Xiangnan Kong

167 papers A* 38A 29B 9C 2Misc 2Journal 54Unranked 31
YearRankTypeTitle / Venue / Authors
2025 conf
AAAI Spring Symposia
Yao Su, Keqi Han, Mingjie Zeng, Lichao Sun, Liang Zhan, Carl Yang, Lifang He, Xiangnan Kong
2025 J jnl
CoRR
Yao Su, Keqi Han, Mingjie Zeng, Lichao Sun, Liang Zhan, Carl Yang, Lifang He, Xiangnan Kong
2024 A* conf
AAAI
Jidapa Thadajarassiri, Walter Gerych, Xiangnan Kong, Elke A. Rundensteiner
2024 B conf
IEEE Big Data
Hang Yin, Yao Su, Liping Liu, Thomas Hartvigsen, Xin Dai, Xiangnan Kong
2024 J jnl
CoRR
Hang Yin, Yao Su, Liping Liu, Thomas Hartvigsen, Xin Dai, Xiangnan Kong
2023 J jnl
CoRR
Thomas Hartvigsen, Jidapa Thadajarassiri, Xiangnan Kong, Elke A. Rundensteiner
2023 A* conf
AAAI
Jidapa Thadajarassiri, Thomas Hartvigsen, Walter Gerych, Xiangnan Kong, Elke A. Rundensteiner
2023 B conf
IEEE Big Data
Jianjun Luo, Ryan A. Rossi, Xiangnan Kong, Yanhua Li
2023 B conf
IEEE Big Data
Hang Yin, Yao Su, Xinyue Liu, Thomas Hartvigsen, Yanhua Li, Xiangnan Kong
2023 J jnl
CoRR
Hang Yin, Yao Su, Xinyue Liu, Thomas Hartvigsen, Yanhua Li, Xiangnan Kong
2023 A* conf
KDD
Yao Su, Zhentian Qian, Lei Ma, Lifang He, Xiangnan Kong
2023 J jnl
CoRR
Yao Su, Zhentian Qian, Lei Ma, Lifang He, Xiangnan Kong
2022 A* conf
ICDM
Yao Su, Xin Dai, Lifang He, Xiangnan Kong
2022 J jnl
CoRR
Yao Su, Xin Dai, Lifang He, Xiangnan Kong
2022 A* conf
KDD
Yao Su, Zhentian Qian, Lifang He, Xiangnan Kong
2022 J jnl
CoRR
Yao Su, Zhentian Qian, Lifang He, Xiangnan Kong
2022 J jnl
World Wide Web
Yao Zhang, Sijia Peng, Yun Xiong, Xiangnan Kong, Xinyue Liu, Yangyong Zhu
2022 J jnl
IEEE Trans. Big Data
Yingxue Zhang, Yanhua Li, Xun Zhou, Xiangnan Kong, Jun Luo
2022 J jnl
CoRR
Zhongfang Zhuang, Xiangnan Kong, Elke A. Rundensteiner, Aditya Arora, Jihane Zouaoui
2022 J jnl
IEEE Trans. Knowl. Data Eng.
Nesreen K. Ahmed, Ryan A. Rossi, John Boaz Lee, Theodore L. Willke, Rong Zhou, Xiangnan Kong, Hoda Eldardiry
2022 A* conf
ICDM
Yingxue Zhang, Yanhua Li, Xun Zhou, Xiangnan Kong, Jun Luo
2022 A conf
CIKM
Thomas Hartvigsen, Walter Gerych, Jidapa Thadajarassiri, Xiangnan Kong, Elke A. Rundensteiner
2022 J jnl
CoRR
Thomas Hartvigsen, Walter Gerych, Jidapa Thadajarassiri, Xiangnan Kong, Elke A. Rundensteiner
2021 A conf
SDM
Xin Dai, Xiangnan Kong, Tian Guo, Yixian Huang
2021 A* conf
KDD
Hang Yin, John Boaz Lee, Xiangnan Kong, Thomas Hartvigsen, Sihong Xie
2021 A conf
CIKM
Xin Dai, Xiangnan Kong, Tian Guo, Xinlu He
2021 J jnl
CoRR
Hang Yin, Xinyue Liu, Xiangnan Kong
2021 J jnl
IEEE Trans. Knowl. Data Eng.
Yun Xiong, Yizhou Zhang, Xiangnan Kong, Huidi Chen, Yangyong Zhu
2021 conf
IEEE BigData
Dongyu Zhang, Cansu Sen, Jidapa Thadajarassiri, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner
2021 J jnl
IEEE Trans. Knowl. Data Eng.
Yao Zhang, Yun Xiong, Xiangnan Kong, Zhuang Niu, Yangyong Zhu
2021 B conf
ICPE
Samuel S. Ogden, Xiangnan Kong, Tian Guo
2021 A* conf
ICDM
Chao Chen, Yifan Shen, Guixiang Ma, Xiangnan Kong, Srinivas Rangarajan, Xi Zhang, Sihong Xie
2021 J jnl
CoRR
Chao Chen, Yifan Shen, Guixiang Ma, Xiangnan Kong, Srinivas Rangarajan, Xi Zhang, Sihong Xie
2021 A* conf
AAAI
Jidapa Thadajarassiri, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner
2020 conf
EMNLP (Findings)
Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Xiangnan Kong, Elke A. Rundensteiner
2020 conf
DASFAA (3)
Yun Xiong, Shaofeng Xu, Keyao Rong, Xinyue Liu, Xiangnan Kong, Shanshan Li, Philip S. Yu, Yangyong Zhu
2020 A* conf
KDD
Yingxue Zhang, Yanhua Li, Xun Zhou, Xiangnan Kong, Jun Luo
2020 A conf
SDM
John Boaz Lee, Xiangnan Kong, Constance M. Moore, Nesreen K. Ahmed
2020 A conf
SDM
Xin Dai, Xiangnan Kong, Xinyue Liu, John Boaz Lee, Constance M. Moore
2020 A conf
CIKM
Xin Dai, Xiangnan Kong, Tian Guo
2020 conf
IEEE BigData
Hang Yin, Xinyue Liu, Xiangnan Kong
2020 A* conf
ACL
Cansu Sen, Thomas Hartvigsen, Biao Yin, Xiangnan Kong, Elke A. Rundensteiner
2020 conf
IEEE BigData
Jidapa Thadajarassiri, Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner
2020 A conf
CIKM
Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, Elke A. Rundensteiner
2020 conf
IEEE BigData
Zhongfang Zhuang, Xiangnan Kong, Elke A. Rundensteiner, Jihane Zouaoui, Aditya Arora
2020 J jnl
CoRR
Zhongfang Zhuang, Xiangnan Kong, Elke A. Rundensteiner, Jihane Zouaoui, Aditya Arora
2020 conf
APWeb/WAIM (1)
Yao Zhang, Yun Xiong, Xiangnan Kong, Xinyue Liu, Yangyong Zhu
2020 A* conf
KDD
Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, Elke A. Rundensteiner
2020 A* conf
KDD
Xin Dai, Xiangnan Kong, Tian Guo, John Boaz Lee, Xinyue Liu, Constance M. Moore
2020 J jnl
Inf. Process. Manag.
Yugang Ji, Chuan Shi, Yuan Fang, Xiangnan Kong, Mingyang Yin
2020 conf
IEEE BigData
Xiao Qin, Cao Xiao, Tengfei Ma, Tabassum Kakar, Susmitha Wunnava, Xiangnan Kong, Elke A. Rundensteiner, Fei Wang
