Wei Xu

88 papers C 1Journal 85Unranked 2
YearRankTypeTitle / Venue / Authors
2025 J jnl
CCF Trans. Pervasive Comput. Interact.
Wei Xu, Zhu Wang, Yifan Guo, Zhihui Ren, Yandi Xu, Bin Guo, Zhiwen Yu, Xingshe Zhou
2025 J jnl
IEEE Trans. Mob. Comput.
Yifan Guo, Zhu Wang, Zhihui Ren, Wei Xu, Yangqian Lei, Qian Qin, Zhuo Sun, Chao Chen, Bin Guo, Zhiwen Yu, Daqing Zhang
2025 J jnl
IEEE Commun. Mag.
Zhihui Ren, Zhu Wang, Wei Xu, Yifan Guo, Yandi Xu, Bin Guo, Zhiwen Yu
2023 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Wenting Zhang, Wei Xu, Lizhi Niu, Yaning Tang
2023 J jnl
Int. J. Bifurc. Chaos
Yuexin Wang, Zhongkui Sun, Shutong Liu, Yining Zhou, Wei Xu
2023 J jnl
Appl. Math. Lett.
Jiankang Liu, Wei Wei, Jinbin Wang, Wei Xu
2023 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Lizhi Niu, Wei Xu, Tongtong Sun, Wenting Zhang, Yisha Lu
2022 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Chen Jin, Zhongkui Sun, Wei Xu
2022 J jnl
Int. J. Bifurc. Chaos
Chen Jin, Zhongkui Sun, Qin Guo, Wei Xu
2022 J jnl
Int. J. Bifurc. Chaos
Shutong Liu, Zhongkui Sun, Nannan Zhao, Wei Xu
2022 J jnl
Int. J. Bifurc. Chaos
Kai Lu, Wenjing Xu, Yongchang Wei, Wei Xu
2022 J jnl
Int. J. Bifurc. Chaos
Minjuan Yuan, Liang Wang, Yiyu Jiao, Wei Xu
2021 J jnl
Appl. Math. Lett.
Jiankang Liu, Wei Xu
2021 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Yuanyuan Liu, Zhongkui Sun, Xiaoli Yang, Wei Xu
2021 J jnl
Int. J. Bifurc. Chaos
Yong-Ge Yang, Yahui Sun, Wei Xu
2021 J jnl
Int. J. Bifurc. Chaos
Wenting Zhang, Wei Xu, Qin Guo, Hongxia Zhang
2021 J jnl
Appl. Math. Comput.
Yuanyuan Liu, Zhongkui Sun, Xiaoli Yang, Wei Xu
2021 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Shutong Liu, Zhongkui Sun, Nannan Zhao, Wei Xu
2021 J jnl
Int. J. Bifurc. Chaos
Yahui Sun, Yong-Ge Yang, Ling Hong, Wei Xu
2021 J jnl
Int. J. Bifurc. Chaos
Meng Su, Wei Xu, Ying Zhang
2021 J jnl
Int. J. Bifurc. Chaos
Liang Wang, Bochen Wang, Jiahui Peng, Xiaole Yue, Wei Xu
2021 J jnl
J. Frankl. Inst.
Hongxia Zhang, Xinzhi Liu, Wei Xu
2020 J jnl
Appl. Math. Lett.
Wenjing Xu, Wei Xu
2020 J jnl
Appl. Math. Comput.
Zhicong Ren, Wei Xu
2020 J jnl
Int. J. Bifurc. Chaos
Qin Guo, Xige Yang, Jiankang Liu, Wei Xu
2020 J jnl
Int. J. Bifurc. Chaos
Qun Han, Wei Xu, Huibing Hao, Xiaole Yue
2020 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Zhicong Ren, Wei Xu, Shuo Zhang
2019 J jnl
Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.
Wei Xu, Zhiwen Yu, Zhu Wang, Bin Guo, Qi Han
2019 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Qin Guo, Zhongkui Sun, Wei Xu
2019 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Hongxia Zhang, Wei Xu, Youming Lei, Yan Qiao
2019 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Rui Xiao, Zhongkui Sun, Xiaoli Yang, Wei Xu
2019 J jnl
Int. J. Bifurc. Chaos
Xiao-Le Yue, Yong Xu, Wei Xu, Jian-Qiao Sun
2019 J jnl
Appl. Math. Comput.
Zhicong Ren, Wei Xu, Yan Qiao
2019 J jnl
Int. J. Bifurc. Chaos
Deli Wang, Wei Xu, Zhicong Ren, Haiqing Pei
2019 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Deli Wang, Wei Xu, Jianwen Xu, Xudong Gu, Guidong Yang
2019 J jnl
Symmetry
Li Liu, Wei Xu, Xiaole Yue, Dongmei Huang
2019 J jnl
Complex.
Yong-Ge Yang, Yahui Sun, Wei Xu
2019 J jnl
Appl. Math. Lett.
Wenjing Xu, Wei Xu, Shuo Zhang
2019 J jnl
Complex.
