AIPR: An Automated Instruction-Level Patching and Rewriting Framework for Sustainable RISC-V Research

Abstract

Computer systems research faces significant challenges in reproducibility because of toolchain fragmentation and the rapid evolution of the RISC-V ecosystem. Many research artifacts stay as ‘digital tombstones’ because they lack stable build environments and suffer from undocumented dependencies. This work presents the AIPR (Automated Instruction-level Patching and Rewriting) framework to address the gap between unstable hardware specifications and reproducible research. The methodology shifts the focus from complex source-level recompilation to direct executable-level modification. A three-stage pipeline automates instruction-level analysis, immediate reconstruction, and binary patching in ELF binaries. Experimental evaluations with the V-FRONT RISC-V processor include 2000 independent trials. These trials verify the functional robustness of the framework under complex architectural constraints. Furthermore, the AIPR framework achieves a 29.57× speedup in artifact generation compared to traditional GCC-based flows.

1. Introduction

In modern computer architecture and systems research, experimental verification is a fundamental methodology [1,2]. Most researchers validate their new architectural ideas using software-based simulators or FPGA-based prototypes [3,4,5,6]. This heavy reliance on digital artifacts leads to a greater emphasis on tool accessibility. Consequently, open-source platforms such as GitHub and GitLab facilitate the growth of `open science’ [7,8]. These environments provide the infrastructure for code sharing and collaborative research.

However, reproducing these results is often difficult in practice [9,10,11]. Code repositories in published papers often disappear over time [12]. Some repositories stay only as ‘digital tombstones,’ and these are artifacts that exist but researchers cannot build or execute them anymore. Even with available source code, researchers face obstacles such as undocumented dependencies, old toolchains, and hidden environmental assumptions [13].

This reproducibility gap was systematically quantified by Collberg et al., who examined the repeatability of computer systems research [9]. Their results revealed that fewer than one-third of surveyed papers could be successfully rebuilt within a realistic reuse window. These findings show a major problem in systems research; software artifacts are often treated as temporary tools instead of long-term scientific results [14].

The issue is especially serious in computer architecture research because experimental pipelines depend on complex toolchains [3,15]. Small changes in instruction set specifications, compilers, or simulators can render years of prior work unusable. Accordingly, researchers spend significant time just managing these environments instead of focusing on new architectural ideas. Modifying executable files is more practical than recompiling source code, which often fails due to toolchain issues, because it avoids a full system rebuild [16,17].

Based on these observations, this work is guided by the following research question:

• RQ: Can executable-level binary rewriting effectively mitigate reproducibility and sustainability challenges caused by toolchain fragmentation in RISC-V systems research, while maintaining functional robustness and performance efficiency?

Proposed strategies involve a shift in the intervention point from the source level to the executable level. AIPR (Automated Instruction-level Patching and Rewriting) serves as a binary-level transformation framework. The key advantage of AIPR revives the reusability of research artifacts even when reproduction remains impossible due to broken environments. By analyzing and rewriting instructions within compiled binaries, the framework allows for both rapid iteration and the continued use of legacy machine code streams. This process keeps architectural correctness and execution results without needing the original source code [18,19].

The main contributions of this work are summarized as follows:

• Executable-Level Methodology: This work shifts the experimental intervention point from fragile source-level recompilation to stable executable-level modification to improve research reproducibility under toolchain fragmentation.
• AIPR Framework: The AIPR framework automates instruction-level analysis, immediate reconstruction, and direct binary patching within ELF binaries.
• Instruction Encoding Automation: A specialized encoding engine ensures correct split-immediate recalculation and sign-extension handling during binary rewriting.
• Accelerated Verification: The proposed approach achieves a 29.57× speedup in artifact generation compared to GCC-based recompilation, significantly reducing verification turnaround time.

The rest of this paper is organized as follows. Section 2 reviews the background and related challenges underlying the proposed framework. Section 3 details the overall architecture and transactional workflow of the AIPR framework. The reliability and performance of the proposed framework are evaluated in Section 4. Finally, Section 5 concludes the work and suggests future research.

2. Background

2.1. Structural Barriers to Reproducibility in Systems Research

Reproducibility has become a persistent barrier in modern computer systems research as experimental pipelines increase in scale and complexity. Many studies depend on large and fragile software toolchains, and long-term reuse of research artifacts remains difficult even when the original methodology is sound. This challenge appears clearly in widely used infrastructures such as the gem5 simulator, which models detailed hardware behavior and relies on both C++ implementations and Python3.11 -based configuration layers [3]. The gem5 simulator is a common platform for architectural studies. However, frequent updates often prevent older research from running in newer environments.