2019 A conf
SDM
Zhongfang Zhuang, Xiangnan Kong, Elke A. Rundensteiner
2019 A* conf
KDD
Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, Elke A. Rundensteiner
2019 conf
IEEE BigData
Zhongfang Zhuang, Xiangnan Kong, Elke A. Rundensteiner, Jihane Zouaoui, Aditya Arora
2019 J jnl
CoRR
Zhongfang Zhuang, Xiangnan Kong, Elke A. Rundensteiner, Jihane Zouaoui, Aditya Arora
2019 A conf
CIKM
Yuyan Zheng, Chuan Shi, Xiangnan Kong, Yanfang Ye
2019 conf
BHI
Jidapa Thadajarassiri, Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner
2019 B conf
ASONAM
Jianjun Luo, Xinyue Liu, Xiangnan Kong
2019 A conf
CIKM
John Boaz Lee, Ryan A. Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, Anup Rao
2019 conf
IEEE BigData
Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner
2019 J jnl
Appl. Netw. Sci.
John Boaz Lee, Xiangnan Kong
2019 J jnl
IEEE Trans. Cybern.
Yafang Li, Caiyan Jia, Xiangnan Kong, Liu Yang, Jian Yu
2019 conf
DASFAA (1)
Shaofeng Xu, Yun Xiong, Xiangnan Kong, Yangyong Zhu
2019 conf
WI (Companion)
Yao Zhang, Yun Xiong, Lu Ruan, Xiangnan Kong, Yangyong Zhu
2019 conf
IEEE BigData
Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke A. Rundensteiner
2019 A* conf
WWW
Thanh Tran, Xinyue Liu, Kyumin Lee, Xiangnan Kong
2019 J jnl
CoRR
Thanh Tran, Xinyue Liu, Kyumin Lee, Xiangnan Kong
2019 A* conf
ICDM
Yingxue Zhang, Yanhua Li, Xun Zhou, Xiangnan Kong, Jun Luo
2018 A* conf
KDD
Xinyue Liu, Xiangnan Kong, Philip S. Yu
2018 conf
Medication and Adverse Drug Event Detection
Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Elke A. Rundensteiner, Xiangnan Kong
2018 C conf
ACML
Yafang Li, Xiangnan Kong, Caiyan Jia, Jianqiang Li
2018 A* conf
ICDM
Hang Yin, Xiangnan Kong, Xinyue Liu
2018 A* conf
WWW
Yizhou Zhang, Yun Xiong, Xiangnan Kong, Shanshan Li, Jinhong Mi, Yangyong Zhu
2018 Misc conf
AMIA
Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Elke A. Rundensteiner, Xiangnan Kong
2018 conf
HEALTHINF
Thomas Hartvigsen, Cansu Sen, Sarah Brownell, Erin Teeple, Xiangnan Kong, Elke A. Rundensteiner
2018 conf
DASFAA (1)
Chang Liao, Yun Xiong, Xiangnan Kong, Yangyong Zhu, Shimin Zhao, Shanshan Li
2018 ch.
Encyclopedia of Social Network Analysis and Mining. 2nd Ed.
Xiangnan Kong, Philip S. Yu
2018 A* conf
KDD
John Boaz Lee, Ryan A. Rossi, Xiangnan Kong
2018 J jnl
CoRR
John Boaz Lee, Ryan A. Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, Anup Rao
2018 J jnl
CoRR
Nesreen K. Ahmed, Ryan A. Rossi, John Boaz Lee, Xiangnan Kong, Theodore L. Willke, Rong Zhou, Hoda Eldardiry
2018 conf
BIOSTEC (Selected Papers)
Susmitha Wunnava, Xiao Qin, Tabassum Kakar, M. L. Tlachac, Xiangnan Kong, Elke A. Rundensteiner, Sanjay K. Sahoo, Suranjan De
2018 J jnl
IEEE Trans. Knowl. Data Eng.
Yun Xiong, Yizhou Zhang, Xiangnan Kong, Yangyong Zhu
2018 conf
HEALTHINF
Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Xiangnan Kong, Elke A. Rundensteiner, Sanjay K. Sahoo, Suranjan De
2018 conf
IEEE BigData
Zhongfang Zhuang, Xiangnan Kong, Elke A. Rundensteiner, Aditya Arora, Jihane Zouaoui
2018 conf
BIBM
Yun Xiong, Lu Ruan, Mengjie Guo, Chunlei Tang, Xiangnan Kong, Yangyong Zhu, Wei Wang
2018 A* conf
ICDE
Dongqing Xiao, Mohamed Y. Eltabakh, Xiangnan Kong
2018 conf
DASFAA (1)
Chang Liao, Yun Xiong, Xiangnan Kong, Yangyong Zhu
2018 A* conf
ICDM
Xinyue Liu, Xiangnan Kong, Lei Liu, Kuorong Chiang
2018 J jnl
CoRR
Xinyue Liu, Xiangnan Kong, Lei Liu, Kuorong Chiang
2017 J jnl
CoRR
Nesreen K. Ahmed, Ryan A. Rossi, Rong Zhou, John Boaz Lee, Xiangnan Kong, Theodore L. Willke, Hoda Eldardiry
2017 A* conf
ICDM
Xinyue Liu, Yuanfang Song, Charu C. Aggarwal, Yao Zhang, Xiangnan Kong
2017 B conf
IJCNN
Xinyue Liu, Xiangnan Kong, Philip S. Yu
2017 J jnl
CoRR
John Boaz Lee, Ryan A. Rossi, Xiangnan Kong
2017 J jnl
SIGWEB Newsl.
Maryam Hasan, Elke A. Rundensteiner, Xiangnan Kong, Emmanuel Agu
2017 A conf
SDM
John Boaz Lee, Xiangnan Kong, Yihan Bao, Constance M. Moore
2017 A* conf
ICDM
Saket Sathe, Charu C. Aggarwal, Xiangnan Kong, Xinyue Liu
2017 A conf
CIKM
Yao Zhang, Yun Xiong, Xiangnan Kong, Yangyong Zhu
2017 A conf
SDM
Yao Zhang, Yun Xiong, Xinyue Liu, Xiangnan Kong, Yangyong Zhu
2017 J jnl
CoRR
Nesreen K. Ahmed, Ryan A. Rossi, Rong Zhou, John Boaz Lee, Xiangnan Kong, Theodore L. Willke, Hoda Eldardiry
2017 A conf
SDM
Xinyue Liu, Xiangnan Kong, Ann B. Ragin
2017 conf
ICSC
Maryam Hasan, Elke A. Rundensteiner, Xiangnan Kong, Emmanuel Agu
2016 J jnl
CoRR
Jiawei Zhang, Xiangnan Kong, Philip S. Yu
2016 B conf
SSDBM
Dongqing Xiao, Mohamed Y. Eltabakh, Xiangnan Kong
2016 A conf
CIKM
Xinyue Liu, Xiangnan Kong, Yanhua Li
2016 A conf
SDM
Xinyue Liu, Charu C. Aggarwal, Yufeng Li, Xiangnan Kong, Xinyuan Sun, Saket Sathe
2016 A* conf
KDD
Yizhou Zhang, Yun Xiong, Xiangnan Kong, Yangyong Zhu
