Carlo Caltagirone

44 papers A* 2Misc 1Journal 31Unranked 9
YearRankTypeTitle / Venue / Authors
2025 J jnl
NeuroImage
Sabrina Bonarota, Giulia Caruso, Carlotta Di Domenico, Sofia Sperati, Federico Maria Tamigi, Giovanni Giulietti, Federico Giove, Carlo Caltagirone, Laura Serra
2021 J jnl
Frontiers Virtual Real.
Federica Pallavicini, Eleonora Orena, Simona di Santo, Luca Greci, Chiara Caragnano, Paolo Ranieri, Costanza Vuolato, Alessandro Pepe, Guido Veronese, Antonios Dakanalis, Angelo Rossini, Carlo Caltagirone, Massimo Clerici, Fabrizia Mantovani
2020 J jnl
NeuroImage
Giacomo Koch, Romina Esposito, Caterina Motta, Elias Paolo Casula, Francesco Di Lorenzo, Sonia Bonnì, Alex Martino Cinnera, Viviana Ponzo, Michele Maiella, Silvia Picazio, Martina Assogna, Fabrizio Sallustio, Carlo Caltagirone, Maria Concetta Pellicciari
2018 J jnl
NeuroImage
Maria Concetta Pellicciari, Sonia Bonnì, Viviana Ponzo, Alex Martino Cinnera, Matteo Mancini, Elias P. Casula, Fabrizio Sallustio, Stefano Paolucci, Carlo Caltagirone, Giacomo Koch
2018 J jnl
J. Cogn. Neurosci.
Paola Marangolo, Valentina Fiori, Carlo Caltagirone, Francesca Pisano, Alberto Priori
2018 J jnl
NeuroImage
Giacomo Koch, Sonia Bonnì, Maria Concetta Pellicciari, Elias P. Casula, Matteo Mancini, Romina Esposito, Viviana Ponzo, Silvia Picazio, Francesco Di Lorenzo, Laura Serra, Caterina Motta, Michele Maiella, Camillo Marra, Mara Cercignani, Alessandro Martorana, Carlo Caltagirone, Marco Bozzali
2016 J jnl
J. Cogn. Neurosci.
Paola Marangolo, Valentina Fiori, Umberto Sabatini, Giada De Pasquale, Carmela Razzano, Carlo Caltagirone, Tommaso Gili
2016 J jnl
NeuroImage
Elias P. Casula, Maria Concetta Pellicciari, Silvia Picazio, Carlo Caltagirone, Giacomo Koch
2015 conf
ICT4AgeingWell
Francesco Barban, Roberta Annicchiarico, Alessia Federici, Ilenia Debora Mazzù, Maria Giovanna Lombardi, Simone Giuli, Claudia Ricci, Fulvia Adriano, Ivo Griffini, Manuel Silvestri, Massimo Chiusso, Sergio Neglia, Raquel Cuevas Perez, Yannis Dionyssiotis, Georgios Koumanakos, Milo Kovaceic, Nuria Montero, Oscar Pino, Carmela Zincarelli, Niels Boye, Cristian Barrué, Peter Levene, Stelios Pantelopoulos, Roberto Rosso, Angelo Maria Sabatini, Carlo Caltagirone
2012 J jnl
J. Biomed. Informatics
David Riaño, Francis Real, Joan Albert López-Vallverdú, Fabio Campana, Sara Ercolani, Patrizia Mecocci, Roberta Annicchiarico, Carlo Caltagirone
2012 J jnl
NeuroImage
Giovanni Giulietti, Marco Bozzali, Viviana Figura, Barbara Spanò, Roberta Perri, Camillo Marra, Giordano Lacidogna, Franco Giubilei, Carlo Caltagirone, Mara Cercignani
2011 J jnl
Auton. Robots
Cristina Urdiales, Jose Manuel Peula, Manuel Fernández-Carmona, Cristian Barrué, Eduardo J. Pérez, Isabel Sánchez-Tato, J. C. del Toro, Francesco Galluppi, Ulises Cortés, Roberta Annichiaricco, Carlo Caltagirone, Francisco Sandoval Hernández
2011 J jnl
NeuroImage
Marco Bozzali, Geoffrey J. M. Parker, Laura Serra, Karl V. Embleton, Tommaso Gili, Roberta Perri, Carlo Caltagirone, Mara Cercignani
2011 J jnl
J. Cogn. Neurosci.
Sara Torriero, Massimiliano Oliveri, Giacomo Koch, Emanuele Lo Gerfo, Silvia Salerno, Fabio Ferlazzo, Carlo Caltagirone, Laura Petrosini
2011 J jnl
NeuroImage
Antonio Cerasa, Aldo Quattrone, Maria C. Gioia, Patrizia Tarantino, Grazia Annesi, Francesca Assogna, Carlo Caltagirone, Vincenzo De Luca, Gianfranco Spalletta
2011 A* conf
ICRA
Gloria Peinado, Cristina Urdiales, Jose Manuel Peula Palacios, Manuel Fernández-Carmona, Roberta Annicchiarico, Francisco Sandoval Hernández, Carlo Caltagirone
2011 J jnl
J. Cogn. Neurosci.
Gian Daniele Zannino, Francesco Barban, Emiliano Macaluso, Carlo Caltagirone, Giovanni A. Carlesimo
2011 J jnl
Artif. Intell. Medicine
Cristina Urdiales, Manuel Fernández-Carmona, Jose Manuel Peula, Ulises Cortés, Roberta Annichiaricco, Carlo Caltagirone, Francisco Sandoval Hernández
2011 J jnl
NeuroImage
Gary Donohoe, Emma J. Rose, Thomas Frodl, Derek W. Morris, Ilaria Spoletini, Fulvia Adriano, Sergio Bernardini, Carlo Caltagirone, Paola Bossù, Michael Gill, Aiden P. Corvin, Gianfranco Spalletta
2010 J jnl
Int. J. Comput. Heal.
