Cella Monet Sum

16 papers A* 1Journal 10Unranked 5
YearRankTypeTitle / Venue / Authors
2025 J jnl
Proc. ACM Hum. Comput. Interact.
Cella Monet Sum, Jiayin Zhi, Amil N. T. Cook, Patrick James Cooper, Arturo Lozano, Tj Johnson, Jason Perez, Rayid Ghani, Michael Skirpan, Motahhare Eslami, Hong Shen, Sarah E. Fox
2025 J jnl
Proc. ACM Hum. Comput. Interact.
Cella Monet Sum, Caroline Shi, Sarah E. Fox
2025 J jnl
CoRR
Morgan McErlean, Cella Monet Sum, Sukrit Venkatagiri, Sarah E. Fox
2025 conf
Aarhus Conference (Adjunct)
Linda Huber, Pedro Reynolds-Cuéllar, Alicia DeVrio, Jensine Raihan, Cella Monet Sum, Lynn Dombrowski, Justine Zhang, Christoph B. Becker, Lilly Irani, P. M. Krafft, Margaret Hughes
2025 conf
CSCW Companion
Cella Monet Sum
2025 J jnl
CoRR
Cella Monet Sum, Anna Konvicka, Mona Wang, Sarah E. Fox
2025 J jnl
Proc. ACM Hum. Comput. Interact.
Cella Monet Sum, Anna Konvicka, Mona Wang, Sarah E. Fox
2024 J jnl
CoRR
Cella Monet Sum, Caroline Shi, Sarah E. Fox
2024 J jnl
XRDS
Cella Monet Sum, Franchesca Spektor, Rahaf Alharbi, Leya Breanna Baltaxe-Admony, Erika Devine, Hazel Anneke Dixon, Jared Duval, Tessa Eagle, Frank Elavsky, Kim Fernandes, Leandro Soares Guedes, Serena Hillman, Vaishnav Kameswaran, Lynn Kirabo, Tamanna Motahar, Kathryn E. Ringland, Anastasia Schaadhardt, Laura Scheepmaker, Alicia Williamson
2024 J jnl
CoRR
William Agnew, Harry H. Jiang, Cella Monet Sum, Maarten Sap, Sauvik Das
2024 conf
CSCW Companion
Kim Fernandes, Rahaf Alharbi, Cella Monet Sum, Vaishnav Kameswaran, Franchesca Spektor, Ashique Ali Thuppilikkat, Adrian Petterson, Megh Marathe, Foad Hamidi, Priyank Chandra
2024 J jnl
XRDS
Cella Monet Sum, Alicia DeVrio
2024 J jnl
Proc. ACM Hum. Comput. Interact.
Christina N. Harrington, Paola Favela, Cella Monet Sum, Sarah E. Fox, Lynn Dombrowski
2023 A* conf
CHI
Cella Monet Sum, Anh-Ton Tran, Jessica Lin, Rachel Kuo, Cynthia L. Bennett, Christina N. Harrington, Sarah E. Fox
2022 conf
CHI Extended Abstracts
Cella Monet Sum, Rahaf Alharbi, Franchesca Spektor, Cynthia L. Bennett, Christina N. Harrington, Katta Spiel, Rua Mae Williams
2020 conf
CHI Extended Abstracts
Emory James Edwards, Cella Monet Sum, Stacy M. Branham
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 == []