2016 B conf
ASONAM
Jiawei Zhang, Xiangnan Kong, Philip S. Yu
2015 J jnl
Brain Informatics
Bokai Cao, Xiangnan Kong, Philip S. Yu
2015 J jnl
CoRR
Bokai Cao, Xiangnan Kong, Philip S. Yu
2015 J jnl
ACM Trans. Multim. Comput. Commun. Appl.
Li-Jia Li, David A. Shamma, Xiangnan Kong, Sina Jafarpour, Roelof van Zwol, Xuanhui Wang
2015 conf
BIH
Bokai Cao, Liang Zhan, Xiangnan Kong, Philip S. Yu, Nathalie Vizueta, Lori L. Altshuler, Alex D. Leow
2015 J jnl
Brain Informatics
Bokai Cao, Xiangnan Kong, Jingyuan Zhang, Philip S. Yu, Ann B. Ragin
2015 A* conf
ICDM
Bokai Cao, Xiangnan Kong, Jingyuan Zhang, Philip S. Yu, Ann B. Ragin
2015 J jnl
CoRR
Bokai Cao, Xiangnan Kong, Jingyuan Zhang, Philip S. Yu, Ann B. Ragin
2015 Misc conf
IRI
Jiawei Zhang, Weixiang Shao, Senzhang Wang, Xiangnan Kong, Philip S. Yu
2015 J jnl
CoRR
Jiawei Zhang, Weixiang Shao, Senzhang Wang, Xiangnan Kong, Philip S. Yu
2015 conf
SciPy
Randy C. Paffenroth, Xiangnan Kong
2014 ch.
Data Classification: Algorithms and Applications
Charu C. Aggarwal, Xiangnan Kong, Quanquan Gu, Jiawei Han, Philip S. Yu
2014 A* conf
ICDM
Bokai Cao, Xiangnan Kong, Philip S. Yu
2014 conf
ICDM Workshops
Jingyuan Zhang, Xiaoxiao Shi, Xiangnan Kong, Hong-Han Shuai, Philip S. Yu
2014 J jnl
CoRR
Jingyuan Zhang, Xiaoxiao Shi, Xiangnan Kong, Hong-Han Shuai, Philip S. Yu
2014 A conf
SDM
Lifang He, Xiangnan Kong, Philip S. Yu, Xiaowei Yang, Ann B. Ragin, Zhifeng Hao
2014 J jnl
CoRR
Lifang He, Xiangnan Kong, Philip S. Yu, Ann B. Ragin, Zhifeng Hao, Xiaowei Yang
2014 J jnl
IEEE Trans. Knowl. Data Eng.
Chuan Shi, Xiangnan Kong, Yue Huang, Philip S. Yu, Bin Wu
2014 A conf
WSDM
Chun-Ta Lu, Sihong Xie, Xiangnan Kong, Philip S. Yu
2014 A conf
SDM
Xiangnan Kong, Zhaoming Wu, Li-Jia Li, Ruofei Zhang, Philip S. Yu, Hang Wu, Wei Fan
2014 J jnl
CoRR
Xiangnan Kong, Zhaoming Wu, Li-Jia Li, Ruofei Zhang, Philip S. Yu, Hang Wu, Wei Fan
2014 A* conf
ICDM
Lifang He, Hong-Han Shuai, Xiangnan Kong, Zhifeng Hao, Xiaowei Yang, Philip S. Yu
2014 J jnl
ACM Trans. Intell. Syst. Technol.
Chuan Shi, Xiangnan Kong, Di Fu, Philip S. Yu, Bin Wu
2014 A conf
CIKM
Jingyuan Zhang, Xiangnan Kong, Roger Jie Luo, Yi Chang, Philip S. Yu
2014 J jnl
CoRR
Chong-Jing Sun, Philip S. Yu, Xiangnan Kong, Yan Fu
2014 J jnl
Trans. Data Priv.
Chong-Jing Sun, Philip S. Yu, Xiangnan Kong, Yan Fu
2014 A* conf
ICDM
Bokai Cao, Lifang He, Xiangnan Kong, Philip S. Yu, Zhifeng Hao, Ann B. Ragin
2014 A conf
WSDM
Jiawei Zhang, Xiangnan Kong, Philip S. Yu
2014 conf
MMM (1)
Li-Jia Li, Xiangnan Kong, Philip S. Yu
2014 A conf
SDM
Ning Yang, Xiangnan Kong, Fengjiao Wang, Philip S. Yu
2014 J jnl
CoRR
Ning Yang, Xiangnan Kong, Fengjiao Wang, Philip S. Yu
2013 J jnl
SIGKDD Explor.
Xiangnan Kong, Philip S. Yu
2013 J jnl
CoRR
Xiangnan Kong, Philip S. Yu, Xue Wang, Ann B. Ragin
2013 A conf
SDM
Xiangnan Kong, Ann B. Ragin, Xue Wang, Philip S. Yu
2013 J jnl
CoRR
Chuan Shi, Xiangnan Kong, Yue Huang, Philip S. Yu, Bin Wu
2013 A conf
CIKM
Xiangnan Kong, Jiawei Zhang, Philip S. Yu
2013 J jnl
CoRR
Xiangnan Kong, Bokai Cao, Philip S. Yu, Ying Ding, David J. Wild
2013 A* conf
KDD
Xiangnan Kong, Bokai Cao, Philip S. Yu
2013 A* conf
ICDM
Sihong Xie, Xiangnan Kong, Jing Gao, Wei Fan, Philip S. Yu
2013 J jnl
CoRR
Sihong Xie, Xiangnan Kong, Jing Gao, Wei Fan, Philip S. Yu
2013 A* conf
ICDM
Jiawei Zhang, Xiangnan Kong, Philip S. Yu
2013 J jnl
CoRR
Jiawei Zhang, Xiangnan Kong, Philip S. Yu
2013 J jnl
CoRR
Shuyang Lin, Xiangnan Kong, Philip S. Yu
2013 A conf
CIKM
Shuyang Lin, Xiangnan Kong, Philip S. Yu
2013 conf
ICDM Workshops
Chong-Jing Sun, Philip S. Yu, Xiangnan Kong, Yan Fu
2013 J jnl
IEEE Trans. Knowl. Data Eng.
Xiangnan Kong, Michael K. Ng, Zhi-Hua Zhou
2012 A* conf
WWW
Wangqun Lin, Xiangnan Kong, Philip S. Yu, Quanyuan Wu, Yan Jia, Chuan Li
2012 A* conf
KDD
Chuan Shi, Chong Zhou, Xiangnan Kong, Philip S. Yu, Gang Liu, Bai Wang
2012 A conf
CIKM
Xiangnan Kong, Philip S. Yu, Ying Ding, David J. Wild
2012 conf
ASIST
Erjia Yan, Ying Ding, Xiangnan Kong
2012 A conf
SDM
Chuan Shi, Xiangnan Kong, Philip S. Yu, Bai Wang
2012 B conf
EDBT
Chuan Shi, Xiangnan Kong, Philip S. Yu, Sihong Xie, Bin Wu
2012 A conf
SDM
Xiaoxiao Shi, Xiangnan Kong, Philip S. Yu
2012 J jnl
Knowl. Inf. Syst.
Xiangnan Kong, Philip S. Yu
2011 C conf
CollaborateCom
Xiangnan Kong, Philip S. Yu
2011 A* conf
KDD
Xiangnan Kong, Wei Fan, Philip S. Yu
2011 A conf
SDM
Xiangnan Kong, Xiaoxiao Shi, Philip S. Yu
2011 conf
ECML/PKDD (3)
Chuan Shi, Xiangnan Kong, Philip S. Yu, Bai Wang
2011 A* conf
ICDM
Yuchen Zhao, Xiangnan Kong, Philip S. Yu
2010 A* conf
ICDM
Xiangnan Kong, Philip S. Yu
2010 A* conf
KDD
Xiangnan Kong, Philip S. Yu
APK_CODE_ANALYSIS_PDD.md
← Index APK_CODE_ANALYSIS_PDD.md markdown
# APK Code Analysis — Product Design Document

