基于静态分析统计C代码中double变量的Load/Store指令需求

Absolutely—static analysis is exactly the right approach here, and it lets you hit all your requirements without needing any hardware at all. Let me break down how this works and how you can pull it off:

Yes, static analysis can fully satisfy every part of your request. It’s one of the most efficient ways to analyze instruction counts and memory operations without running your application on physical hardware.

1. Source-Code Level Analysis (If You Have Access to Code) #

If you have the application’s source code, this is the most straightforward path:

• Clang/LLVM Plugins: Write a custom Clang plugin that traverses the Abstract Syntax Tree (AST) to identify double variables. You can then track every load (reading the variable) and store (writing to the variable) operation involving these variables. To count between two specific functions, you can flag the start/end of those functions in the AST and only tally operations within their scope or in the call chains between them.
• Python AST Tools: For Python applications, use the built-in ast module to parse code and trace double (or float—since Python’s float is a double-precision type) operations. For C/C++, tools like pycparser let you parse source code into an AST and perform similar tracking.

2. Binary-Level Analysis (If You Only Have Compiled Binaries) #

No source code? No problem—static analysis works on compiled binaries too:

• Reverse Engineering Tools: Use tools like Ghidra (free, open-source) or IDA Pro (commercial) to disassemble the binary. These tools let you identify load/store instructions targeting double-precision values: on x86, look for instructions like movsd or vmovsd (which operate on 64-bit floating-point values), and on ARM, look for ldr dX/str dX (where dX is a 64-bit floating-point register).
• Custom Scripts: For quick prototyping, use objdump to disassemble the binary, then pipe the output to awk/grep scripts that filter and count the relevant instructions. To narrow down to the range between two functions, first locate their entry addresses in the binary, then analyze the control flow graph (CFG) to tally operations in the paths connecting them.

3. Targeting double Load/Store Operations #

The key to precision is focusing only on double-specific operations:

• Source Code: Directly match variable declarations of type double (or Python’s float, which maps to double-precision under the hood) and track every read/write reference to those variables.
• Binary: Filter instructions that operate on 64-bit floating-point registers or memory locations known to hold double values. Most reverse-engineering tools can annotate variable types if you provide debug symbols, making this even easier.

4. Counting Between Two Specific Functions #

To limit counts to the range between your two target functions, you have two main strategies:

• Direct Scope: If the functions are in a linear execution path (e.g., Function A calls Function B directly), you can mark the start of Function A and end of Function B in your AST/CFG and tally only operations within that bounded range.
• Call Chain Scope: If you need to count operations across all paths from Function A’s invocation to Function B’s invocation, build a call graph of the application. Static analysis tools can map all possible execution paths between the two functions, letting you tally relevant operations along those paths. Note that this will include all potential paths (not just runtime-executed ones), which is the best you can do without hardware/runtime data.

5. Tools to Get Started #

Here are some go-to tools to implement this:

• Source code: Clang/LLVM plugins, pycparser (C/C++), Python’s ast module
• Binary: Ghidra, IDA Pro, objdump + shell scripts
• LLVM IR: Use LLVM’s opt tool to write a custom pass that counts load/store instructions of type double, with filters to target your function range.

内容的提问来源于stack exchange，提问作者Black S.