Comparable reproducibility obstacles also arise in the RISC-V ecosystem, where rapid specification updates and extension revisions are common [20,21,22]. Extensibility represents a core strength of RISC-V, but incompatible toolchain versions and changes in compiler or kernel support regularly invalidate existing experimental setups. Researchers often face a choice between maintaining outdated environments or discontinuing follow-on studies. These structural difficulties reveal a structural gap between short-term experimental success and long-term reproducibility. This environment fosters the emergence of the Research Software Engineer (RSE) role, which applies software engineering practices to sustain executable research artifacts over time [14,23].

2.2. Binary Rewriting as an Alternative Intervention Point

Binary rewriting provides a practical way to modify executable files when source code is unavailable [16,17]. Researchers previously focused on security hardening and performance profiling with such techniques, but the scope now expands to general systems research. The process requires an analysis of control-flow structures and instruction encodings to ensure safe code insertion or replacement.

Recent studies improve binary-level transformation reliability and allow stable modifications with low overhead, even in complex execution environments [16,18]. Direct intervention at the executable level avoids the fragilities of source-level toolchains, so such an approach preserves program behavior and prevents build-time errors.

Such features establish binary rewriting as a strong base for architectural experiments because executable-level manipulation separates logic from unstable build environments [12]. Systematic isolation of the experimental layer ensures a reliable foundation for long-term research artifacts, and such benefits led to the AIPR framework design in this study.

3. AIPR Framework

3.1. AIPR Framework Implementation

The AIPR framework, implemented in Python, serves as a specialized engine for instruction-level analysis, immediate reconstruction, and direct binary patching. The system orchestrates a three-stage pipeline to transform high-level architectural parameters into physical binary modifications:

• ELF Parsing and Section Discovery: The engine parses the section header table to identify the offset and size of the .text section. This stage remains a prerequisite to ensure that all modifications occur strictly within executable boundaries.
• Instruction Injection and Encoding: Assembly patterns undergo conversion into 32-bit or 16-bit hexadecimal streams. This process requires a precise implementation of RISC-V immediate encoding logic [24,25].
• Direct Binary Patching: The framework overwrites the target byte stream at specific offsets. The current version excludes any modifications to the ELF header. This approach ensures file integrity and simulator compatibility. It also avoids complex structural recalculations.

The technical analysis focuses primarily on the Instruction Encoding Engine and the Binary Patching Engine. These modules manage the core algorithmic complexity, including immediate reconstruction and atomic hexadecimal stream manipulation. In contrast, the initial parsing stage utilizes standard structural analysis, so it performs a secondary role.

Table 1. Pseudocode of Instruction Encoding Engine for Assembly-to-Binary Transformation.

def calculate_riscv_immediates(self, target_value):          low_12 = target_value & 0xFFF          up_20 = (target_value >> 12) & 0xFFFFF          # Compensation for RISC-V ADDI sign-extension          if low_12 >= 0x800:                up_20 = (up_20 + 1) & 0xFFFFF          return up_20, low_12    def encode_u_type(self, imm_20, opcode=0x37):          # Format: imm[31:12] | rd[11:7] | opcode[6:0]          instr = (imm_20 << 12) | (self.rd << 7) | opcode          return instr    def encode_i_type(self, imm_12, rs1=None, funct3=0x0, opcode=0x13):          # Format: imm[11:0] | rs1[19:15] | funct3[14:12] | rd[11:7] | opcode[6:0]          if rs1 is None: rs1 = self.rd # Default to self-increment          instr = (imm_12 << 20) | (rs1 << 15) | (funct3 << 12) | (self.rd << 7) |                opcode          return instr    def generate_binary_pair(self, new_param):          up, low = self.calculate_riscv_immediates(new_param)          lui_bin = self.encode_u_type(up)          addi_bin = self.encode_i_type(low)          return lui_bin, addi_bin

and

Table 2. Pseudocode of Binary Rewriting Engine for Direct Hexadecimal Stream Manipulation.