**Author:** Engineering Team
**Date:** 2026-03-07
**Status:** Draft
**Target:** redb ingestor pipeline
**Depends on:** APK_FEATURES_PDD.md (APK static analysis extractors — implemented)

---

## 1. Overview

This document describes the design for adding **DEX code analysis** (decompilation, disassembly, call graphs, cross-references, and function similarity) to the redb ingestor pipeline. This is the Android equivalent of the Binary Ninja code analysis pipeline that exists for PE and ELF binaries.

### 1.1 Goals

- Decompile and disassemble APK DEX bytecode at the **method level**, producing per-method content and reference records analogous to the Binary Ninja `code_binja_*` tables
- Extract **call graphs and cross-references** (caller/callee relationships) for each method
- Compute **function similarity hashes** (SHA-256, ssdeep, TLSH, MinHash) for method-level clustering and hunting
- **Filter out library/framework code** to focus on user-written application logic — same philosophy as the `is_lib_or_thunk()` filter in Binary Ninja analysis
- Produce **decompiled Java source** (via JADX) and **smali disassembly** (via apktool) for each method, following the content/reference split pattern used by Binary Ninja tables
- Integrate with the existing APK extractor pipeline (runs after the Phase 1 APK extractors from `APK_FEATURES_PDD.md`)

### 1.2 Non-Goals

- **Native .so library analysis** — These are equivalent to external DLLs/shared libraries in PE/ELF. They are catalogued by `APKNativeLibExtractor` but not decompiled. If deep native analysis is needed, the existing ELF pipeline can be used on extracted `.so` files in a future phase.
- **Dynamic analysis / emulation** — Out of scope
- **Full APK repackaging / patching** — We use apktool for disassembly only, not rebuild
- **Inter-procedural data-flow analysis** (e.g., FlowDroid taint tracking) — Future consideration

### 1.3 Relationship to Existing Work

| Existing | New (this PDD) |
|----------|----------------|
| `APK_FEATURES_PDD.md` — APK metadata, manifest, permissions, certificates, DEX summary, resources, native libs | DEX **code-level** analysis: per-method decompilation, disassembly, call graphs, similarity hashes |
| `DecompileBinja` — PE/ELF code analysis via Binary Ninja | `DecompileAPK` — APK/DEX code analysis via androguard + JADX + apktool |
| `code_binja_*` ClickHouse tables | `code_apk_*` ClickHouse tables (same content/reference split pattern) |

---

## 2. Background

### 2.1 DEX Bytecode vs Native Code

| Aspect | PE/ELF (Binary Ninja) | APK/DEX (This PDD) |
|--------|----------------------|---------------------|
| Code format | Machine code (x86, ARM) | Dalvik bytecode (register-based VM) |
| Basic unit | Function (by address) | Method (by class + signature) |
| Disassembly | x86/ARM mnemonics | Smali (Dalvik assembly) |
| Decompilation | Pseudo-C (HLIL) | Java source code |
| Library filtering | `is_lib_or_thunk()` — symbol type | Package prefix filtering (e.g., `android.*`, `androidx.*`, `com.google.*`) |
| Similarity hashing | SHA-256 of normalized disassembly | SHA-256 of normalized smali |

