Jae W. Lee

127 papers A* 40A 14B 9C 3Misc 4Journal 42Unranked 15
YearRankTypeTitle / Venue / Authors
2026 conf
ASPLOS (2)
Donghyun Lee, Dawoon Jeong, Jae W. Lee, Hongil Yoon
2026 J jnl
CoRR
Omin Kwon, Yeonjae Kim, Doyeon Kim, Minseo Kim, Yeonhong Park, Jae W. Lee
2026 A* conf
HPCA
Junghoon Kim, Jongheon Jeong, Seokwon Moon, Seong Hoon Seo, Yeonhong Park, Jinkyu Jeong, Nam Sung Kim, Jae W. Lee
2025 J jnl
IEEE Comput. Archit. Lett.
Kyungsoo Kim, Omin Kwon, Yeonhong Park, Jae W. Lee
2025 A* conf
WWW
Sungjun Jung, Yongsang Park, Haeun Lee, Young H. Oh, Jae W. Lee
2025 B conf
PACT
Keun Soo Lim, Yunjay Hong, Jongheon Jeong, Sam Son, Donguk Kim, Yeonhong Park, Jae W. Lee, Jinkyu Jeong
2025 J jnl
CoRR
Sangwoo Kwon, Seong Hoon Seo, Jae W. Lee, Yeonhong Park
2025 A* conf
OSDI
Yeonhong Park, Jake Hyun, Hojoon Kim, Jae W. Lee
2025 A* conf
HPCA
Seong Hoon Seo, Junghoon Kim, Donghyun Lee, Seonah Yoo, Seokwon Moon, Yeonhong Park, Jae W. Lee
2025 J jnl
CoRR
Donghyun Lee, Dawoon Jeong, Jae W. Lee, Hongil Yoon
2025 A* conf
ICML
Seung Yul Lee, Hojoon Kim, Yutack Park, Dawoon Jeong, Seungwu Han, Yeonhong Park, Jae W. Lee
2025 J jnl
CoRR
Donghyun Lee, Dawoon Jeong, Jae W. Lee, Hongil Yoon
2025 A* conf
ICML
Jinuk Kim, Marwa El Halabi, Wonpyo Park, Clemens J. S. Schaefer, Deokjae Lee, Yeonhong Park, Jae W. Lee, Hyun Oh Song
2025 J jnl
CoRR
Jinuk Kim, Marwa El Halabi, Wonpyo Park, Clemens JS Schaefer, Deokjae Lee, Yeonhong Park, Jae W. Lee, Hyun Oh Song
2025 A conf
CIKM
Bonggeun Sim, Yushin Kim, Minseo Kim, Yeonhong Park, Jae W. Lee
2025 J jnl
CoRR
Jang-Hyun Kim, Jinuk Kim, Sangwoo Kwon, Jae W. Lee, Sangdoo Yun, Hyun Oh Song
2025 J jnl
CoRR
Haeun Lee, Omin Kwon, Yeonhong Park, Jae W. Lee
2025 Misc conf
SAC
Jongsung Lee, Sam Son, Jonghyun Bae, Yunho Jin, Tae Jun Ham, Jae W. Lee
2025 Misc conf
SAC
Gyusun Lee, Jinyong Park, Jae W. Lee, Jinkyu Jeong
2024 A conf
ICCAD
Hoon Shin, Rihae Park, Jae W. Lee
2024 J jnl
IEEE Comput. Archit. Lett.
Dongho Yoon, Taehun Kim, Jae W. Lee, Minsoo Rhu
2024 J jnl
ACM Trans. Storage
Donguk Kim, Jongsung Lee, Keun Soo Lim, Jun Heo, Tae Jun Ham, Jae W. Lee
2024 A* conf
ICML
Yeonhong Park, Jake Hyun, SangLyul Cho, Bonggeun Sim, Jae W. Lee
2024 J jnl
CoRR
Yeonhong Park, Jake Hyun, SangLyul Cho, Bonggeun Sim, Jae W. Lee
2024 conf
ECCV (66)
