoran Chen
2021 J jnl
Int. J. Circuit Theory Appl.
Hai Huang, Chang Liu, Lei Tian, Junsheng Mu, Xiaojun Jing
2021 J jnl
KSII Trans. Internet Inf. Syst.
Wei Yang, Xiaojun Jing, Hai Huang, Chunsheng Zhu, Qiaojie Jiang, Dongliang Xie
2020 conf
BMSB
Xueshu Wang, Xiaojun Jing, Hai Huang, Yuanhao Cui, Michel Kadoch, Mohamed Cheriet
2020 J jnl
IET Commun.
Junsheng Mu, Dongliang Xie, Hai Huang, Xiaojun Jing
2020 J jnl
IEEE Access
Shaokang Wang, Yihao Chen, Hongjun Ming, Hai Huang, Lingxian Mi, Zengyi Shi
2020 J jnl
IEEE Access
Wei Yang, Hai Huang, Xiaojun Jing, Zhannan Li, Chunsheng Zhu
2018 J jnl
IEEE Access
Wei Yang, Xiaojun Jing, Hai Huang
2018 J jnl
KSII Trans. Internet Inf. Syst.
Wei Yang, Xiaojun Jing, Hai Huang
2018 J jnl
IEEE Commun. Lett.
Junsheng Mu, Xiaojun Jing, Hai Huang, Ning Gao
2017 conf
ICSPCS
Shaoka Sun, Hai Huang, Xiaojun Jing, Jincai Du
2017 C conf
ICCC
Yuanquan Hong, Xiaojun Jing, Hui Gao, Songlin Sun, Hai Huang, Ning Gao, Jianxiao Xie, Bohan Li
2017 conf
CrownCom
Wei Yang, Xiaojun Jing, Hai Huang
2017 J jnl
IET Biom.
Xin Ma, Xiaojun Jing, Hai Huang, Yuanhao Cui, Junsheng Mu
2017 conf
mmNets
Yuanquan Hong, Xiaojun Jing, Hui Gao, Hai Huang, Ning Gao, Jiaoxiao Xie
2017 J jnl
IEEE Commun. Lett.
Ning Gao, Xiaojun Jing, Hai Huang, Junsheng Mu
2016 conf
ISCIT
Li Zhang, Hai Huang, Xiaojun Jing
2016 conf
UEMCON
Junsheng Mu, Xiaojun Jing, Hai Huang, Ning Gao
2016 conf
ISCIT
Jincai Du, Hai Huang, Xiaojun Jing, Xuqi Chen
2016 conf
ISCIT
Shaoka Sun, Hai Huang, Xiaojun Jing
2016 conf
ISCIT
Baobin Liang, Hai Huang, Xiaojun Jing
2016 conf
VTC Spring
Zifeng Lian, Xiaojun Jing, Songlin Sun, Hai Huang
2016 conf
ISCIT
Yan Bi, Xiaojun Jing, Songlin Sun, Hai Huang
2016 conf
ISCIT
Jingrui Zhang, Li Zhang, Hai Huang, Xiaojun Jing
2016 conf
ISCIT
Yue Li, Ying Chen, Hai Huang, Xiaojun Jing
2016 conf
ISCIT
Xin Chen, Fukang Hou, Hai Huang, Xiaojun Jing
2016 conf
ISCIT
Fukang Hou, Xin Chen, Hai Huang, Xiaojun Jing
2015 conf
ICSON
Zhengmao Ye, Xiaojun Jing, Hai Huang
2015 conf
ICSON
Na Chen, Xiaojun Jing, Hai Huang
2015 conf
ICSON
Yuewen Li, Songlin Sun, Na Chen, Hai Huang
2015 conf
ICSON
Ying Chen, Songlin Sun, Xinzhou Cheng, Hai Huang
2015 conf
ICSON
Zhen Dai, Xiaojun Jing, Hai Huang
2015 conf
ICSON
Li Wang, Xiaojun Jing, Hai Huang
2015 conf
ISCIT
Na Chen, Xiaojun Jing, Songlin Sun, Fengye Zhang, Hai Huang, Zheng Zhou
2015 conf
ICSON
Ying Chen, Xiaojun Jing, Hai Huang
2015 conf
ICSON