### 2.2 Tool Selection

Three tools are combined to replicate the Binary Ninja analysis depth:

| Tool | Role | Integration | License |
|------|------|-------------|---------|
| **Androguard** (Python library) | Method enumeration, call graphs, cross-references, bytecode access, permissions analysis | Direct Python import — `from androguard.misc import AnalyzeAPK` | Apache 2.0 |
| **JADX** (Java CLI) | High-quality Java decompilation (equivalent to Binary Ninja HLIL) | subprocess (following CAPA/DIE pattern) | Apache 2.0 |
| **apktool** (Java CLI) | Smali disassembly with resource decoding (equivalent to Binary Ninja disassembly) | subprocess (following CAPA/DIE pattern) | Apache 2.0 |

**Why all three:**
- **Androguard** is the analysis engine — it provides call graphs, xrefs, and method enumeration natively in Python. However, its decompiler (DAD) produces lower-quality Java than JADX.
- **JADX** produces the best Java decompilation available. It is the industry standard for Android reverse engineering (47k+ GitHub stars).
- **apktool** produces canonical smali output with decoded resources. While androguard can access bytecode, apktool's smali output is the standard interchange format for Android RE.

### 2.3 Library Filtering Strategy

Native `.so` libraries are **not reverse-engineered** — they are equivalent to external DLLs in PE or shared libraries in ELF, and are already inventoried by `APKNativeLibExtractor`.

For DEX code, we filter out **framework/library packages** to focus on user-written code. This is the Android equivalent of `is_lib_or_thunk()` in the Binary Ninja pipeline.

**Default filter list** (configurable via environment variable `APK_LIBRARY_PREFIXES`):

```
android.*              # Android SDK
androidx.*             # AndroidX support libraries
com.google.android.*   # Google Play Services, Firebase
com.google.firebase.*  # Firebase
com.google.gson.*      # Gson JSON library
com.google.protobuf.*  # Protocol Buffers
kotlin.*               # Kotlin stdlib
kotlinx.*              # Kotlin extensions
org.apache.*           # Apache Commons
com.squareup.*         # OkHttp, Retrofit, Moshi
io.reactivex.*         # RxJava
org.reactivestreams.*  # Reactive Streams
com.facebook.*         # Facebook SDK
com.crashlytics.*      # Crashlytics
io.fabric.*            # Fabric
org.junit.*            # Test frameworks
org.mockito.*          # Test frameworks
```

Methods in filtered packages are still counted in call graph edges (caller/callee arrays) but their content is not stored in content tables. This mirrors how Binary Ninja records calls to library functions in `functions_caller`/`functions_call` arrays without decompiling the library functions themselves.

### 2.4 Packer Detection

Packer/protector detection for APKs uses **DetectItEasy (DIE)**, consistent with how packer detection works for PE/ELF/Mach-O in the existing pipeline. DIE already has signatures for common Android packers (Qihoo 360, Bangcle, Ijiami, Tencent Legu, Baidu, etc.).

The existing `DIEExtractor` runs as a format-agnostic extractor before format-specific analysis and requires no changes.

---

## 3. Architecture

### 3.1 Extractor Class Hierarchy

```
Extractor (redb/extractors/extractor.py)
└── DecompileAPK (NEW — redb/extractors/decompiler/DecompileAPK.py)
    ├── Uses: APKCodeAnalyzer (NEW — redb/extractors/decompiler/apk/analyzer.py)
    │   ├── AndroguardAnalysis — call graphs, xrefs, method enumeration
    │   ├── JADXDecompiler — Java decompilation (subprocess)
    │   └── ApktoolDisassembler — smali extraction (subprocess)
    └── Produces: multi_table ClickHouse export (same pattern as DecompileBinja)
```

**Design rationale:** `DecompileAPK` extends `Extractor` directly (not `APKExtractor`) because it follows the `DecompileBinja` pattern — a standalone extractor with its own analysis engine, rather than an APK metadata extractor that shares a parsed `APK` object. The APK parsing object from androguard is used internally but not shared with other extractors.

### 3.2 Analysis Pipeline Flow

```
APK file
  │
  ├─[1]─► apktool d <apk> ─► smali files on disk (temp dir)
  │
  ├─[2]─► jadx <apk> --no-res ─► Java source files on disk (temp dir)
  │
  └─[3]─► androguard AnalyzeAPK() ─► Analysis object (in-memory)
              │
              ├── Method enumeration ──► filter library packages
              │
              ├── For each user method:
              │     ├── Read smali from apktool output [1]
              │     ├── Read Java source from JADX output [2]
              │     ├── Get xrefs from Analysis object [3]
              │     ├── Compute content hashes (SHA-256 of smali, SHA-256 of Java)
              │     ├── Compute similarity hashes (ssdeep, TLSH of smali)
              │     └── Emit content + reference records
              │
              └── Call graph export ──► caller/callee arrays per method
```

Steps [1], [2], and [3] run in parallel (apktool and JADX as subprocess, androguard in-process). All three must complete before per-method analysis begins.

### 3.3 Content/Reference Split Pattern

Following the Binary Ninja schema pattern exactly:

- **Content tables** — Keyed by `function_hash` (SHA-256 of the method content). Deduplicated: if two APKs share identical method code, only one content record exists.
- **Reference tables** — Keyed by `(sha256, method_hash)`. Links a specific binary to its methods. Contains per-binary metadata (method name, class, address, callers, callees, fuzzy hashes).

This is the same pattern as `code_binja_decompiled_functions_content` / `code_binja_decompiled_functions_references`.