Donghyun Lee, Yejin Lee, Jae W. Lee, Hongil Yoon
2024 C ed.
ISMM
Michael D. Bond, Jae W. Lee, Hannes Payer
2024 J jnl
CoRR
Yeonhong Park, Jake Hyun, Hojoon Kim, Jae W. Lee
2024 conf
ACCV (8)
Soosung Kim, Yeonhong Park, Hyunseung Lee, Sungchan Yi, Jae W. Lee
2024 A* conf
MICRO
Seung Yul Lee, Hyunseung Lee, Jihoon Hong, SangLyul Cho, Jae W. Lee
2023 J jnl
IEEE Trans. Circuits Syst. II Express Briefs
Jongho Kim, Young H. Oh, Hyeonsik Kim, Jae W. Lee, Jintae Kim
2023 A* conf
DAC
Hyunseung Lee, Jihoon Hong, Soosung Kim, Seung Yul Lee, Jae W. Lee
2023 A* conf
ISCA
Wenjing Jin, Wonsuk Jang, Haneul Park, Jongsung Lee, Soosung Kim, Jae W. Lee
2023 A conf
EuroSys
Gyewon Lee, Jaewoo Maeng, Jinsol Park, Jangho Seo, Haeyoon Cho, Youngseok Yang, Taegeon Um, Jongsung Lee, Jae W. Lee, Byung-Gon Chun
2023 conf
APSys
Woohyeon Baek, Jonghyun Bae, Donghyun Lee, Hyunwoong Bae, Yeonhong Park, Jae W. Lee
2023 J jnl
ACM Trans. Embed. Comput. Syst.
Deok-Jae Oh, Yaebin Moon, Do Kyu Ham, Tae Jun Ham, Yongjun Park, Jae W. Lee, Jung Ho Ahn, Eojin Lee
2023 A* conf
AAAI
Yejin Lee, Donghyun Lee, Junguk Hong, Jae W. Lee, Hongil Yoon
2023 J jnl
IEEE Micro
Christopher Batten, Jae W. Lee
2023 J jnl
Proc. VLDB Endow.
Jongsung Lee, Dong Uk Kim, Jae W. Lee
2022 conf
ESSCIRC
Seong Hoon Seo, Soosung Kim, Sung Jun Jung, Sangwoo Kwon, Hyunseung Lee, Jae W. Lee
2022 conf
ICEIC
Shine Kim, Jae W. Lee
2022 A* conf
HPCA
Yejin Lee, Hyunji Choi, Sunhong Min, Hyunseung Lee, Sangwon Beak, Dawoon Jeong, Jae W. Lee, Tae Jun Ham
2022 J jnl
ACM Trans. Embed. Comput. Syst.
Dongsuk Shin, Hakbeom Jang, Kiseok Oh, Jae W. Lee
2022 J jnl
IEEE Trans. Computers
Yunho Jin, Shine Kim, Tae Jun Ham, Jae W. Lee
2022 A* conf
DAC
Hoon Shin, Rihae Park, Seung Yul Lee, Yeonhong Park, Hyunseung Lee, Jae W. Lee
2022 J jnl
CoRR
Yeonhong Park, Sunhong Min, Jae W. Lee
2022 J jnl
Proc. VLDB Endow.
Yeonhong Park, Sunhong Min, Jae W. Lee
2022 A ed.
CGO
Jae W. Lee, Sebastian Hack, Tatiana Shpeisman
2022 conf
ECCV (11)
Jonghyun Bae, Woohyeon Baek, Tae Jun Ham, Jae W. Lee
2022 J jnl
CoRR
Jonghyun Bae, Woohyeon Baek, Tae Jun Ham, Jae W. Lee
2022 J jnl
IEICE Trans. Inf. Syst.