Tiantian Ran, Songlin Sun, Hai Huang
2015 conf
ISCIT
Yuanhao Cui, Xiaojun Jing, Songlin Sun, Xiaohan Wang, Dongmei Cheng, Hai Huang
2015 conf
ICSON
Danyang Wang, Xiaojun Jing, Hai Huang
2015 conf
ICSON
Zhen Wei, Xiaojun Jing, Hai Huang
2015 conf
ISCIT
Jianlan Liu, Xiaojun Jing, Songlin Sun, Xiaohan Wang, Dongmei Cheng, Hai Huang
2015 conf
ICSON
Fukang Hou, Xiaojun Jing, Hai Huang
2015 conf
ICSON
Fengye Zhang, Songlin Sun, Na Chen, Hai Huang
2015 conf
ICSON
Tingting Huang, Songlin Sun, Wei Liu, Hai Huang
2015 conf
ICSON
Chang Li, Songlin Sun, Wei Liu, Hai Huang
2015 conf
ICSON
Tianyi Feng, Songlin Sun, Hai Huang
2014 conf
ISCIT
Na Chen, Lusha Wang, Songlin Sun, Xiaojun Jing, Hai Huang
2014 conf
CCIS
Xue Liu, Xiaojun Jing, Songlin Sun, Hai Huang
2014 conf
ISCIT
Wanbin Qi, Xiaojun Jing, Songlin Sun, Hai Huang
2014 conf
ISCIT
Guihong Li, Xiaojun Jing, Songlin Sun, Hai Huang
2014 conf
ISCIT
Song Xue, Xiaojun Jing, Songlin Sun, Hai Huang
2014 B conf
AVSS
Wang Bo, Li Teng, Songlin Sun, Xiaojun Jing, Hai Huang
2014 B conf
PIMRC
Yuhan Zheng, Fei Qi, Xinzhou Cheng, Xiaojun Jing, Hai Huang
2014 conf
ISCIT
Jiaxu Yu, Sonling Sun, Xiaojun Jing, Hai Huang
2014 conf
ISCIT
Xi Quan, Xiaojun Jing, Songlin Sun, Hai Huang, Lusha Wang
2013 conf
VTC Spring
Xiaolong Guo, Songlin Sun, Xiaojun Jing, Hai Huang
2013 conf
ICCVE
Xingxing Li, Xiaojun Jing, Songlin Sun, Hai Huang, Na Chen, Yueming Lu
2013 Misc conf
GrC
Lei Tao, Xiaojun Jing, Songlin Sun, Hai Huang, Na Chen, Yueming Lu
2013 conf
ISCTCS
Yaoyao Guo, Songlin Sun, Xiaojun Jing, Hai Huang, Yueming Lu, Na Chen
2013 Misc conf
GrC
Lin Cai, Xiaojun Jing, Songlin Sun, Hai Huang, Na Chen, Yueming Lu
2013 conf
ISSPIT
Yi Sun, Xiaojun Jing, Songlin Sun, Hai Huang
2012 conf
IC-NIDC
Ping Zheng, Xiaojun Jing, Songlin Sun, Hai Huang
2011 conf
CCIS
Xiao Xia, Songlin Sun, Xiaojun Jing, Hai Huang
2010 conf
Edutainment
Hai Huang, Lei Tian, Wei Wu, Songlin Sun, Xiaojun Jing
2007 conf
Edutainment
Jing Zhang, Hai Huang
2007 conf
ICANNGA (1)
Lei Tian, Liyan Han, Hai Huang
2006 conf
ICNC (2)
Tian Lei, Liu Lieli, Liyan Han, Hai Huang
2006 conf
Edutainment
Hai Huang, Wei Wu, Xin Tang, Zhong Zhou
2006 conf
ICAT
Jing Zhang, Hai Huang
2005 conf
ICNC (3)
Wei Wu, Hai Huang, Zhong Zhou, Zhongshu Liu
2004 conf
GCC
Hai Huang, Shaofeng Wang, Yan Zhang, Wei Wu
redb/extractors/decompiler/bninja/decompiler.py
← Index redb/extractors/decompiler/bninja/decompiler.py python
import os
import time
import json