### 3.4 Method-Level Hashing

Hashing is computed at the **method level** for consistency with the Binary Ninja pipeline:

| Hash | Input | Purpose |
|------|-------|---------|
| `decompiled_method_hash` | SHA-256 of decompiled Java source (whitespace-normalized) | Content deduplication, exact match |
| `smali_method_hash` | SHA-256 of smali body (instructions only, no `.method`/`.end method` directives) | Content deduplication, exact match |
| `ssdeep_smali` | ssdeep of smali body | Fuzzy similarity search |
| `tlsh_smali` | TLSH of smali body | Fuzzy similarity search |
| `minhash_smali` | MinHash signature of smali instruction n-grams | LSH-based similarity clustering |

### 3.5 Obfuscation Indicators (per method)

Computed from the smali representation:

- `short_method_name` — Method name is <= 2 characters (a, b, c — typical R8/ProGuard output)
- `short_class_name` — Enclosing class has a single-letter name
- `has_string_encryption` — Method contains `const-string` followed by decryption-pattern calls
- `has_reflection_calls` — Method uses `java.lang.reflect.*` APIs
- `excessive_goto_count` — Number of `goto` instructions exceeds threshold (control flow flattening indicator)

---

## 4. Data Model

### 4.1 New Dataclasses

```python
@dataclass
class APKDecompiledMethodContent:
    """Decompiled Java source for a single method (content table — deduplicated by hash)."""
    decompiled_method_hash: str          # SHA-256 of normalized Java source
    decompiled_method: str               # Full Java method source
    method_type: str                     # "USER" or "LIBRARY"
    has_string_encryption: bool = False
    has_reflection_calls: bool = False
    excessive_goto_count: bool = False


@dataclass
class APKDecompiledMethodReference:
    """Links a specific APK to one of its decompiled methods (reference table)."""
    sha256: str                          # APK hash
    sha1: str
    md5: str
    decompiled_method_hash: str          # FK to content table
    smali_method_hash: Optional[str]     # FK to smali content table
    class_name: str                      # e.g., "com.example.MainActivity"
    method_name: str                     # e.g., "onCreate"
    method_signature: str                # e.g., "(Landroid/os/Bundle;)V"
    method_prototype: str                # e.g., "void onCreate(Bundle)"
    functions_caller: List[str]          # Methods that call this method
    functions_call: List[str]            # Methods called by this method


@dataclass
class APKSmaliMethodContent:
    """Smali disassembly for a single method (content table — deduplicated by hash)."""
    smali_method_hash: str               # SHA-256 of normalized smali body
    smali_method: str                    # Full smali method body
    method_type: str                     # "USER" or "LIBRARY"
    instructions_count: int = 0
    register_count: int = 0
    has_string_encryption: bool = False
    has_reflection_calls: bool = False
    excessive_goto_count: bool = False


@dataclass
class APKSmaliMethodReference:
    """Links a specific APK to one of its smali methods (reference table)."""
    sha256: str
    sha1: str
    md5: str
    smali_method_hash: str               # FK to content table
    decompiled_method_hash: Optional[str] # FK to decompiled content table
    class_name: str
    method_name: str
    method_signature: str
    ssdeep_smali: Optional[str] = None
    tlsh_smali: Optional[str] = None


@dataclass
class APKMethodSimilarityMetrics:
    """Similarity hashes for method-level clustering (keyed by smali hash)."""
    smali_method_hash: str
    cyclomatic_complexity: Optional[int] = None
    ssdeep_smali: Optional[str] = None
    tlsh_smali: Optional[str] = None
    minhash: Optional[List[int]] = None


@dataclass
class APKCodeAnalysisError:
    """Error encountered during method analysis."""
    sha256: str
    class_name: Optional[str] = None
    method_name: Optional[str] = None
    error_location: str = ""             # "jadx", "apktool", "androguard", "analysis"
    error_message: Optional[str] = None
    error_type: Optional[str] = None
```

### 4.2 Tag Enum Addition

```python
# In redb/extractors/enum.py
APK_DECOMPILED = "apk_decompiled"
```

### 4.3 ClickHouse Tables

| Table | Key | Pattern | Analog |
|-------|-----|---------|--------|
| `code_apk_decompiled_methods_content` | `decompiled_method_hash` | Content (deduplicated) | `code_binja_decompiled_functions_content` |
| `code_apk_decompiled_methods_references` | `(sha256, decompiled_method_hash)` | Reference (per-binary) | `code_binja_decompiled_functions_references` |
| `code_apk_smali_methods_content` | `smali_method_hash` | Content (deduplicated) | `code_binja_disassembled_functions_content` |
| `code_apk_smali_methods_references` | `(sha256, smali_method_hash)` | Reference (per-binary) | `code_binja_disassembled_functions_references` |
| `code_apk_method_similarity_metrics` | `smali_method_hash` | Similarity | `code_binja_function_similarity_metrics` |
| `code_apk_analysis_errors` | `(sha256, class_name, method_name)` | Errors | `function_analysis_errors_binja` |

All tables use `ReplacingMergeTree(analysis_date)` engine, consistent with existing schema.

---

## 5. External Tool Management

### 5.1 JADX

- **Invocation:** `jadx --no-res --no-imports --threads-count 2 --output-dir <tmpdir> <apk_path>`
- **Flags:**
  - `--no-res` — Skip resource decompilation (androguard handles resources)
  - `--no-imports` — Omit import statements for cleaner per-method extraction
  - `--threads-count 2` — Limit threads (same as Binary Ninja worker thread limit)
- **Output:** Java source files in `<tmpdir>/<package>/<Class>.java`
- **Timeout:** Configurable via `JADX_TIMEOUT` env var (default: 600s)
- **Path:** Configurable via `JADX_PATH` env var (default: `jadx`)
- **Error handling:** If JADX fails for a specific APK, the decompiled content tables are skipped but smali analysis continues. Error logged to `code_apk_analysis_errors`.

### 5.2 apktool

- **Invocation:** `apktool d --no-res --force --output <tmpdir> <apk_path>`
- **Flags:**
  - `--no-res` — Skip resource decoding (only want smali)
  - `--force` — Overwrite output directory if exists
- **Output:** Smali files in `<tmpdir>/smali/com/example/ClassName.smali` (one per class, containing all methods)
- **Timeout:** Configurable via `APKTOOL_TIMEOUT` env var (default: 600s)
- **Path:** Configurable via `APKTOOL_PATH` env var (default: `apktool`)
- **Error handling:** Same as JADX — if apktool fails, smali content tables are skipped but decompiled Java analysis continues. Error logged.

