IRedDragonICY/AOSP-Automated-Compilation-Artifacts

Experimental Dataset: Automated AOSP Compilation and Dependency Resolution Artifacts

Abstract

The compilation of the Android Open Source Project (AOSP) involves complex dependency resolution, extensive hardware-specific patching, and massive computational overhead. This repository, AOSP-Automated-Compilation-Artifacts, hosts the output artifacts (synthetic datasets) generated from an experimental automated build pipeline. The primary objective is to evaluate the structural integrity, runtime performance, and hardware compatibility of custom-compiled OS images across various embedded device architectures.

The datasets provided herein are fully compiled system images packaged in deployable .zip archives, intended for empirical evaluation on physical target hardware.

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1. System Architecture and Compilation Workflow

The underlying methodology of this research involves a heavily modified build environment designed to mitigate dependency conflicts and optimize the LLVM/Clang compiler toolchain.

Figure 1.1: Schematic representation of the automated OS compilation pipeline, detailing the integration of the Ninja/Soong orchestrator.

As illustrated in Figure 1.1, the workflow begins with the synchronization of the standard AOSP source tree. The source is subjected to an automated dependency resolution module which injects hardware-specific Vendor blobs, Device Trees (HAL), and Kernel patches. The compilation is subsequently handed over to the core build engine, which utilizes a Compiler Cache (CCache) and applies targeted compiler flags (-O3, Link-Time Optimization) before packaging the final OS artifact.

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2. Resource Utilization and Compilation Metrics

A critical aspect of this study is observing the resource utilization during the build process to ensure pipeline stability and prevent out-of-memory (OOM) fatal errors.

Figure 1.2: Time-series analysis of CPU workload and Memory allocation during the build process.

Figure 1.2 demonstrates the comparative efficiency of our modified build system. By caching intermediate build states and optimizing the dependency graph, the pipeline significantly reduces computational overhead. The datasets hosted in the Files directory are the direct results of this optimized state, allowing researchers to verify if the reduced compilation overhead compromises system stability.

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3. Subsystem Compilation Latency

To further validate the efficiency of the generated dataset, compilation latency was segmented based on the core OS subsystems.

Figure 1.3: Box-and-whisker plot illustrating the distribution of compilation latency across isolated OS subsystems.

Figure 1.3 illustrates that while standard AOSP suffers from high latency variance (represented by massive outliers in the System Framework compilation phase), the automated pipeline produces predictable and highly stable build times. The deployable artifacts represent the terminal output of this stabilized compilation vector.

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4. Dataset Archive (Output Artifacts)

The compiled OS artifacts are available in the repository's Files section. Each dataset corresponds to a specific hardware target. Evaluators must ensure they retrieve the artifact corresponding to the correct hardware codename.

Target Architecture (Codename)	OS Base Environment	Android Version	Compilation Date	Output File (.zip)
[device_codename_1]	[e.g., LineageOS]	[e.g., 14.0.0]	2026-06-14	Available in Files
[device_codename_2]	[e.g., PixelExperience]	[e.g., 14.0.0]	2026-06-14	Available in Files

(Note: Ensure you download the specific artifact associated with your hardware validation target).

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5. Empirical Evaluation Methodology (Hardware Deployment)

To conduct empirical testing of these datasets, the .zip artifacts must be deployed onto the target physical hardware. Evaluators are required to utilize a custom recovery environment capable of modifying block-level partitions (e.g., TWRP, OrangeFox Recovery).

Prerequisites for Evaluation:

• Target device with an unlocked bootloader state.
• Elevated deployment environment (Custom Recovery).

Deployment Procedure:

1. Initialize the device into the recovery environment.
2. Ensure data preservation by conducting a full NANDroid state backup.
3. Purge existing system data to prevent cross-contamination by executing formatting protocols on Dalvik/ART Cache, Cache, System, Vendor, and Data partitions.
4. (Conditional) If migrating from a disparate OS base, execute a full Format Data command to eradicate forced encryption artifacts.
5. Deploy the primary dataset artifact (the .zip archive downloaded from the Files directory).
6. (Optional) Deploy necessary auxiliary cryptographic or system modules (e.g., Google Mobile Services binaries or KernelSU for root-level debugging).
7. Terminate recovery mode and initiate the primary system boot sequence.

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6. Liability Disclaimer

This repository and its contents are provided strictly for research, benchmarking, and evaluation purposes.

Modification of embedded device firmware carries inherent risks, including but not limited to irreversible hardware failure ("bricking"), degradation of cryptographic keys (e.g., Widevine DRM L1), or loss of persistent user data. The maintainer of this repository assumes absolutely no liability for any hardware malfunction, data loss, thermal damage, or secondary damages resulting from the deployment of these experimental datasets. All hardware evaluators proceed entirely at their own risk.

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Maintainer: IRedDragonICY Research Focus: Operating System Compilation, Kernel Tuning, Hardware Abstraction Layer (HAL) Optimization.