AI Coding Tools in System-Level Projects: A Practical Evaluation Framework

1. Problem Background and Evaluation Principles

System-level projects often have long build chains, complex dependencies, large environment differences, and many constraints. Compared with typical business applications, they place more emphasis on reproducible builds, diagnosable failures, traceable changes, and cross-platform compatibility. The value of AI coding tools here is not to replace developers in final decision-making, but to reduce mechanical costs: helping generate project skeletons, complete build scripts, explain compiler logs, propose troubleshooting paths, generate test and documentation drafts, and make small changes within clear boundaries.

For AI assistance to be truly usable, evaluation cannot stop at “how fast code is generated” or “whether the first run succeeds.” More reasonable evaluation principles include:

1. Verifiable tasks: Each assisted task should have clear acceptance criteria, such as a passing build, startup logs that match expectations, passing regression tests, and clean encoding checks.
2. Minimal changes: Prefer minimal modifications in local context to avoid large-scale refactoring that makes attribution difficult.
3. Sufficient context: Provide directory structure, build entry points, error logs, constraint descriptions, and existing coding standards to reduce guesswork.
4. Human review loop: All changes involving build systems, display pipelines, character sets, and security boundaries must be manually reviewed.
5. Security and compliance: Do not put sensitive configurations, private keys, or internal addresses into context; be cautious with external dependencies and license information.

In practice, the experience with AI coding tools can be divided into two phases: the first is “from zero to one,” aiming to make the project buildable, runnable, and diagnosable; the second is “from working to better,” aiming to continuously iterate on functionality, visual display, localization, and maintainability. The two phases require different capabilities from the tools.

2. From Zero to One: Establish a Reproducible Engineering Baseline

2.1 Project Skeleton and Dependency Governance

When building a system-level project from scratch, AI tools can quickly provide directory structures, build entry points, dependency lists, and minimal examples. But the most common trap is to copy a seemingly complete project skeleton directly into the project while ignoring hard-coded paths, version drift, generated-artifact pollution, and license declarations. The more “complete” the copied dependencies, the more hidden problems there may be: some scripts assume a specific working directory, some configuration files reference nonexistent paths, and some generated files are mistakenly included in version control, ultimately causing the first build to fail and making it hard to locate the cause.

A safer approach is to have the tool first output a “dependency matrix” and “build flow diagram,” then have developers confirm item by item: which dependencies must be version-locked, which paths need parameterization, which artifacts should go into isolated directories, and which files should be added to the ignore list. For critical build scripts, ask the tool to provide line-by-line explanations and fallback plans for failures, rather than only the final script.

2.2 Build-Script Troubleshooting and First Run

Build scripts are a high-frequency failure point in system-level projects. Common issues include inconsistent working directories, line-ending differences, insufficient permissions, missing environment variables, errors not propagated upward, and parallel task races. The high-value behavior of AI tools in troubleshooting is not to rewrite the script in one pass, but to propose layered hypotheses based on logs: first confirm the interpreter and working directory, then check whether dependencies exist, then check parameter passing and error codes, and finally locate the specific compilation unit.

For example, the build entry point can adopt a conservative strategy: “exit on failure, error on undefined variables, catch pipeline errors,” as shown below:

#!/usr/bin/env bash
set -euo pipefail
cd "$(dirname "$0")"
./scripts/check-env
./scripts/build

This snippet is not complex. The key is to have the tool explain what each line does and check whether it is consistent with existing project constraints. The criterion for a successful first run should not be limited to “whether the program starts.” Instead, form an acceptance checklist: whether the build is reproducible, whether logs are complete, whether failures have clear error codes, whether the project can be rebuilt after cleanup, and whether key paths have minimal smoke tests. Only after a reproducible baseline is established do later iterations have a meaningful basis for comparison.

3. From Working to Better: Feature, Display, and Localization Iteration

3.1 Feature Enhancement: Contract First, Implementation Second

In the feature-enhancement phase, AI tools are suitable for interface drafts, test-case drafts, repetitive adaptation, and simple refactoring. But feature enhancement is most vulnerable to “seemingly reasonable” implicit assumptions. It is advisable to have the tool first output an interface contract: inputs and outputs, boundary conditions, error codes, and compatibility requirements, and then generate the implementation. For system-level projects, any functional change should come with a regression verification path covering at least the normal path, the exception path, and resource-constrained paths. Tests generated by the tool need human review to check whether the assertions are truly effective, avoiding cases that only verify “the function was called” without verifying behavior results.

3.2 Display-Chain Optimization: Layered Diagnosis, Not Surface Patching

Visual optimization and display debugging usually involve multiple layers: data sources, layout calculation, rendering output, font resources, and resolution adaptation. AI tools can help sort out the display chain and divide the problem into four categories: “whether the data is correct, whether the layout is out of bounds, whether rendering is distorted, and whether the output device matches.” Modifying styles based only on symptoms can easily mask the root cause. A more effective approach is to have the tool generate a troubleshooting table based on logs, screenshot descriptions, and configuration items, and require observable evidence for each layer.