### 5.3 Androguard

- **Invocation:** Direct Python API — `AnalyzeAPK(filepath)` returns `(APK, list[DEX], Analysis)`
- **The `Analysis` object provides:**
  - `get_methods()` — All `MethodAnalysis` objects
  - `get_call_graph()` — networkx `MultiDiGraph` of method calls
  - `MethodAnalysis.get_xref_from()` — Who calls this method
  - `MethodAnalysis.get_xref_to()` — What this method calls
  - `MethodAnalysis.get_method()` — Access to `EncodedMethod` for bytecode
- **No timeout needed** — runs in-process, same Python process

---

## 6. Ingestor Integration

In `workers.py:process_binary_file()`, the APK branch will be extended to run `DecompileAPK` after the existing APK extractors:

```python
# Existing APK extractors (from APK_FEATURES_PDD.md)
for module in apk_modules:
    extractor = module(filepath, logger, exporters=exporters, ...)
    extractor.export_data()

# NEW: Code analysis (this PDD)
if "DecompileAPK" in selected_modules or "all" in selected_modules:
    decompiler = DecompileAPK(
        filepath, logger, exporters=exporters,
        index_prefix=index_prefix, filetype="apk",
    )
    decompiler.export_data()
```

The `DecompileAPK` extractor runs with its own timeout (configurable via `APK_DECOMPILE_TIMEOUT`, default: 1800s) using the same daemon-thread pattern as `DecompileBinja`.

---

## 7. New Dependencies

### 7.1 Required (system-level)

| Tool | Installation | Version | License | Purpose |
|------|-------------|---------|---------|---------|
| **JADX** | System package or download from GitHub releases | >= 1.5 | Apache 2.0 | Java decompilation |
| **apktool** | System package or download from GitHub releases | >= 2.9 | Apache 2.0 | Smali disassembly |
| **Java Runtime** | System package (`openjdk-17-jre` or similar) | >= 11 | GPL+CE | Required by JADX and apktool |

### 7.2 Required (Python — already installed)

| Library | Current Version | Usage in this PDD |
|---------|----------------|-------------------|
| `androguard` | >=4.1 | Call graphs, xrefs, method enumeration (already in requirements.txt) |
| `ppdeep` | installed | ssdeep fuzzy hashing (already used by Binary Ninja pipeline) |
| `py-tlsh` | installed | TLSH fuzzy hashing (already used by Binary Ninja pipeline) |
| `mmh3` | installed | MinHash computation (already used by Binary Ninja pipeline) |
| `networkx` | installed via androguard | Call graph representation (transitive dependency) |

### 7.3 No new Python dependencies required

All Python libraries needed are already in `requirements.txt`. The only new system-level dependencies are JADX, apktool, and a Java runtime.

---

## 8. Implementation Phases

### Phase 1 — Core Infrastructure

1. `APKCodeAnalyzer` class — androguard integration (method enumeration, call graph, xrefs, library filtering)
2. `JADXDecompiler` wrapper — subprocess management with timeout, output parsing
3. `ApktoolDisassembler` wrapper — subprocess management with timeout, smali parsing
4. Method-level content extraction and hashing logic
5. Unit tests for all Phase 1 components

### Phase 2 — Extractor and Data Export

6. `DecompileAPK` extractor class (following `DecompileBinja` pattern)
7. ClickHouse table creation functions
8. Multi-table export (`prepare_export_data`) for all 6 tables
9. Integration with `workers.py` dispatch
10. Unit tests for extractor, schema, and export
11. Update `TEST_INDEX.md`

### Phase 3 — Similarity and Obfuscation Analysis

12. Method-level similarity hash computation (ssdeep, TLSH, MinHash on smali)
13. Obfuscation indicator computation per method
14. `code_apk_method_similarity_metrics` table population
15. Unit tests for similarity and obfuscation
16. Update `TEST_INDEX.md`

### Phase 4 — Integration Testing and Hardening

17. End-to-end integration tests with real APK samples (benign + malicious + obfuscated)
18. Edge case handling: multi-DEX, empty DEX, packed APKs, APKs with no user code
19. Performance profiling and timeout tuning
20. Final `TEST_INDEX.md` update

---

## 9. Testing Strategy

### 9.1 Unit Tests

All unit tests mock external tools (JADX, apktool, androguard) and require no system dependencies:

- **Analyzer tests** — Method enumeration, library filtering, call graph extraction, xref parsing
- **JADX wrapper tests** — Subprocess invocation, output parsing, timeout handling, error recovery
- **Apktool wrapper tests** — Same as JADX
- **Hashing tests** — SHA-256 normalization, ssdeep/TLSH computation, MinHash signature generation
- **Extractor tests** — `DecompileAPK.extract()`, `prepare_export_data()`, multi-table schema validation
- **Smali parsing tests** — Method boundary detection, instruction extraction, register counting

### 9.2 Integration Tests

Require JADX, apktool, and Java installed:

- Full pipeline run on known APK samples
- Cross-validate decompiled output against known method signatures
- Verify ClickHouse export column counts and types
- Test with obfuscated APKs (ProGuard/R8 output)

### 9.3 Markers

```python
@pytest.mark.apk           # All APK tests
@pytest.mark.decompile      # All decompiler tests
@pytest.mark.unit           # No external deps
@pytest.mark.integration    # Requires JADX/apktool/Java
```

---

## 10. Configuration

All configuration via environment variables, consistent with existing extractors:

| Variable | Default | Description |
|----------|---------|-------------|
| `JADX_PATH` | `jadx` | Path to JADX binary |
| `JADX_TIMEOUT` | `600` | JADX subprocess timeout (seconds) |
| `APKTOOL_PATH` | `apktool` | Path to apktool binary |
| `APKTOOL_TIMEOUT` | `600` | apktool subprocess timeout (seconds) |
| `APK_DECOMPILE_TIMEOUT` | `1800` | Overall decompilation timeout (seconds) |
| `APK_LIBRARY_PREFIXES` | (see §2.3) | Comma-separated package prefixes to filter |
| `APK_MIN_METHOD_INSTRUCTIONS` | `5` | Minimum smali instruction count to analyze a method |

---

## 11. Open Questions / Future Work

1. **ProGuard/R8 mapping file support** — If mapping files are bundled (rare in malware, common in crash reports), JADX can use them to restore original names. Deferred.
2. **Kotlin-specific analysis** — Kotlin metadata annotations could provide richer type information. Deferred.
3. **Cross-DEX analysis** — Multi-DEX APKs may have cross-DEX method calls. Androguard handles this via `AnalyzeAPK()` which loads all DEX files into a single `Analysis` object. No special handling needed.
4. **JADX as Java library via JPype** — Could eliminate subprocess overhead. Deferred in favor of the proven subprocess pattern, but may be revisited if performance is an issue.