def find_instruction_offset(self, original_hex_sequence):          target_bytes = bytes.fromhex(original_hex_sequence)          offset = self.binary_data.find(target_bytes)          if offset == -1:                raise ValueError("[AIPR_Error]Target_instruction_pattern_not_found.")          return offset    def apply_hex_patch(self, offset, patched_hex_sequence):          patch_bytes = bytes.fromhex(patched_hex_sequence)          with open(self.file_path, ’r+b’) as f:                f.seek(offset)                f.write(patch_bytes)                f.flush()                # Verification Logic                f.seek(offset)                if f.read(len(patch_bytes)) != patch_bytes:                      raise IOError("[AIPR_Error]Patch_verification_failed.")    def execute_automated_flow(self, search_pattern, replace_pattern):       self.load_binary()       try:           target_offset = self.find_instruction_offset(search_pattern)           self.apply_hex_patch(target_offset, replace_pattern)        except Exception as e:           print(f"Workflow_Interrupted:{e}")

present simplified Python-like pseudocode for clarity. The production implementation includes additional logic for relocation-aware offset resolution and transactional verification.

The implementation of the Instruction Encoding Engine, as detailed in Table 1, provides a high-level abstraction layer that isolates the developer from bit-level architectural specifics. The engine encapsulates complex bit-masking and shifting operations within specialized functions such as encode_u_type and encode_i_type. Such encapsulation ensures that architectural parameters map accurately to specific field positions—including rd, rs1, and funct3—defined by the RISC-V ISA. Such modularity enables the framework to process 32-bit constants as abstract input variables. The engine then decomposes these values into valid instruction formats and eliminates the need for manual bit-field calculations. Furthermore, the generate_binary_pair API serves as a critical orchestration point that maintains structural consistency throughout the encoding workflow. The method manages the exact sequence of instruction generation and guarantees that the LUI and ADDI pairs couple correctly.

The Binary Rewriting Engine (Table 2) adopts a state-managed approach to file manipulation and ensures stability during the rewriting process. The engine maintains a local buffer of binary_data and utilizes the r+b (read-plus-binary) file mode to perform targeted seek operations. The chosen strategy significantly minimizes disk I/O overhead. Localized manipulation remains essential for the preservation of ELF structure integrity, as the engine modifies specific byte-ranges in-place. The technique removes the necessity for full binary re-serialization and prevents unintended alignment or offset shifts in non-target sections.

Operational reliability stems from the execute_automated_flow method, which functions as a centralized control loop with rigorous exception handling. The orchestration logic treats the entire patching sequence from pattern discovery via find_instruction_offset to the final write as a single transactional unit. If the framework fails to locate the target pattern or if the write operation encounters an error, the engine immediately interrupts the workflow. The mechanism protects mission-critical firmware or simulated hardware binaries from the execution of partially patched or corrupted machine code.

3.2. Operational Methodology

The AIPR framework transforms architectural parameters into binary modifications. The system analyzes how hardware configurations are converted into low-level instructions. This methodology identifies how the source code defines registers and hardware units. Such analysis allows the framework to target specific parameters for modification at the executable level.

The RISC-V architecture uses instructions with a fixed length of 32 bits. Consequently, the system builds large addresses and constants through multiple steps. The framework tracks the synchronization of instruction pairs to maintain structural consistency. This tracking ensures that register base pointers stay correct when the system modifies hardware units across different memory regions.

Managing sign-extension is a major part of this process. The lower part of an instruction is treated as a signed integer, which can change the final value. The Instruction Encoding Engine calculates these limits in advance. The engine adjusts the upper instruction to keep the correct value even when the numbers are large.

The framework changes specific bit-fields in the machine code. The logic modifies only the immediate fields and leaves opcodes or register identifiers unchanged. This method ensures that the internal data flow stays the same. The approach avoids side effects for other hardware logic and keeps the program structure stable.

The final step modifies the hexadecimal data in the binary file. This process follows the little-endian memory layout of the architecture. The methodology also maintains alignment for different instruction lengths. Readers can find detailed tables and assembly sequences in the Appendix A.

4. Experimental Results and Analysis

This section presents an empirical evaluation designed to directly address the research question posed in Introduction. Specifically, the experiments assess whether executable-level binary rewriting can (i) maintain functional robustness across diverse instruction-level transformation challenges and (ii) significantly improve the efficiency of research artifact generation under fragmented toolchain conditions.

4.1. Experimental Environments

The primary objective of the proposed framework is to facilitate operational functionality in broken environments where source-level recompilation is practically unfeasible. Consequently, the fundamental focus of the experimental evaluation is to demonstrate the transition from technical infeasibility to feasibility. Detailed execution logs provide the evidence to determine whether the transformation is possible or impossible.