from .analysis.medium_level import MediumLevelAnalysis

# disable the plugins set by user for binary ninja
os.environ["BN_DISABLE_USER_PLUGINS"] = "True"
import traceback

# Binary Ninja imports (conditional)
try:
    import binaryninja
    from binaryninja import mainthread, Symbol
    from binaryninja.enums import SymbolType
    BINARYNINJA_AVAILABLE = True
except Exception:
    BINARYNINJA_AVAILABLE = False
    binaryninja = None
    mainthread = None
    Symbol = None
    SymbolType = None

# Support both package and standalone imports
try:
    # Package import (when imported from redb)
    from .utils.hashes import calculate_sha256, calculate_tlsh
    from .utils.license import set_license
    from .utils.logging import setup_default_logger
    from .analysis.strings import StringAnalysis

    # Only import modules that depend on Binary Ninja when available
    if BINARYNINJA_AVAILABLE:
        from .analysis.cfg import CFGAnalysis
        from .analysis.disassembly import DisassemblyAnalysis
        from .analysis.low_level import LowLevelAnalysis
        from .arch.creator import ArchitectureCreator
        from .custom_options import register_custom_analysis_options
        from .function_type import FunctionTypeAnalysis, FunctionType
        from .analysis.scores import ObfuscationScores

except ImportError:
    # Fallback to absolute imports (for multiprocessing spawned processes)
    from redb.extractors.decompiler.bninja.utils.hashes import calculate_sha256, calculate_tlsh
    from redb.extractors.decompiler.bninja.utils.license import set_license
    from redb.extractors.decompiler.bninja.utils.logging import setup_default_logger

    # Only import modules that depend on Binary Ninja when available
    if BINARYNINJA_AVAILABLE:
        from redb.extractors.decompiler.bninja.analysis.cfg import CFGAnalysis
        from redb.extractors.decompiler.bninja.analysis.disassembly import DisassemblyAnalysis
        from redb.extractors.decompiler.bninja.analysis.low_level import LowLevelAnalysis
        from redb.extractors.decompiler.bninja.arch.creator import ArchitectureCreator
        from redb.extractors.decompiler.bninja.custom_options import register_custom_analysis_options
        from redb.extractors.decompiler.bninja.function_type import FunctionTypeAnalysis


class BinaryNinjaDecompiler:
    """A Binary Ninja-based decompiler that replicates the functionality of GhidraDecompilerScript.
    This class extracts decompiled code, disassembly with multiple normalization levels,
    and control flow graph information from binary files.
    """

    MIN_FUNCTION_SIZE = 10  # instructions
    MIN_BLOCK_SIZE = 4  # instructions
    INVALID_STACK_SIZE = -1

    def __init__(
        self,
        filepath,
        timeout,
        log=None,
        exporters=None,
        index_prefix=None,
        filetype=None,
        goresym=None,
        decompile_modules=None,
    ):
        """Initialize the Binary Ninja decompiler.

        Args:
            filepath: Path to the binary file to analyze
            log: Logger object (optional)
            timeout: Maximum time in seconds for analysis (default: 1200)
            exporters: List of exporters for the results (optional)
            index_prefix: Prefix for elastic index (optional)
            filetype: Type of the file (optional)

        """
        self.filepath = filepath
        self.log = log if log else setup_default_logger("BninjaDecompiler")
        self.BNINJA_TIMEOUT = timeout
        self.bv = None
        self.analysis_results = None
        self.errors = []
        self.exporters = exporters
        self.index_prefix = index_prefix
        self.filetype = filetype
        self.goresym = goresym
        self.decompile_modules = decompile_modules or {"all"}

        set_license(binaryninja)

        # Map to track instruction categorization
        mainthread.set_worker_thread_count(3)
        register_custom_analysis_options(binaryninja)

    def log_error(
        self, message, function_name, address, exception=None, error_location="unknown"
    ):
        """Log an error during processing."""
        error_msg = f"Error in function {function_name} at {address}: {message}"
        if exception:
            error_msg += f" - {str(exception)}"

        self.log.error(error_msg)

        # Add to errors list
        error = {
            "function_name": function_name,
            "function_address": str(address),
            "error_location": error_location,
            "error_message": message,
            "error_details": str(exception) if exception else "",
            "error_type": type(exception).__name__ if exception else "Unknown",
            "timestamp": int(time.time() * 1000),
        }
        self.errors.append(error)

    def __enter__(self):
        """Context manager entry point."""
        self.log.info(f"Opening binary file: {self.filepath}")
        binaryninja.BinaryViewType.add_binaryview_initial_analysis_completion_event(
            self.on_analysis_complete
        )