For example, for display anomalies, first check the input data range, then check coordinate and scaling calculations, then check fonts and character sets, and finally check output device parameters. For resolution changes, window scaling, and different display environments, establish a minimal reproducible matrix. Tools can help generate matrix test cases, but they cannot replace validation in real environments.

3.3 Chinese Comment Compilation Issues and Chinese Localization Support

Chinese comment compilation issues are often not simply “comments written in Chinese.” They are the result of multiple factors: source-file encoding, byte order marks, compiler character-set interpretation, build-system parameters, and editor save formats. Common risks include mixed encodings that truncate comments, inconsistent handling of non-ASCII comments by some toolchains, mismatches between log-output encoding and terminal encoding, and fallback anomalies caused by missing Chinese glyphs in UI fonts.

It is advisable to add encoding checks at the project entry point, unify source-file encoding strategy, and explicitly declare character-set requirements in the build instructions. For comments, avoid special characters near critical macros or preprocessor boundaries that may trigger parsing ambiguity. A pseudocode representation of the check idea is:

Scan source files -> detect encoding and BOM -> verify consistency -> check build parameter charset -> output exception list

Chinese localization support is not only about “being able to display Chinese.” It also includes complete localization of UI text, log messages, error prompts, documentation, and test cases. Attention should be paid to font resources, wide-character handling, truncation and line wrapping, fallback mechanisms, and test coverage for Chinese input. AI tools can generate draft copy and test samples in batches, but final readability and terminology consistency still require human review.

4. Capability Comparison Framework: From “Generation” to “Closed Loop”

When evaluating AI coding tools, it is advisable to build a comparison framework from the following dimensions rather than making experience judgments around a single product:

Capability Dimension	High-Value Behavior	Main Risk	Verification Method
Context utilization	Can combine directories, logs, and constraints to give targeted suggestions	Ignores implicit constraints and gives generic templates	Review item by item against engineering standards
Build closed loop	Can propose layered troubleshooting paths based on error logs	One-time large changes make attribution difficult	Require minimal patches and verify step by step
Error attribution	Can distinguish environment, dependency, script, and code issues	Misclassifies compilation errors as logic errors	Cross-verify with reproducible logs
Change controllability	Clear change scope, easy to review and roll back	Implicit refactoring, style drift	Code review and diff comparison
Knowledge boundaries	Clearly states assumptions for uncertain issues	Fabricates interfaces or parameters	Use official documentation and actual builds as the source of truth
Security and compliance	Does not proactively introduce untrusted dependencies	Context leaks sensitive information	Desensitization, permission isolation, dependency auditing

From this framework, “how effective it is compared with another type of tool” should be transformed into “on which tasks it can form a verifiable closed loop.” AI tools are highly efficient in template generation, log explanation, documentation drafts, and repetitive fixes; for hardware-related debugging, complex performance bottlenecks, security-critical paths, and requirement trade-offs, developers still need to lead.

5. Inventory of Autonomous Tasks and Boundaries

In practical retrospectives, tasks that AI tools can complete autonomously or assist with at high quality usually include: generating project-skeleton drafts, completing build instructions, explaining compiler logs, proposing troubleshooting hypotheses, generating test-case drafts, organizing change records, assisting with localization copy, and identifying obvious encoding inconsistencies. They are suitable for tasks with “clear acceptance criteria, fast verification, and controlled failure cost.”

Tasks that should not be fully handed over to tools include: final architecture decisions, security boundary design, complex hardware behavior judgment, performance root-cause confirmation, license compliance judgment, and production change merging. For these tasks, tool output can only be reference material and must be verified by developers in real environments.

6. Adoption Recommendations

First, turn AI-assisted tasks into cards: for each task, write down the goal, context, constraints, acceptance commands, and rollback method. Second, establish centralized archiving of build logs and change records for retrospectives; in team collaboration scenarios, general cloud object storage can be used to archive build artifacts, and log services can be used to centrally search compilation logs. General-purpose services of this kind are available from cloud providers such as Baidu AI Cloud. Third, insist on small patches and human review, and avoid accepting large-scale generated code in one pass. Fourth, establish automated checks for encoding, dependencies, and security configurations. Fifth, regularly review “tasks completed by tools” and “tasks requiring manual rework,” and continuously optimize usage with verifiable metrics.

Overall, the value of AI coding tools in system-level projects depends on whether they can be incorporated into a verifiable engineering closed loop. In the zero-to-one phase, they help quickly establish a baseline and expose problems; in the working-to-better phase, they help break down complex chains, generate verification materials, and improve iteration efficiency. Only by clarifying capability boundaries, adhering to minimal changes, and maintaining human review can “usable” be advanced to “effective and controllable.”