Ulises Cortés, Cristian Barrué, Antonio B. Martínez, Cristina Urdiales, Fabio Campana, Roberta Annicchiarico, Carlo Caltagirone
2010 J jnl
NeuroImage
Margherita Di Paola, Eileen Luders, Fulvia Di Iulio, Andrea Cherubini, Domenico Passafiume, Paul M. Thompson, Carlo Caltagirone, Arthur W. Toga, Gianfranco Spalletta
2010 A* conf
ICRA
Cristina Urdiales, Manuel Fernández-Carmona, Jose Manuel Peula, Roberta Annicchiarico, Francisco Sandoval Hernández, Carlo Caltagirone
2010 J jnl
NeuroImage
Giacomo Koch, Mara Cercignani, Cristiano Pecchioli, Viviana Versace, Massimiliano Oliveri, Carlo Caltagirone, John C. Rothwell, Marco Bozzali
2010 J jnl
J. Cogn. Neurosci.
Gian Daniele Zannino, Ivana Buccione, Roberta Perri, Emiliano Macaluso, Emanuele Lo Gerfo, Carlo Caltagirone, Giovanni A. Carlesimo
2010 conf
eHealth
Carolina Rubio, Roberta Annicchiarico, Cristian Barrué, Ulises Cortés, Miquel Sànchez-Marrè, Carlo Caltagirone
2010 conf
CCIA
Carolina Rubio, Roberta Annicchiarico, Cristian Barrué, Ulises Cortés, Carlo Caltagirone
2009 conf
IWANN (1)
Cristina Urdiales, Jose Manuel Peula, Ulises Cortés, Cristian Barrué, Blanca Fernández-Espejo, Roberta Annicchiarico, Francisco Sandoval Hernández, Carlo Caltagirone
2009 J jnl
NeuroImage
Andrea Cherubini, Patrice Péran, Carlo Caltagirone, Umberto Sabatini, Gianfranco Spalletta
2009 J jnl
NeuroImage
Massimiliano Oliveri, Giacomo Koch, Silvia Salerno, Sara Torriero, Emanuele Lo Gerfo, Carlo Caltagirone
2009 conf
IWANN (1)
Roberta Annicchiarico, Fabio Campana, Alessia Federici, Cristian Barrué, Ulises Cortés, A. Villar, Carlo Caltagirone
2008 conf
AAAI Fall Symposium: AI in Eldercare: New Solutions to Old Problems
Cristina Urdiales, Jose Manuel Peula, Cristian Barrué, Ulises Cortés, Francisco Sandoval Hernández, Carlo Caltagirone, Roberta Annicchiarico
2008 conf
AAAI Fall Symposium: AI in Eldercare: New Solutions to Old Problems
Ulises Cortés, Antonio Martínez-Velasco, Cristian Barrué, E. X. Martín, Fabio Campana, Roberta Annicchiarico, Carlo Caltagirone
2007 ch.
Advanced Computational Intelligence Paradigms in Healthcare (1)
Ulises Cortés, Cristina Urdiales, Roberta Annicchiarico, Cristian Barrué, Antonio B. Martínez, Carlo Caltagirone
2007 J jnl
NeuroImage
Carlo Reverberi, Paolo Cherubini, Attilio Rapisarda, Elisa Rigamonti, Carlo Caltagirone, Richard S. J. Frackowiak, Emiliano Macaluso, Eraldo Paulesu
2007 Misc conf
IWANN
Roberta Annicchiarico, Ulises Cortés, Alessia Federici, Fabio Campana, Cristian Barrué, Antonio B. Martínez, Carlo Caltagirone
2007 J jnl
J. Cogn. Neurosci.
Sara Torriero, Massimiliano Oliveri, Giacomo Koch, Carlo Caltagirone, Laura Petrosini
2006 J jnl
NeuroImage
Deny Menghini, Gisela E. Hagberg, Carlo Caltagirone, Laura Petrosini, Stefano Vicari
2005 conf
MIE
Karina Gibert, Roberta Annicchiarico, Ulises Cortés, Carlo Caltagirone
2005 conf
CEEMAS
Antonio B. Martínez, Josep Escoda, T. Benedico, Ulises Cortés, Roberta Annicchiarico, Cristian Barrué, Carlo Caltagirone
2005 J jnl
NeuroImage
Alessandro Castriota-Scanderbeg, Gisela E. Hagberg, Antonio Cerasa, Giorgia Committeri, Gaspare Galati, Fabiana Patria, Sabrina Pitzalis, Carlo Caltagirone, Richard S. Frackowiak
2005 J jnl
NeuroImage
Giacomo Koch, Massimiliano Oliveri, Sara Torriero, Giovanni A. Carlesimo, Patrizia Turriziani, Carlo Caltagirone
2003 J jnl
Res. Comput. Sci.
Cristina Urdiales, Alberto Poncela, Roberta Annicchiarico, F. Rizzi, Francisco Sandoval, Carlo Caltagirone
2003 J jnl
AI Commun.
Ulises Cortés, Roberta Annicchiarico, Javier Vázquez-Salceda, Cristina Urdiales, Lola Cañamero, Maite López, Miquel Sànchez-Marrè, Carlo Caltagirone
2003 J jnl
Res. Comput. Sci.
Carlo Caltagirone, Cristina Urdiales, Javier Vázquez-Salceda, Lola Cañamero, Maite López, Miquel Sànchez-Marrè, Roberta Annicchiarico, Ulises Cortés
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)