Maartje de Graaf

14 papers A* 4B 2Journal 4Unranked 3
YearRankTypeTitle / Venue / Authors
2026 J jnl
ACM Trans. Hum. Robot Interact.
Timea Noemi Nagy, Zahra Rezaei Khavas, Monish Reddy Kotturu, Baptist Liefooghe, Paul Robinette, Maartje de Graaf
2026 ed.
HRI Companion
Lynne Baillie, William D. Smart, Maartje de Graaf, Matthew Gombolay, Ilaria Torre
2026 A* ed.
HRI
Lynne Baillie, William D. Smart, Maartje de Graaf, Matthew Gombolay, Ilaria Torre
2025 A* conf
ICRA
Joséphine Mélot-Chesnel, Maartje de Graaf
2024 B conf
RO-MAN
Sam Thellman, Kelvin Koenders, Anouk Neerincx, Maartje de Graaf
2024 conf
HRI (Companion)
Elmira Yadollahi, Marta Romeo, Fethiye Irmak Dogan, Wafa Johal, Maartje de Graaf, Shelly Levy-Tzedek, Iolanda Leite
2023 B conf
RO-MAN
Anouk Neerincx, Yanzhe Li, Kelvin van de Sande, Frank Broz, Mark A. Neerincx, Maartje de Graaf
2022 A* conf
HRI
Maartje de Graaf, Giulia Perugia, Eduard Fosch-Villaronga, Angelica Lim, Frank Broz, Elaine Schaertl Short, Mark A. Neerincx
2022 J jnl
ACM Trans. Hum. Robot Interact.
Sam Thellman, Maartje de Graaf, Tom Ziemke
2021 conf
UMAP (Adjunct Publication)
Anouk Neerincx, Thirza Hiwat, Maartje de Graaf
2020 conf
HRI (Companion)
Matthew Rueben, Stefanos Nikolaidis, Maartje de Graaf, Elizabeth Phillips, Lionel Robert, David Sirkin, Minae Kwon, Sam Thellman
2020 J jnl
Int. J. Soc. Robotics
Somaya Ben Allouch, Maartje de Graaf, Selma Sabanovic
2019 A* conf
HRI
Hee Rin Lee, Eunjeong Cheon, Maartje de Graaf, Patrícia Alves-Oliveira, Cristina Zaga, James E. Young
2018 J jnl
Interactions
Xuan Luo, Jason Lawrence, Steven M. Seitz, Anne-Claire Bourland, Peter Gorman, Jess McIntosh, Asier Marzo, Cheng-Te Chi, Ian Gonsher, Steve Kim, McKenna Cisler, Jonathan Lister, Benjamin Navetta, Peter Haas, Ethan Mok, Horatio Han, Beth Phillips, Maartje de Graaf
tests/unit/test_decompile_utils.py
← Index tests/unit/test_decompile_utils.py python
"""Unit tests for decompiler utility modules:
- bninja/utils/hashes.py
- bninja/utils/json_encoder.py
- bninja/analysis/low_level_normalization.py
"""
import hashlib
import json
import pytest


# ============================================================================
# 1a. hashes.py
# ============================================================================

from redb.extractors.decompiler.bninja.utils.hashes import (
    calculate_md5,
    calculate_sha256,
    calculate_tlsh,
)


class TestCalculateMD5:
    def test_calculate_md5_known_value(self):
        expected = hashlib.md5(b"test").hexdigest()
        assert calculate_md5("test") == expected

    def test_calculate_md5_empty(self):
        expected = hashlib.md5(b"").hexdigest()
        assert calculate_md5("") == expected


class TestCalculateSHA256:
    def test_calculate_sha256_known_value(self):
        expected = hashlib.sha256(b"hello world").hexdigest()
        assert calculate_sha256("hello world") == expected

    def test_calculate_sha256_empty_string(self):
        result = calculate_sha256("")
        assert len(result) == 64
        assert all(c in "0123456789abcdef" for c in result)


class TestCalculateTLSH:
    def test_calculate_tlsh_long_data(self):
        # TLSH requires >= 50 bytes
        data = "A" * 100
        result = calculate_tlsh(data)
        assert result is not None
        assert isinstance(result, str)

    def test_calculate_tlsh_short_data(self):
        data = "A" * 10
        result = calculate_tlsh(data)
        assert result is None

    def test_calculate_tlsh_deterministic(self):
        data = "x" * 200
        assert calculate_tlsh(data) == calculate_tlsh(data)



# ============================================================================
# 1b. json_encoder.py
# ============================================================================

from redb.extractors.decompiler.bninja.utils.json_encoder import BinaryNinjaEncoder


class TestBinaryNinjaEncoder:
    def test_encode_value_confidence_object(self):
        obj = type("VC", (), {"value": 42, "confidence": 255})()
        result = json.dumps(obj, cls=BinaryNinjaEncoder)
        assert json.loads(result) == 42

    def test_encode_str_fallback(self):
        obj = type("Obj", (), {"__str__": lambda self: "custom_repr"})()
        result = json.dumps(obj, cls=BinaryNinjaEncoder)
        assert json.loads(result) == "custom_repr"

    def test_encode_normal_types(self):
        data = {"a": 1, "b": [2, 3], "c": "hello"}
        result = json.dumps(data, cls=BinaryNinjaEncoder)
        assert json.loads(result) == data

    def test_encode_set_via_str(self):
        # Python sets have __str__, so BinaryNinjaEncoder converts them
        # to their string repr instead of raising TypeError.
        result = json.dumps(set([1, 2, 3]), cls=BinaryNinjaEncoder)
        parsed = json.loads(result)
        assert isinstance(parsed, str)
        assert "1" in parsed


# ============================================================================
# 1c. low_level_normalization.py
# ============================================================================

from redb.extractors.decompiler.bninja.analysis.low_level_normalization import (
    LowLevelNormalization,
)


class MockIL:
    """Mock IL node for normalization tests."""
    def __init__(self, operation, operands=None):
        self.operation = operation
        self.operands = operands or []


class TestLowLevelNormalization:
    def setup_method(self):
        self.normalizer = LowLevelNormalization()

    def test_normalize_single_instruction(self):
        node = MockIL(operation=5)
        result = self.normalizer.normalize_instruction_all_levels(node)
        assert result == [5]

    def test_normalize_nested_operands(self):
        child1 = MockIL(operation=10)
        child2 = MockIL(operation=20)
        root = MockIL(operation=1, operands=[child1, child2])
        result = self.normalizer.normalize_instruction_all_levels(root)
        assert result == [1, 10, 20]

    def test_normalize_empty_operands(self):
        node = MockIL(operation=42, operands=[])
        result = self.normalizer.normalize_instruction_all_levels(node)
        assert result == [42]

    def test_normalize_list_operands(self):
        # Simulates phi-node style list operands
        inner = MockIL(operation=99)
        node = MockIL(operation=7, operands=[[inner]])
        result = self.normalizer.normalize_instruction_all_levels(node)
        assert result == [7, 99]

    def test_normalize_none_input(self):
        result = self.normalizer.normalize_instruction_all_levels(None)
        assert result == []