System-Specific Interpreters Make Megasystems Friendlier

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MATTHEW SOTOUDEH,Stanford University, USA

Modern operating systems, browsers, and oce suites have become megasystems built on millions of lines

of code. Their sheer size can intimidate even experienced users and programmers away from attempting

to understand and modify the software running on their machines. This paper introduces system-specic

interpreters (SSIs) as a tool to help users regain knowledge of and control over megasystems. SSIs directly

execute individual modules of a megasystem in a

gdb

-like environment without forcing the user to build, run,

and trace the entire system. A prototype framework to help write SSIs is described in this paper and available

for download at https://github.com/matthewsot/ssi-live22.

1 INTRODUCTION

Ballooning user expectations have caused computer systems to grow in size and complexity over

time. Operating systems, web browsers, oce suites, compilers, and even websites are routinely

hundreds of thousands or even millions of lines of code across a variety of programming languages.

These megasystems are extremely useful, surprisingly dependable, and often free software. But

their sheer size makes it increasingly dicult for users and hobbyist programmers to understand

and modify them. Even compiling some megasystems, such as the Linux kernel, can be a surprisingly

challenging task. Such barriers represent an unfortunate bottleneck through which even reasonably

motivated and skilled users are prevented from understanding and exercising control over the

software that has become necessary for their daily lives.

This paper proposes the development and use of system-specic interpreters (SSIs) to help users

understand and modify portions of such megasystems. The key insight is that, from the perspective

of any given module of the larger system, the rest of the system exposes a domain-specic language

in which the module is written. Directly building an interpreter (the SSI) for that language allows

the user to run individual modules, les, and functions without having to wait for the system to

build, construct inputs triggering the code in question, or insert tracing code.

In fact, programmers already build such SSIs informally in their heads when attempting to

understand megasystems. We usually start at some interesting part of the code, e.g., a particular

device driver, and read it without knowing exactly how it is called by the overall megasystem or

what all of the functions it calls do. Over time, an informal mental model of this interface between

the module and megasystem is formed to enable such underconstrained reasoning.

Our key idea is to formalize and mechanize this mental model of the interface between the module

and the rest of the system. By giving users the tools to formally specify (as an SSI) their previously

informal model of the system–module interface, the cognitive burden of remembering a large

model is reduced. Executing code against this model via the SSI helps users nd inconsistencies or

inaccuracies in their mental model of the system.

We describe a framework that makes writing SSIs easier. The key idea is to separate imple-

mentation of language-level syntax and semantics from system-level syntax and semantics. Once

written, SSIs simulate execution of a module in isolation, similar to a unit test. The user interacts

with the SSI’s

gdb

-style interface to trace and modify intermediate states of the execution. After

modifying the code, the SSI can be re-run to check if the modication had the desired eect. By

directly interpreting the specic code that the programmer cares about, SSIs are a rst step towards

bringing the power of live programming and REPL environments to bear on the unique challenges

of lower-level systems code.

Author’s address: Matthew Sotoudeh, Stanford University, Stanford, CA, USA, sotoudeh@stanford.edu.

arXiv:2210.09460v1 [cs.SE] 17 Oct 2022

1:2 Mahew Sotoudeh

Linux

Build Run

Trace Understand

(a) Without SSI

Linux +SSI

Understand

(b) With SSI

Fig. 1. Poking at megasystem code usually involves figuring out how to: (i) build the system, (ii) execute it

with the right inputs to reach the desired code, and (iii) trace the eects of the code. None of these steps are

easy in most modern megasystems. SSIs execute individual modules of a megasystem within an interpreter

environment, allowing users to skip directly to the key poking-and-understanding phase of the cycle.

2 CASE STUDY

Consider a curious Jane Doe. One day, she is using a Raspberry Pi to control a light strip and

becomes interested in how the kernel’s GPIO driver works. She pulls up the Linux source code,

and nds the relevant

pinctrl-bcm2835

driver code excerpted in Figure 2a. Reading the code, she

notices that the broader Linux kernel provides essentially a domain-specic language (DSL) to the

driver code. This DSL includes, for example, commands to remap (

devm_ioremap_resource

) or

write to (

writel

) memory-mapped input-output (MMIO) addresses. The DSL exposed by the kernel

actually spans multiple real languages, e.g., MMIO addresses are implicitly read from a device tree

source include (DTSI) le that gets compiled into the kernel at build time.

Jane quickly recalls why playing with megasystem codebases is a daunting task. Traditional

debugging tools like

gdb

require her to build and run the code, but Jane has only ever used a

pre-built kernel. Even if she could build the kernel or driver, Jane may not always have immediate

access to a Raspberry Pi to run the code on. Even if all of those issues were worked out, Jane would

have to spend time writing a test program that correctly invokes the right combination of syscalls

to trigger the driver code she is trying to understand, and then somehow gure out what the driver

code is actually doing to the hardware, i.e., what MMIO addresses are actually being written to at

each point in the program and with what values. Unless Jane wants to try and understand the code

entirely in her head, without the benet of actually running it like code to see what it does, she must

solve all of these problems before even beginning to understand the code.

2.1 Using a System-Specific Interpreter

Suppose instead that Jane had a system-specic interpreter (SSI) for this device driver, as shown

in Figure 2b. The SSI loads the driver source code, then provides a

gdb

-like REPL interface to a

simulated instance of the driver. Jane can ask it to load the driver and run dierent driver operations

in a simulated environment. She can trace, set breakpoints, and step-through the driver code.

Figure 2b shows how Jane can use the SSI to print out the arguments of every call to

writel

, and

set a breakpoint to inspect the values of local variables.

Because the SSI is directly interpreting the driver code itself, not the entire Linux kernel, it starts

and executes quickly. If Jane later modies the code, she can immediately re-run the SSI to verify

it produces the desired behavior. Only once Jane has understood the driver code and made any

desired modications might she need to worry about building and loading the driver. The SSI allows

her to separate concerns: understanding and modifying an individual module in the megasystem

without rst understanding the entire megasystem and associated build system.

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1System-SpecificInterpretersMakeMegasystemsFriendlierMATTHEWSOTOUDEH,StanfordUniversity,USAModernoperatingsystems,browsers,andofficesuiteshavebecomemegasystemsbuiltonmillionsoflinesofcode.Theirsheersizecanintimidateevenexperiencedusersandprogrammersawayfromattemptingtounderstandandmodifythesoftwarer...

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