Rahul Madhavan

17 papers A* 2A 1Journal 12Unranked 2
YearRankTypeTitle / Venue / Authors
2025 A* conf
ICML
Taneesh Gupta, Rahul Madhavan, Xuchao Zhang, Chetan Bansal, Saravan Rajmohan
2025 J jnl
CoRR
Taneesh Gupta, Rahul Madhavan, Xuchao Zhang, Chetan Bansal, Saravan Rajmohan
2025 conf
ACL (Findings)
Taneesh Gupta, Shivam Shandilya, Xuchao Zhang, Rahul Madhavan, Supriyo Ghosh, Chetan Bansal, Huaxiu Yao, Saravan Rajmohan
2025 J jnl
CoRR
Pragya Srivastava, Harman Singh, Rahul Madhavan, Gandharv Patil, Sravanti Addepalli, Arun Suggala, Rengarajan Aravamudhan, Soumya Sharma, Anirban Laha, Aravindan Raghuveer, Karthikeyan Shanmugam, Doina Precup
2024 J jnl
RLJ
Rahul Madhavan, Aurghya Maiti, Gaurav Sinha, Siddharth Barman
2024 J jnl
CoRR
Rahul Madhavan, Aurghya Maiti, Gaurav Sinha, Siddharth Barman
2024 J jnl
CoRR
Taneesh Gupta, Rahul Madhavan, Xuchao Zhang, Chetan Bansal, Saravan Rajmohan
2024 J jnl
CoRR
Taneesh Gupta, Rahul Madhavan, Xuchao Zhang, Chetan Bansal, Saravan Rajmohan
2024 A* conf
NeurIPS
Yerram Varun, Rahul Madhavan, Sravanti Addepalli, Arun Suggala, Karthikeyan Shanmugam, Prateek Jain
2024 J jnl
CoRR
Yerram Varun, Rahul Madhavan, Sravanti Addepalli, Arun Suggala, Karthikeyan Shanmugam, Prateek Jain
2023 conf
ACL (Findings)
Rahul Madhavan, Rishabh Garg, Kahini Wadhawan, Sameep Mehta
2023 J jnl
CoRR
Rahul Madhavan, Rishabh Garg, Kahini Wadhawan, Sameep Mehta
2023 J jnl
CoRR
Rahul Madhavan, Kahini Wadhawan
2023 J jnl
CoRR
Ayush Sawarni, Rahul Madhavan, Gaurav Sinha, Siddharth Barman
2023 A conf
UAI
Ayush Sawarni, Rahul Madhavan, Gaurav Sinha, Siddharth Barman
2021 J jnl
CoRR
Rahul Madhavan, Aurghya Maiti, Gaurav Sinha, Siddharth Barman
2021 J jnl
CoRR
Rahul Madhavan, Gugan Thoppe, Hemanta Makwana
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 == []