        #self.bv = binaryninja.load(self.filepath, update_analysis=False)
        self.bv = binaryninja.load(self.filepath, update_analysis=True)
        if self.bv is None:
            raise ValueError(f"Failed to open file: {self.filepath}")

        self.log.info("Waiting for analysis to complete...")

        self.log.debug(f"Binja analysis complete: {len(list(self.bv.functions))} functions")

        # set the architecture
        # todo: personalize this for other architectures
        self.arch = ArchitectureCreator("x86").get()

        # apply goresym
        if self.goresym is not None:
            self.__apply_goresym()
        return self

    def on_analysis_complete(self, bv):
        # Request an additional update after analysis is complete to ensure IL generation
        self.bv = bv
        self.bv.update_analysis()

        return

    def __apply_goresym(self):
        file = self.goresym
        data = None
        try:
            data = json.loads(open(file, 'r').read())
        except Exception as e:
            self.log_error(
                "Failed to open file from goresym: ",
                file,
                e,
                "analyze_binary",
            )

        if data is None:
            return

        self.bv.begin_undo_actions()
        if data.get('UserFunctions') is not None:
            user_functions = data['UserFunctions']
            for func in user_functions:
                try:
                    start = int(func['Start'])
                    name = func['FullName']
                    if self.bv.get_function_at(start) is None:
                        self.bv.create_user_function(start)

                    sym = Symbol(SymbolType.FunctionSymbol, start, name, name, name)
                    self.bv.define_user_symbol(sym)
                except Exception as e:
                    self.log.warning(f"Failed to apply GoReSym symbol for UserFunction {func.get('FullName', 'unknown')} at {func.get('Start', 'unknown')}: {e}")

        if data.get('StdFunctions') is not None:
            standard_functions = data['StdFunctions']
            for func in standard_functions:
                try:
                    start = int(func['Start'])
                    name = func['FullName']
                    if self.bv.get_function_at(start) is None:
                        self.bv.create_user_function(start)

                    sym = Symbol(SymbolType.FunctionSymbol, start, name, name, name)
                    self.bv.define_user_symbol(sym)
                except Exception as e:
                    self.log.warning(f"Failed to apply GoReSym symbol for StdFunction {func.get('FullName', 'unknown')} at {func.get('Start', 'unknown')}: {e}")

        self.bv.commit_undo_actions()
        return

    def __exit__(self, exc_type, exc_val, exc_tb):
        """Context manager exit point - clean up resources."""
        if self.bv:
            # Make sure to cancel any pending analysis
            # (if we have no pending analysis, binary ninja will log an error)

            # todo(@nicolo): investigate
            # if hasattr(self.bv, "abort_analysis"):
            #    self.bv.abort_analysis()
            self.bv.file.close()

        self.log.info("Cleanup completed successfully")
        return

    def tag(self):
        """Return the tag for this extractor."""
        return "DECOMPILED"

    def _module_selected(self, module_name):
        """Check if a decompiler sub-module is selected."""
        return "all" in self.decompile_modules or module_name in self.decompile_modules

    def analyze_binary(self):
        """Run Binary Ninja analysis and return results.

        Respects self.decompile_modules to selectively run/skip sub-modules:
        - strings: independent, skipped if not selected
        - decompilation: leaf module, skipped if not selected
        - disassembly: always runs (backbone — provides hash linkage for all others)
        - llil: leaf module, skipped if not selected
        - cfg: leaf module, skipped if not selected

        Only selected modules' results are appended to the results dict for DB insertion.
        Disassembly is always computed for linkage but only inserted when selected.
        """
        try:
            results = {
                "decompiled": [],
                "disassembled": [],
                "cfg": [],
                "llil": [],
                "errors": [],
                "strings": [],
                "mlil": []
            }

            run_all = "all" in self.decompile_modules
            run_strings = run_all or "strings" in self.decompile_modules
            run_decompilation = run_all or "decompilation" in self.decompile_modules
            run_disassembly = run_all or "disassembly" in self.decompile_modules
            run_llil = run_all or "llil" in self.decompile_modules
            run_cfg = run_all or "cfg" in self.decompile_modules

            # Determine if we need the per-function loop at all
            need_per_function = run_decompilation or run_disassembly or run_llil or run_cfg

            #functions_list = list(filter(is_not_ext_lib_function, self.bv.functions))
            functions_list = list(filter(is_lib_or_thunk, self.bv.functions))
            functions_list = list(filter(self.is_too_few_blocks, functions_list))