Young H. Oh, Yunho Jin, Tae Jun Ham, Jae W. Lee
2022 A* conf
HPCA
Michael Jaemin Kim, Jaehyun Park, Yeonhong Park, Wanju Doh, Namhoon Kim, Tae Jun Ham, Jae W. Lee, Jung Ho Ahn
2022 conf
MLSys
Jaeyeon Won, Jeyeon Si, Sam Son, Tae Jun Ham, Jae W. Lee
2021 conf
USENIX ATC
Sam Son, Seung Yul Lee, Yunho Jin, Jonghyun Bae, Jinkyu Jeong, Tae Jun Ham, Jae W. Lee, Hongil Yoon
2021 J jnl
IEEE Micro
Tae Jun Ham, Yejin Lee, Seong Hoon Seo, U. Gyeong Song, Jae W. Lee, David Bruns-Smith, Brendan Sweeney, Krste Asanovic, Young H. Oh, Lisa Wu Wills
2021 J jnl
IEEE Access
Jongho Kim, Beomkyu Seo, Young H. Oh, Jung-Hoon Chun, Jae W. Lee, Jintae Kim
2021 A* conf
ISCA
Jun Heo, Seung Yul Lee, Sunhong Min, Yeonhong Park, Sungjun Jung, Tae Jun Ham, Jae W. Lee
2021 A conf
FAST
Shine Kim, Yunho Jin, Gina Sohn, Jonghyun Bae, Tae Jun Ham, Jae W. Lee
2021 A* conf
ISCA
Tae Jun Ham, Yejin Lee, Seong Hoon Seo, Soosung Kim, Hyunji Choi, Sung Jun Jung, Jae W. Lee
2021 A conf
FAST
Jonghyun Bae, Jongsung Lee, Yunho Jin, Sam Son, Shine Kim, Hakbeom Jang, Tae Jun Ham, Jae W. Lee
2021 A ed.
CGO
Jae W. Lee, Mary Lou Soffa, Ayal Zaks
2021 A* conf
HPCA
Young H. Oh, Seonghak Kim, Yunho Jin, Sam Son, Jonghyun Bae, Jongsung Lee, Yeonhong Park, Dong Uk Kim, Tae Jun Ham, Jae W. Lee
2021 A* conf
ASPLOS
Yejin Lee, Seong Hoon Seo, Hyunji Choi, Hyoung Uk Sul, Soosung Kim, Jae W. Lee, Tae Jun Ham
2021 B conf
LCTES
Deok-Jae Oh, Yaebin Moon, Eojin Lee, Tae Jun Ham, Yongjun Park, Jae W. Lee, Jung Ho Ahn
2021 A conf
CGO
Jae W. Lee
2021 J jnl
CoRR
Michael Jaemin Kim, Jaehyun Park, Yeonhong Park, Wanju Doh, Namhoon Kim, Tae Jun Ham, Jae W. Lee, Jung Ho Ahn
2020 A* conf
HPCA
Tae Jun Ham, Sungjun Jung, Seonghak Kim, Young H. Oh, Yeonhong Park, Yoonho Song, Jung-Hun Park, Sanghee Lee, Kyoung Park, Jae W. Lee, Deog-Kyoon Jeong
2020 J jnl
CoRR
Tae Jun Ham, Sungjun Jung, Seonghak Kim, Young H. Oh, Yeonhong Park, Yoonho Song, Jung-Hun Park, Sanghee Lee, Kyoung Park, Jae W. Lee, Deog-Kyoon Jeong
2020 A* conf
ISCA
Gyusun Lee, Wenjing Jin, Wonsuk Song, Jeonghun Gong, Jonghyun Bae, Tae Jun Ham, Jae W. Lee, Jinkyu Jeong
2020 A* conf
ISCA
Jaeyoung Jang, Sungjun Jung, Sunmin Jeong, Jun Heo, Hoon Shin, Tae Jun Ham, Jae W. Lee
2020 A* conf
ISCA
Tae Jun Ham, David Bruns-Smith, Brendan Sweeney, Yejin Lee, Seong Hoon Seo, U. Gyeong Song, Young H. Oh, Krste Asanovic, Jae W. Lee, Lisa Wu Wills
2020 A* conf
MICRO
Yeonhong Park, Woosuk Kwon, Eojin Lee, Tae Jun Ham, Jung Ho Ahn, Jae W. Lee
2020 A* conf
ASPLOS
Jun Heo, Jaeyeon Won, Yejin Lee, Shivam Bharuka, Jaeyoung Jang, Tae Jun Ham, Jae W. Lee
2020 A conf
ICCAD
Yeonhong Park, Seung Yul Lee, Hoon Shin, Jun Heo, Tae Jun Ham, Jae W. Lee
2019 conf
USENIX ATC
Gyusun Lee, Seokha Shin, Wonsuk Song, Tae Jun Ham, Jae W. Lee, Jinkyu Jeong
2019 A* conf
MICRO
Jaeyoung Jang, Jun Heo, Yejin Lee, Jaeyeon Won, Seonghak Kim, Sungjun Jung, Hakbeom Jang, Tae Jun Ham, Jae W. Lee
2019 J jnl
IEICE Trans. Inf. Syst.