To supplement this functional proof-of-concept with quantitative data, additional empirical evaluations were conducted to measure performance efficiency. The time required to generate 20 unique test binaries was measured. The experimental environment consisted of a workstation running Ubuntu 22.04 LTS, equipped with an Intel Core i7-12700K processor (3.60 GHz) and 32 GB of DDR4 RAM. The evaluation compared the proposed framework against a traditional GCC-based recompilation flow.

To evaluate the AIPR framework in a realistic yet controlled architectural setting, all experiments utilize V-FRONT [26] as a representative baseline for verifying instruction-level transformations, as it strictly adheres to the RV32I v2.1 base integer instruction set.The most significant feature and constraint of this core is the combination of a Von Neumann structure and a 5-stage pipeline. The implementation incorporates MTE functionality through RTL modifications. However, the details of the hardware implementation remain beyond the scope of this paper, and the study excludes further technical discussion regarding the RTL changes. The detailed information is provided in Appendix B.

4.2. Functional Robustness and Reliability Validation

The initial phase of the evaluation focuses on the fundamental reliability across diverse architectural artifacts. This experiment serves as a formal verification of the technical feasibility of binary-level intervention. Unlike general architectural benchmarks that measure workload performance, the target binaries in this study are categorized by the structural obstacles they present to the rewriting process. Such structural challenges include standard immediate replacement, arithmetic correction for sign-extension boundaries, base address reconstruction, and global sequence propagation across dependent instructions.

To ensure the comprehensive robustness of the framework, the evaluation involved 2000 independent transformation trials, which were systematically categorized into four specialized challenge groups to ensure diversity: (1) isolated immediate replacement, (2) arithmetic sign-extension correction, (3) base address reconstruction, and (4) global sequence propagation. This confirms the framework’s ability to handle various architectural patterns beyond simple variations. As shown in

Table 3. Classification of Target Binaries based on Binary Rewriting Complexity and Architectural Dependencies.

Category	ID Range	Overwriting-Specific Challenges
Type 1	B1–B5	Standard Immediate Replacement: The engine updates isolated 12-bit or 20-bit immediate fields within a single instruction where no arithmetic dependencies exist.
Type 2	B6–B10	Arithmetic Correction: The process handles the 0 × 800 sign-extension boundary, which requires a simultaneous update of LUI and ADDI pairs to prevent value corruption.
Type 3	B11–B15	Base Address Modification: The framework targets the reconstruction of 32-bit memory-mapped register addresses through multi-instruction sequence analysis and offset alignment.
Type 4	B16–B20	Sequence Propagation: The system manages the consistent update of multiple dependent instructions across different hardware units to ensure global policy enforcement.

, for each of the 20 target binaries, 100 heterogeneous patching scenarios were executed.

As illustrated in Figure 1, the AIPR framework achieved a 100% success rate across all 2000 trials. Even in complex scenarios such as Type 2 arithmetic correction and Type 4 sequence propagation, no instances of binary corruption occurred. The evaluation follows a strict validation process to ensure that the patch logic preserves the original program semantics while the ELF header remains completely untouched. The procedure involves a three-tier check: structural consistency via disassembly, execution correctness through cycle-accurate RTL simulation, and control-flow integrity via program counter trace comparison. Such a rigorous assessment confirms that the framework successfully updates security policies at the executable level and avoids any unintended functional divergence.

4.3. Turnaround Time Comparison and Scalability Analysis

While the previous experiment validates the functional reliability, Table 4 evaluates its operational efficiency in accelerating the hardware verification loop. To provide a direct comparison with the functional validation, the turnaround time was measured across the same 2000 independent transformation trials (100 variants for each of the 20 target binaries across Types 1–4). The evaluation compared the cumulative time required to generate these artifacts using two distinct methodologies:

• Baseline (Recompilation): Modifying source-level parameters followed by a full toolchain execution (riscv32-unknown-elf-gcc) including parsing, optimization, and linking for each variant.
• AIPR (Binary Patching): Utilizing the proposed framework to directly manipulate immediates and opcodes within the pre-compiled artifacts.

Table 4. Quantitative Performance Comparison ($N=2000$ Trials).

Metric	Recompilation Flow	AIPR Framework
Total Execution Time	∼6800 s (∼1.89 h)	230 s (29.57×)
Average Time per Trial	3.4 s	0.115 s
Peak CPU Load	High (Multi-threaded)	Negligible
Disk I/O Intensity	High (Object Files)	Low (In-place)

Table 4. Quantitative Performance Comparison ($N=2000$ Trials).