            # Strings extraction — independent of per-function analysis
            if run_strings:
                results["strings"] = StringAnalysis(self.bv, functions_list).analyze()

            if not need_per_function:
                return results

            for function in functions_list:
                try:
                    # Decompilation (HLIL) — leaf module, skip if not selected
                    hlil_json = self.extract_hlil(function) if run_decompilation else None

                    # Disassembly — always compute (provides hash linkage for others)
                    disass_json = self.extract_disasm(function)

                    # CFG — leaf module, skip if not selected
                    cfg_json = self.extract_cfg(function) if run_cfg else None

                    # LLIL — leaf module, skip if not selected
                    lowlevel_json = self.extract_lowlevel(function) if run_llil else None

                    # we run mlil only if we have cfg
                    mlil_json = self.extract_mediumlevel(function) if run_llil else None

                    # Calculate fuzzy hashes for disassembly using utils.hashes
                    if disass_json:
                        disass_no_addr = disass_json.get("disassembled_function_no_addresses", "")
                        disass_json["tlsh_disassembly"] = calculate_tlsh(disass_no_addr)

                    if hlil_json and disass_json:
                        hlil_json["disassembled_function_hash"] = disass_json[
                            "disassembled_function_hash"
                        ]
                        disass_json["decompiled_function_hash"] = hlil_json[
                            "decompiled_function_hash"
                        ]

                        results["decompiled"].append(hlil_json)
                        if run_disassembly:
                            results["disassembled"].append(disass_json)

                    elif hlil_json:
                        hlil_json["disassembled_function_hash"] = None
                        results["decompiled"].append(hlil_json)
                    elif disass_json:
                        disass_json["decompiled_function_hash"] = None
                        if run_disassembly:
                            results["disassembled"].append(disass_json)

                    # Add bi-directional linkage between LLIL and disassembly with fuzzy hashes
                    # LLIL fuzzy hashes (tlsh_llil) are already calculated in lowlevel_json
                    if lowlevel_json and disass_json:
                        # Add disassembly info to LLIL
                        lowlevel_json["disassembled_function_hash"] = disass_json["disassembled_function_hash"]
                        lowlevel_json["tlsh_disassembly"] = disass_json.get("tlsh_disassembly")

                        # Add LLIL fuzzy hashes to disassembly for easy export access
                        disass_json["tlsh_llil"] = lowlevel_json.get("tlsh_llil")
                        disass_json["minhash"] = lowlevel_json.get("minhash")

                    elif lowlevel_json:
                        lowlevel_json["disassembled_function_hash"] = None
                        lowlevel_json["tlsh_disassembly"] = None
                    elif disass_json:
                        # No LLIL available for this disassembly
                        disass_json["tlsh_llil"] = None
                        disass_json["minhash"] = None

                    if lowlevel_json:
                        results["llil"].append(lowlevel_json)
                        results["mlil"].append(mlil_json)

                    # Add CFG linkage with disassembled_function_hash
                    # Also add cyclomatic_complexity to disass_json for similarity metrics export
                    if cfg_json and disass_json:
                        cfg_json["disassembled_function_hash"] = disass_json["disassembled_function_hash"]
                        disass_json["cyclomatic_complexity"] = cfg_json.get("cyclomatic_complexity")
                        results["cfg"].append(cfg_json)
                    elif cfg_json:
                        cfg_json["disassembled_function_hash"] = None
                        results["cfg"].append(cfg_json)

                    # Ensure cyclomatic_complexity is set even if no cfg_json
                    if disass_json and "cyclomatic_complexity" not in disass_json:
                        disass_json["cyclomatic_complexity"] = None

                except Exception as e:
                    self.log_error(
                        "Failed to process function: ",
                        function.name,
                        function.start,
                        e,
                        "analyze_binary",
                    )

            return results

        except Exception as e:
            self.log.error(f"Error in binary analysis: {str(e)}")
            return None

    def extract_mediumlevel(self, function):
        middle_level = MediumLevelAnalysis(function, self.bv, self.log)
        middle_level_result, errors = middle_level.analyze()

        for error in errors:
            self.errors.append(error)

        return middle_level_result


    def extract_lowlevel(self, function):
        low_level = LowLevelAnalysis(function, self.bv, self.log)
        disassembly_json, errors = low_level.analyze()

        for error in errors:
            self.errors.append(error)

        return disassembly_json

    def extract_cfg(self, function):
        try:
            llil = function.llil if hasattr(function, 'llil') else None
            cfg = CFGAnalysis(function, llil_function=llil).extract_function_cfg()
            return cfg
        except Exception as e:
            self.log_error(
                "Fatal error in CFG extraction",
                function.name,
                function.start,
                e,
                "extract_disassembly",
            )
            return None

    def extract_disasm(self, function):
        try:
            disass_analysis = DisassemblyAnalysis(
                self.arch, function, self.bv, self.log
            )