Hakbeom Jang, Jonghyun Bae, Tae Jun Ham, Jae W. Lee
2019 B conf
PACT
Jongwook Chung, Yuhwan Ro, Joonsung Kim, Jaehyung Ahn, Jangwoo Kim, John Kim, Jae W. Lee, Jung Ho Ahn
2019 conf
USENIX ATC
Shine Kim, Jonghyun Bae, Hakbeom Jang, Wenjing Jin, Jeonghun Gong, Seungyeon Lee, Tae Jun Ham, Jae W. Lee
2019 J jnl
IEEE Micro
Jonghyun Bae, Hakbeom Jang, Jeonghun Gong, Wenjing Jin, Shine Kim, Jaeyoung Jang, Tae Jun Ham, Jinkyu Jeong, Jae W. Lee
2018 B conf
PACT
Young H. Oh, Quan Quan, Daeyeon Kim, Seonghak Kim, Jun Heo, Sungjun Jung, Jaeyoung Jang, Jae W. Lee
2018 C conf
ICCE
Dongsuk Shin, Jae W. Lee
2018 J jnl
IEICE Electron. Express
Dongsuk Shin, Hakbeom Jang, Jae W. Lee
2017 conf
GLOBECOM Workshops
Yong I. Choi, Jae W. Lee, Chung Gu Kang, Minjoong Rim
2017 Misc conf
HiPC
Changsu Kim, Juhyun Kim, Juwon Kang, Jae W. Lee, Hanjun Kim
2017 J jnl
IEICE Electron. Express
Yongjun Lee, Yunkeuk Kim, Jinkyu Jeong, Jae W. Lee
2017 J jnl
IEICE Electron. Express
Dongsuk Shin, Hakbeom Jang, Jae W. Lee
2017 J jnl
IEEE Comput. Archit. Lett.
WonJun Song, Hyungjoon Jung, Jung Ho Ahn, Jae W. Lee, John Kim
2017 A* conf
ASPLOS
WonJun Song, Gwangsun Kim, Hyungjoon Jung, Jongwook Chung, Jung Ho Ahn, Jae W. Lee, John Kim
2017 conf
IEEE BigData
Jonghyun Bae, Hakbeom Jang, Wenjing Jin, Jun Heo, Jaeyoung Jang, Joo Young Hwang, Sangyeun Cho, Jae W. Lee
2017 J jnl
IEEE Trans. Wirel. Commun.
Yong I. Choi, Jae W. Lee, Minjoong Rim, Chung Gu Kang
2017 J jnl
IEEE Comput. Archit. Lett.
Young Hoon Son, Hyunyoon Cho, Yuhwan Ro, Jae W. Lee, Jung Ho Ahn
2017 A* conf
HPCA
Yuhwan Ro, Hyunyoon Cho, Eojin Lee, Daejin Jung, Young Hoon Son, Jung Ho Ahn, Jae W. Lee
2017 A* conf
ASPLOS
Channoh Kim, Jaehyeok Kim, Sungmin Kim, Doo-young Kim, Namho Kim, Gitae Na, Young H. Oh, Hyeon-Gyu Cho, Jae W. Lee
2016 J jnl
IEEE Des. Test
Donghwan Jeong, Young H. Oh, Jae W. Lee, Yongjun Park
2016 A* conf
HPCA
Hakbeom Jang, Yongjun Lee, Jongwon Kim, Youngsok Kim, Jangwoo Kim, Jinkyu Jeong, Jae W. Lee
2016 A* conf
ISCA
Channoh Kim, Sungmin Kim, Hyeon-Gyu Cho, Doo-young Kim, Jaehyeok Kim, Young H. Oh, Hakbeom Jang, Jae W. Lee
2016 B conf
PACT
Jialu Huang, Prakash Prabhu, Thomas B. Jablin, Soumyadeep Ghosh, Sotiris Apostolakis, Jae W. Lee, David I. August
2016 J jnl
IEEE Trans. Parallel Distributed Syst.
Jae Young Jang, Hao Wang, Euijin Kwon, Jae W. Lee, Nam Sung Kim
2015 A* conf
ISCA
Yongjun Lee, Jongwon Kim, Hakbeom Jang, Hyunggyun Yang, Jangwoo Kim, Jinkyu Jeong, Jae W. Lee
2015 J jnl
IEEE Trans. Consumer Electron.
