Ramakrishnan Sundaram

29 papers B 3C 17Misc 3Journal 3Unranked 3
YearRankTypeTitle / Venue / Authors
2024 C conf
FIE
Ramakrishnan Sundaram
2022 conf
WF-IoT
Ramakrishnan Sundaram
2021 B conf
FG
Yiming Han, Xiaocong Fan, Radhika Bhosale, Ramakrishnan Sundaram, Jonathan Liaw
2021 C conf
FIE
Steve Rowland, Ramakrishnan Sundaram
2020 J jnl
Multim. Tools Appl.
Ramakrishnan Sundaram, K. S. Ravichandran, Premaladha Jayaraman, Balasubramaniam Venkatraman
2020 C conf
ICIS
Premaladha Jayaraman, Raghunathan Krishankumar, K. S. Ravichandran, Ramakrishnan Sundaram, Samarjit Kar
2019 J jnl
J. Intell. Fuzzy Syst.
Ramakrishnan Sundaram, K. S. Ravichandran
2019 J jnl
J. Medical Imaging Health Informatics
Ramakrishnan Sundaram, Premaladha Jayaraman, R. Rangarajan, R. Rengasri, C. Rajeshwari, K. S. Ravichandran
2019 C conf
FIE
Ramakrishnan Sundaram
2018 C conf
FIE
Ramakrishnan Sundaram
2017 C conf
FIE
Ramakrishnan Sundaram
2016 C conf
FIE
Ramakrishnan Sundaram
2016 C conf
FIE
Fong Mak, Ramakrishnan Sundaram
2015 C conf
FIE
Ramakrishnan Sundaram
2015 C conf
FIE
Ramakrishnan Sundaram
2015 C conf
FIE
Ramakrishnan Sundaram
2013 C conf
FIE
Qing Zheng, Pengtao Lin, Fong Mak, Ramakrishnan Sundaram, Lin Zhao
2013 C conf
FIE
Ramakrishnan Sundaram
2012 C conf
FIE
Ramakrishnan Sundaram
2011 C conf
FIE
Ramakrishnan Sundaram
2011 C conf
FIE
Qing Zheng, Ramakrishnan Sundaram, Fong Mak
2011 C conf
FIE
Ramakrishnan Sundaram
2007 B conf
IJCNN
Ramakrishnan Sundaram
2007 B conf
IJCNN
Hasan U. Ozer, Ramakrishnan Sundaram
2005 Misc conf
WSC
Ronald E. Giachetti, Edwin A. Centeno, Martha A. Centeno, Ramakrishnan Sundaram
2003 conf
Visual Information Processing
Niranjan Tallapally, Ramakrishnan Sundaram
2003 Misc conf
Visualization and Data Analysis
Niranjan Tallapally, Ramakrishnan Sundaram, Leonard R. Coover
2002 Misc conf
Visualization and Data Analysis
Ramakrishnan Sundaram
1998 conf
Visual Information Processing
Ramakrishnan Sundaram
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 == []