            # Create disassembly JSON
            disassembly_json, errors = disass_analysis.get_json()

            for error in errors:
                self.errors.append(error)

            return disassembly_json

        except Exception as e:
            self.log_error(
                "Fatal error in disassembly extraction",
                function.name,
                function.start,
                e,
                "extract_disassembly",
            )
            return None

    def extract_hlil(self, function):
        """Extract HLIL from a function."""
        try:
            # Access function.hlil directly - this will either return the HLIL or raise an exception
            # Removed hlil_if_available check as it was causing race conditions
            if function.hlil is None:
                return None

            if len(function.hlil.basic_blocks) == 0:
                return None

            # if function has one basic block, then compare the len of the instructions against minimum of our functions
            if len(function.hlil.basic_blocks) == 1:
                block = function.basic_blocks[0]
                if len(list(block.disassembly_text)) < self.MIN_FUNCTION_SIZE:
                    return None

            # Get decompiled code
            function_prototype = str(function)
            decompiled_code = str(function.hlil)
            if not decompiled_code or decompiled_code.strip() == "":
                self.log.warning(f"Empty decompilation result for {function.name}")
                return None

            callers = []
            for caller_site in function.caller_sites:
                if caller_site.hlil:
                    callers.append(str(caller_site.hlil))

            calls = []
            for call in function.call_sites:
                if call.hlil:
                    calls.append(str(call.hlil))

            analysis_score = ObfuscationScores(function.hlil)
            flattened_score = analysis_score.flattened_score()
            mba_score = analysis_score.MBA_score()

            if decompiled_code:
                # Create json object
                function_json = {
                    "decompiled_function_hash": calculate_sha256(decompiled_code),
                    "decompiled_function": decompiled_code,
                    "decompiled_function_name": function.name,
                    "decompiled_function_prototype": function_prototype,
                    "decompiled_function_address": function.start,
                    "function_type": FunctionTypeAnalysis(function)
                    .get_function_type()
                    .name,
                    "functions_caller": list(callers),
                    "functions_call": list(calls),
                    "flattened_score": flattened_score,
                    "mba_score": mba_score,
                }
                return function_json
            else:
                return None

        except Exception as e:
            self.log.warning(
                f"Failed to get HLIL for {function.name} at {function.start}: {str(e)}"
            )

        return None

    def extract(self) -> bool:
        """Extract and process all analysis results.

        Returns:
            bool: True if extraction was successful, False otherwise

        """
        self.log.info(f"Starting binary analysis on {self.filepath}")
        try:
            results = self.analyze_binary()
            if not results:
                self.log.error("Analysis failed to produce results")
                return False

            self.analysis_results = results
            self.log.info(
                f"Successfully analyzed binary: {len(results['decompiled'])} decompiled functions, "
                f"{len(results['disassembled'])} disassembled functions, "
                #f"{len(results['cfg'])} basic blocks, "
                f"{len(results['errors'])} errors"
            )
            return True

        except Exception as e:
            self.log.error(f"Error in extraction: {str(e)}")
            return False

    def is_too_few_blocks(self, function):
        if function is None:
            return False

        if function.basic_blocks is None:
            return False

        # when binary ninja does not wait for the analysis, it creates function stubs where basic blocks array
        # is not populated yet. Therefore, we disable this heuristic.
        #if len(function.basic_blocks) == 0:
        #    return False

        # if function has one basic block, then compare the len of the instructions against minimum of our functions
        if len(function.basic_blocks) == 1:
            block = function.basic_blocks[0]
            if len(list(block.disassembly_text)) < self.MIN_FUNCTION_SIZE:
                return False

        return True

def is_lib_or_thunk(function):
    function_type = FunctionTypeAnalysis(function).get_function_type()
    return not (function_type == FunctionType.THUNK or function_type == FunctionType.EXTERNAL or function_type == FunctionType.LIBRARY)