---

## Appendix A — CFG Feature Parity with Binary Ninja Pipeline

**Date:** 2026-03-13
**Status:** Planned (Phase 5)
**Depends on:** Phases 13 complete

### A.1 Motivation

The Binary Ninja pipeline produces a dedicated `code_binja_cfg_functions` table with 17 fields capturing graph topology, structural hashes, and per-block feature vectors. The current APK pipeline computes only basic graph scalars (`block_count`, `edge_count`, `cyclomatic_complexity`, `loop_count`, `max_depth`, `max_fan_out`) and bundles them into the `code_apk_method_similarity_metrics` table alongside fuzzy hashes.

Analysis shows that **all advanced CFG features can be computed from smali** — this is not a limitation of Java bytecode. The existing APK infrastructure already:

- Builds `successors[]` adjacency lists from smali control flow (`smali_cfg.py`)
- Computes per-block ACFG feature vectors using the same 8-category schema as Binary Ninja (`smali_cfg.py:_build_block_features`)
- Normalizes Dalvik opcodes into semantic categories equivalent to LLIL categories (`smali_normalization.py`)

The generic functions in `cfg_features.py` (`compute_topology_hash`, `compute_md_index_topdown/bottomup`, `compute_wl_minhash`, `compute_cfg_feature_tlsh`, `pack_adjacency`) operate on adjacency lists and block feature arrays — they have no Binary Ninja dependency and can be called directly from the APK pipeline.

### A.2 Table Restructuring

Split the current single table into two, mirroring the Binja pattern:

#### `code_apk_method_similarity_metrics` (content-based fuzzy matching)

Retains only fuzzy hashes and instruction-sequence similarity data:

| Column | Type | Change |
|--------|------|--------|
| `smali_method_hash` | FixedString(64) | Unchanged |
| `cyclomatic_complexity` | Nullable(UInt16) | Stays (duplicated in both, same as Binja) |
| `ssdeep_smali` | Nullable(String) | Unchanged |
| `tlsh_smali` | Nullable(FixedString(72)) | Unchanged |
| `minhash` | Array(UInt8) | Unchanged |
| `analysis_date` | DateTime64(3, 'UTC') | Unchanged |

Removed from this table: `block_count`, `edge_count`, `loop_count`, `max_depth`, `max_fan_out` — these move to the CFG table.

#### `code_apk_cfg_methods` (NEW — structural/topological similarity)

Mirrors `code_binja_cfg_functions`:

| Column | Type | Source | Analog in Binja |
|--------|------|--------|-----------------|
| `smali_method_hash` | FixedString(64) | Existing | `disassembled_function_hash` |
| `cfg_topology_hash` | FixedString(16) | NEW — `cfg_features.compute_topology_hash()` | Same |
| `block_count` | UInt16 | Moved from similarity table | Same |
| `edge_count` | UInt16 | Moved from similarity table | Same |
| `instructions_count` | UInt32 | Existing (total Dalvik instructions) | `llil_total_operations` |
| `call_count` | UInt16 | NEW — count of `invoke-*` instructions | Same |
| `cyclomatic_complexity` | UInt16 | Moved from similarity table | Same |
| `loop_count` | UInt8 | Moved from similarity table | Same |
| `max_depth` | UInt16 | Moved from similarity table | Same |
| `max_fan_out` | UInt8 | Moved from similarity table | Same |
| `md_index_topdown` | UInt64 | NEW — `cfg_features.compute_md_index_topdown()` | Same |
| `md_index_bottomup` | UInt64 | NEW — `cfg_features.compute_md_index_bottomup()` | Same |
| `prime_product_smali` | UInt64 | NEW — Dalvik opcode → prime mapping | `prime_product_llil` |
| `cfg_feature_tlsh` | Nullable(FixedString(72)) | NEW — `cfg_features.compute_cfg_feature_tlsh()` | Same |
| `wl_minhash` | Array(UInt8) | NEW — `cfg_features.compute_wl_minhash()`, 128 elements | Same |
| `bb_features` | Array(Array(UInt16)) | Existing (computed, not exported) | Same |
| `cfg_adjacency` | Array(UInt32) | NEW — `cfg_features.pack_adjacency()` | Same |
| `analysis_date` | DateTime64(3, 'UTC') | | Same |

### A.3 Naming Differences from Binja

Two columns are intentionally renamed to reflect what the data actually represents:

- **`prime_product_smali`** (not `prime_product_llil`) — the prime mapping is applied to normalized Dalvik opcodes, not LLIL. Semantically equivalent for APK-to-APK comparison but not numerically comparable to Binja values.
- **`instructions_count`** (not `llil_total_operations`) — counts Dalvik instructions, not LLIL operations. LLIL decomposes machine instructions into sub-operations; Dalvik bytecode is already at a higher abstraction level where one instruction ≈ one operation.

### A.4 Implementation Requirements

| Task | Effort | Notes |
|------|--------|-------|
| Build `predecessors[]` from `successors[]` in `smali_cfg.py` | ~5 lines | Trivial reverse mapping |
| Define `SMALI_OP_PRIMES` mapping | ~30 lines | Map semantic categories from `smali_normalization.py` to same primes used in `cfg_features.py` |
| Wire `cfg_features.py` functions into `smali_cfg.py` | ~40 lines | Call `compute_topology_hash`, `compute_md_index_*`, `compute_wl_minhash`, `compute_cfg_feature_tlsh`, `pack_adjacency` |
| Export `bb_features` (already computed, not exported) | ~5 lines | Add to results dict |
| Count `invoke-*` instructions for `call_count` | ~5 lines | Filter in instruction loop |
| New `code_apk_cfg_methods` table export in `DecompileAPK.py` | ~60 lines | Follow existing export pattern |
| Slim down `code_apk_method_similarity_metrics` export | ~10 lines | Remove moved columns |
| ClickHouse schema for new table | ~30 lines | Mirror `code_binja_cfg_functions` |
| Unit tests | ~100 lines | Test new fields, reuse patterns from `test_cfg_features.py` |

**Total estimated: ~285 lines of code changes.**

### A.5 What Cannot Be Identical Cross-Platform

The `prime_product_smali` values are **not numerically comparable** to `prime_product_llil` from the Binja pipeline. LLIL decomposes native instructions into sub-operations (e.g., one x86 `push` becomes `STORE` + `SET_REG`), while Dalvik bytecode maps 1:1 to semantic categories. The prime products are valid for APK-vs-APK similarity and APK-vs-APK clustering, which is the intended use case.

All other fields (`cfg_topology_hash`, `md_index_*`, `wl_minhash`, `cfg_feature_tlsh`, `bb_features`, `cfg_adjacency`) are computed from the same generic algorithms and are structurally equivalent across platforms.