Doo-young Kim, Jin Min Kim, Hakbeom Jang, Jinkyu Jeong, Jae W. Lee
2015 B conf
PPoPP
Xianglan Piao, Channoh Kim, Younghwan Oh, Huiying Li, Jincheon Kim, Hanjun Kim, Jae W. Lee
2014 conf
WWW (Companion Volume)
Xianglan Piao, Channoh Kim, Younghwan Oh, Hanjun Kim, Jae W. Lee
2014 J jnl
IEEE Trans. Computers
Gwangsun Kim, Michael Mihn-Jong Lee, John Kim, Jae W. Lee, Dennis Abts, Michael R. Marty
2014 A conf
SC
Young Hoon Son, Seongil O, Hyunggyun Yang, Daejin Jung, Jung Ho Ahn, John Kim, Jangwoo Kim, Jae W. Lee
2014 A conf
ISLPED
Wonjun Lee, Channoh Kim, Houp Song, Jae W. Lee
2014 A* conf
CCS
WonJun Song, John Kim, Jae W. Lee, Dennis Abts
2014 A conf
ISLPED
Kyungsang Cho, Yongjun Lee, Young H. Oh, Gyoo-Cheol Hwang, Jae W. Lee
2013 A* conf
ASPLOS
Taewook Oh, Hanjun Kim, Nick P. Johnson, Jae W. Lee, David I. August
2013 B conf
LCTES
Hakbeom Jang, Channoh Kim, Jae W. Lee
2013 A* conf
ISCA
Young Hoon Son, Seongil O, Yuhwan Ro, Jae W. Lee, Jung Ho Ahn
2012 A conf
CGO
Hanjun Kim, Nick P. Johnson, Jae W. Lee, Scott A. Mahlke, David I. August
2012 J jnl
Int. J. Parallel Program.
Yun Zhang, Jae W. Lee, Nick P. Johnson, David I. August
2012 Misc conf
CASES
Arun Raman, Jae W. Lee, David I. August
2012 J jnl
J. Parallel Distributed Comput.
Jae W. Lee, Man Cheuk Ng, Krste Asanovic
2012 A* conf
PLDI
Arun Raman, Ayal Zaks, Jae W. Lee, David I. August
2012 A conf
CGO
Yun Zhang, Soumyadeep Ghosh, Jialu Huang, Jae W. Lee, Scott A. Mahlke, David I. August
2011 A* conf
PLDI
Arun Raman, Hanjun Kim, Taewook Oh, Jae W. Lee, David I. August
2010 B conf
PACT
Michael Mihn-Jong Lee, John Kim, Dennis Abts, Michael R. Marty, Jae W. Lee
2010 B conf
PACT
Yun Zhang, Jae W. Lee, Nick P. Johnson, David I. August
2010 A* conf
MICRO
Michael Mihn-Jong Lee, John Kim, Dennis Abts, Michael R. Marty, Jae W. Lee
2010 A* conf
MICRO
Hanjun Kim, Arun Raman, Feng Liu, Jae W. Lee, David I. August
2008 A* conf
ISCA
Jae W. Lee, Man Cheuk Ng, Krste Asanovic
2007 C conf
ICCD
Jae W. Lee, Myron King, Krste Asanovic
2006 conf
IEEE Real Time Technology and Applications Symposium
Jae W. Lee, Krste Asanovic
2005 J jnl
IEEE Trans. Very Large Scale Integr. Syst.
Daihyun Lim, Jae W. Lee, Blaise Gassend, G. Edward Suh, Marten van Dijk, Srinivas Devadas
2004 A* conf
ASPLOS
G. Edward Suh, Jae W. Lee, David Zhang, Srinivas Devadas
2002 J jnl
IEEE Micro
Michael B. Taylor, Jason Sungtae Kim, Jason E. Miller, David Wentzlaff, Fae Ghodrat, Ben Greenwald, Henry Hoffmann, Paul R. Johnson, Jae W. Lee, Walter Lee, Albert Ma, Arvind Saraf, Mark Seneski, Nathan Shnidman, Volker Strumpen, Matthew I. Frank, Saman P. Amarasinghe, Anant Agarwal
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.