Metric	Recompilation Flow	AIPR Framework
Total Execution Time	∼6800 s (∼1.89 h)	230 s (29.57×)
Average Time per Trial	3.4 s	0.115 s
Peak CPU Load	High (Multi-threaded)	Negligible
Disk I/O Intensity	High (Object Files)	Low (In-place)

The results indicate a significant 29.57× speedup in artifact generation. In contrast, the traditional recompilation flow took 1.89 h for the 2000-trial campaign. This delay occurred because standard compilers use slow processes like multi-pass optimization and symbol resolution. The AIPR framework now demonstrated that it restores research feasibility in only 230 s. The same task previously required nearly two hours of expert-level manual intervention or remained technically infeasible. This framework achieved a 29.57× speedup, but this efficiency is a secondary benefit. The primary contribution is research sustainability in fragmented RISC-V ecosystems. The framework converts a multi-hour engineering struggle into a sub-four-minute automated task. This transition allows researchers and practitioners to reproduce artifacts efficiently. Consequently, the tool establishes a stable foundation for long-term research within the specialized architectural community.

4.4. Limitations and Comparative Analysis

The observed 29.57× speedup demonstrated that instruction-level patching was highly efficient compared to the traditional GCC recompilation flow. I recognized that this gain was expected because the AIPR framework avoided the time-consuming overhead of multi-pass optimization and symbol resolution required by a compiler. However, the primary value of my proposal was the restoration of research feasibility in “broken” environments where source-level reconstruction was no longer possible. Unlike general binary rewriting frameworks that primarily targeted security hardening or performance profiling, the AIPR framework was a specialized methodology for architectural research sustainability. The framework separated experimental logic from unstable build environments to provide a stable foundation for long-term research artifacts.

Despite these advantages, the framework had specific failure cases. Because the engine excluded modifications to the ELF header to ensure simulator compatibility, the current system was unable to handle patches that required expanding binary sections beyond their original boundaries. Furthermore, complex structural changes to the binary file remained outside its capabilities. While this approach ensured high functional reliability for parameter modification, this design choice restricted the framework’s use for general-purpose code expansion or complex binary transformations.

The Instruction Encoding Engine is designed with a modular architecture that encapsulates bit-masking and shifting operations. I have explicitly noted that the framework can be extended to architectures like ARM or x86 by updating the encoding logic for their respective instruction formats. However, the AIPR framework utilizes a transactional, in-place patching method. The engine avoids modifications to the ELF header to maintain strict compatibility with architectural simulators. Because of this design, the framework cannot expand binary sections beyond their original boundaries. The current system is optimized for statically linked research artifacts. Additionally, it does not support complex formats such as position-independent executables (PIE) or dynamic linking. These formats are excluded because they require intensive structural recalculations. Moreover, aggressive compiler optimizations remain outside the current scope to prioritize semantic integrity. Such constraints limit practical applications in specific scenarios. For instance, researchers cannot currently insert large-scale security instrumentation or recalculate complex branch targets for long-distance jumps. Future research could address these issues with a full ELF rebuilding engine. This proposed engine would enable section relocation and more extensive structural changes.

5. Conclusions and Future Work

This research addresses systemic inefficiencies in computer architecture research by proposing the AIPR framework. The system bridged the gap between volatile specifications and reproducible research. The framework shifted the intervention point from brittle source-level recompilation to the stable executable level. The evaluations prove that reliable binary-level modification is possible. This remains true despite constraints like the little-endian layout and sign-extension. The framework also handles instruction length variability in the RISC-V ISA. Through a transactional workflow, the engine ensured semantic integrity without the original source code. Experimental results validate the robustness of the approach across 2000 trials, where the AIPR framework delivered a 29.57× speedup compared to traditional recompilation. The framework reduced the total execution time from 1.89 h to 230 s. Furthermore, the in-place manipulation maintained a negligible CPU load and low disk I/O, which proved the efficiency of the proposed binary rewriting methodology.

Future research for the AIPR framework will prioritize expanding verification coverage by generating non-standard binary layouts that standard compilers typically avoid. I aim to develop an architectural fuzzer and a Boundary-Seeker module to expose deep-seated microarchitectural errors by forcing illegal opcodes and branch instructions across critical hardware limits. Furthermore, I plan to explore the application of global–local feature fusion to accurately classify complex hardware-integrity anomalies within these generated traces, leveraging few-shot learning to identify rare bug signatures even with limited labeled data [27]. Finally, I will establish a roadmap for long-term sustainability by integrating AIPR into automated research workflows, focusing on concrete scenarios such as restoring legacy “digital tombstones” to demonstrate practical artifact reuse across evolving RISC-V specifications.