Qin Guo, Zhongkui Sun, Ying Zhang, Wei Xu
2018 J jnl
Int. J. Bifurc. Chaos
Yong-Ge Yang, Wei Xu, YangQuan Chen, Bingchang Zhou
2018 J jnl
Int. J. Bifurc. Chaos
Jintian Zhang, Zhongkui Sun, Xiaoli Yang, Wei Xu
2018 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Jiaojiao Sun, Wei Xu, Zifei Lin
2018 J jnl
Int. J. Bifurc. Chaos
Meng Su, Wei Xu, Guidong Yang
2018 J jnl
Int. J. Bifurc. Chaos
Qin Guo, Zhongkui Sun, Wei Xu
2017 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Dongmei Huang, Wei Xu
2017 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Yong-Ge Yang, Wei Xu, Yahui Sun, Yanwen Xiao
2017 J jnl
Entropy
Zifei Lin, Wei Xu, Jiaorui Li, Wantao Jia, Shuang Li
2016 J jnl
Int. J. Bifurc. Chaos
Jin Fu, Zhongkui Sun, Yuzhu Xiao, Wei Xu
2016 J jnl
Math. Comput. Simul.
Xiying Wang, Xinzhi Liu, Wei-Chau Xie, Wei Xu, Yong Xu
2016 J jnl
Int. J. Bifurc. Chaos
Tao Liu, Wei Xu, Yong Xu, Qun Han
2016 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Guidong Yang, Wei Xu, Wantao Jia, Meijuan He
2016 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Xiangrong Zhao, Wei Xu, Yong-Ge Yang, Xiying Wang
2015 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Meijuan He, Wei Xu, Zhongkui Sun, Lin Du
2015 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Qun Han, Wei Xu, Xiaole Yue, Ying Zhang
2015 J jnl
IEEE Trans. Image Process.
Chengcai Leng, Wei Xu, Irene Cheng, Anup Basu
2015 C conf
ISM
Chengcai Leng, Wei Xu, Irene Cheng, Zhihui Xiong, Anup Basu
2015 J jnl
J. Frankl. Inst.
Xiying Wang, Xinzhi Liu, Wei Xu, Kexue Zhang
2014 J jnl
J. Appl. Math.
Haiwu Rong, Xiangdong Wang, Qizhi Luo, Wei Xu, Tong Fang
2014 J jnl
Int. J. Bifurc. Chaos
Qun Han, Wei Xu, Xiaole Yue
2014 J jnl
Appl. Math. Comput.
Chao Li, Wei Xu, Liang Wang, Zhenpei Wang
2014 J jnl
Int. J. Bifurc. Chaos
Chao Li, Wei Xu, Xiaole Yue
2013 J jnl
J. Appl. Math.
Xiuchun Li, Jianhua Gu, Wei Xu
2013 J jnl
Commun. Nonlinear Sci. Numer. Simul.
Xiaole Yue, Wei Xu, Liang Wang
2011 conf
FSKD
Chengcai Leng, Wei Xu, Min Li, Nathaniel Rossol, Li He, Di Liu
2011 conf
FSKD
Ying Zhang, Wei Xu, Liang Wang, Bruno Rossetto
2011 J jnl
Int. J. Bifurc. Chaos
Ying Zhang, Bruno Rossetto, Wei Xu, Xiaole Yue, Tong Fang
2010 J jnl
Int. J. Bifurc. Chaos
Xiaole Yue, Wei Xu
2009 J jnl
Appl. Math. Comput.
Yuzhu Xiao, Wei Xu, Sufang Tang, Xiuchun Li
2009 J jnl
J. Comput. Appl. Math.
Zhaoyang Lu, Wei Xu, Decai Sun, Weiguo Han
2009 J jnl
Math. Comput. Simul.
Li Ling, Wei Xu, Minghai Li
2008 J jnl
Math. Comput. Model.
Zhongkui Sun, Wei Xu, Xiaoli Yang
2008 J jnl
Appl. Math. Comput.
Xiaoshan Zhao, Mingchun Wang, Wei Xu
2008 J jnl
Appl. Math. Comput.
Hongxian Zhou, Wei Xu
2007 J jnl
Appl. Math. Comput.
Wei Xu, Liang Gao, Yaning Tang, Jianwei Shen
2007 J jnl
Appl. Math. Comput.
Hongxian Zhou, Wei Xu, Xiaoshan Zhao, Bingchang Zhou
2007 J jnl
Appl. Math. Comput.
Hongxian Zhou, Wei Xu, Xiaoshan Zhao, Shuang Li
2007 J jnl
Appl. Math. Comput.
Hongxian Zhou, Wei Xu, Shuang Li, Ying Zhang
2007 J jnl
Appl. Math. Comput.
Ying Zhang, Wei Xu, Tong Fang
2007 J jnl
Appl. Math. Comput.
Xiaoshan Zhao, Wei Xu
2006 J jnl
Appl. Math. Comput.
Xiaoshan Zhao, Wei Xu, Shuang Li, Jianwei Shen
2006 J jnl
Appl. Math. Comput.
Ruihong Li, Wei Xu, Shuang Li
2006 J jnl
Int. J. Bifurc. Chaos
Xiaoli Yang, Wei Xu, Zhongkui Sun
2006 J jnl
Appl. Math. Comput.
Jianwei Shen, Wei Xu
2005 J jnl
Appl. Math. Comput.
Jianwei Shen, Wei Xu, Yanfei Jin
2005 J jnl
Appl. Math. Comput.
Jianwei Shen, Jibin Li, Wei Xu
2005 J jnl
Appl. Math. Comput.
Xiaoli Yang, Wei Xu, Zhongkui Sun, Yong Xu
2005 J jnl
Appl. Math. Comput.
Jianwei Shen, Wei Xu, Yong Xu
2003 J jnl
Int. J. Bifurc. Chaos
Wei Xu, Qun He, Tong Fang, Haiwu Rong
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)