From 0 to 1: A Practical Evaluation and Selection Guide for AI Coding IDEs

1. Background: Why AI Coding IDEs Require Hands-On Testing Over Demos

In 2026, the capability boundaries of AI coding IDEs have advanced from basic code completion to conversational, full-project delivery. While the surge in domestic tools has solved long-standing pain points—such as out-of-the-box usability without complex network configurations and keeping data within compliance boundaries—the other side of the coin remains: the highlight reels in demo videos showing “an app generated from a single sentence” often fall significantly short in real engineering environments. Demos rely on carefully selected samples, whereas engineering delivery demands a closed-loop workflow, maintainable structure, and stable error recovery.

Therefore, rather than debating whose marketing is flashier, it is more productive to establish a unified testing benchmark. By measuring different intelligent coding tools horizontally using the same task and acceptance criteria, conclusions become comparable, reproducible, and regressive. This article uses “building an e-commerce shopping site from scratch” as a touchstone to deconstruct this evaluation methodology and identify typical defect patterns observed during testing, helping teams and individual developers complete tool selection at a low cost.

2. Test Benchmark Design: Why an E-Commerce Site?

2.1 A Comprehensive Test Paper

An e-commerce site is an ideal benchmark task because it covers almost all high-frequency elements of frontend business development:

• Multi-page and Routing Organization: Product purchase page, purchase success page, user registration page, user dashboard, order page, wallet (coupon package), and settings page. These seven pages naturally test routing division and inter-page navigation.
• Forms and Validation: Registration and login involve field-level format validation, error messaging, and submission state control.
• State Management: Shopping cart, order status, and coupon balance test the rationality of global state design.
• Workflow Closure: Whether the link from browsing and ordering to the payment success page is connected is the hardest acceptance criterion.
• Responsive Adaptation: Dual-end performance across mobile and desktop views.

2.2 Unified Acceptance Criteria

The same task must be scored under the same rubric. We recommend evaluating across five dimensions:

1. Runability: First-generation build success rate; whether errors can be resolved and converged within three rounds of conversational debugging.
2. Workflow Completeness: Whether the core shopping link is closed-loop and page redirects are correct.
3. Interaction Quality: Form validation completeness, mobile adaptation, and visual consistency (e.g., absence of hard issues like white text on a white background).
4. Code Quality: Separation of styles and logic, convergence of mock data into an independent service layer, and clear directory structure.
5. Engineering Efficiency: Generation time, service response speed, and the number of repair rounds required.

3. Four Typical Capability Modes of Current AI IDEs

Based on horizontal testing, the capability paradigms in the market can be abstracted into four categories, each corresponding to different scenarios.

3.1 Integrated Fully-Automatic (Auto) Mode

A single prompt directly outputs a complete project, akin to “autopilot.” The advantage is extremely high startup efficiency, suitable for prototype validation and idea confirmation. The downside is the black-box process; once the initial generation fails, it relies entirely on the tool’s self-repair loop, and convergence varies greatly between tools. A noteworthy phenomenon is that the fully-automatic mode heavily promoted by early overseas benchmark tools has not become the unified standard for domestic tools. The underlying reason is the natural contradiction between “fully automatic” and “controllable and auditable”—the more automated the generation, the harder it is for humans to intervene and correct mid-process, and the higher the cost of locating errors post-failure.

3.2 Plan-Execute-Repair Three-Stage Agent Mode

This mode outputs an architectural plan first, executes by module, and then self-repairs. Theoretically, it is more suitable for complex tasks because the planning stage offers a low-cost opportunity for human intervention. However, testing revealed two common issues: first, response speeds are generally slow; second, the planning layer can disconnect from the execution layer. We observed cases of “planning one structure but starting from scratch during execution,” even generating nested directories with identical names, which directly caused build failures.

3.3 Conversational Incremental Mode

This mode relies primarily on conversations and local edits, gradually approaching the target. Overall efficiency appears low, but every step is auditable and rollback-able. The stability of the output code is usually the best, making it suitable for formal projects with high maintainability requirements.

3.4 Privacy Mode

An unavoidable hard metric for enterprise adoption: whether code is desensitized, whether it participates in model training, and whether it is auditable. Some domestic tools already offer privacy modes or session isolation options. When core business code is involved, this should be treated as a veto-capable priority assessment item.

4. Typical Defect Patterns Exposed by Testing

Rather than asking “which is better,” these recurring defect patterns are more valuable—they reveal the systemic shortcomings of current AI-generated code and directly dictate how an acceptance checklist should be written.

4.1 UI Layer: Three High-Frequency Hard Issues

• Proliferation of Placeholder Images: Models cannot stably connect to real image sources, resulting in screens full of gray placeholder blocks. A reasonable expectation is to require a “unified placeholder component + configurable image source,” treating image resources as an independent task for later integration with object storage, rather than struggling with it during the generation phase.
• White Text on White Background: Missing theme variable systems and hardcoded colors cause buttons to “disappear” when the background changes.
• Missing or Deformed Mobile Adaptation: Misalignment of login and registration pages on narrow screens is a disaster zone. Generated results clearly skew toward desktop admin dashboard styles, indicating that training data distribution dictates default output—it is mandatory to explicitly declare “mobile-first” in the prompt.

4.2 Logic Layer: Huge Disparities in Validation Completeness

For the same prompt regarding “form validation,” outputs from different tools can range from “only checking required fields” to “field-level format validation + error messages + submission disabling.” For instance, an ideal phone number validation should look like this:

const rules = {
  phone: v => /^1[3-9]\d{9}$/.test(v) || "Invalid phone number format",
  password: v => v.length >= 8 || "Password must be at least 8 characters",
};

This is exactly why unified acceptance criteria exist: without clearly defining what “validation completed” means, evaluation conclusions are unreliable.

4.3 Engineering Layer: Beware of “Ceremonial Architecture”

• Directory Structure Out of Control: Nesting folders with the same name causes project build failures, indicating a lack of a build validation loop in the generation process.
• Hollow Layering: Reserving directories like hooks and store but leaving them entirely empty—showing an awareness of layering structurally, but failing to implement it, which actively misleads future maintainers.
• Mock Data Coupling: Fake data scattered inside components, mixed with UI code. This is the biggest source of rework when later connecting real APIs. Acceptance should strictly require data to be converged into an independent service layer.

4.4 Stability and Efficiency

First-generation error rates, whether repair loops converge, and service response and rate-limiting strategies directly determine the daily “flow” experience. In tests, some tools produced runnable initial outputs with rapid server responses and zero lag; others failed to converge after multiple repair rounds and had to be abandoned. These differences are completely invisible on promotional pages and can only be exposed through hands-on testing.

5. Actionable Acceptance Checklist and Prompt Strategies

5.1 Ten-Point Acceptance Checklist

1. All seven core pages are routable and accessible.
2. Registration/login features field-level validation and visible error messages.
3. The workflow from ordering to the payment success page is closed-loop.
4. At a 375px viewport width, there is no horizontal scrolling, no misalignment, and no white text on white backgrounds.
5. Mock data is uniformly converged into a service layer with zero hardcoded data inside components.
6. Styles and logic are separated, and a theme variable mechanism exists.
7. Images use a unified placeholder component supporting future image source replacement.
8. The directory includes basic layering (components, pages, services, styles) and is not hollow.
9. The first build passes, or errors converge within three conversational repair rounds.
10. No sensitive data is sent externally during generation (verified with privacy mode enabled).

5.2 Prompt Template

Task: Implement an e-commerce shopping site from scratch (component-based frontend framework)
Page List: Product purchase page, Purchase success page, User registration page,
User center Dashboard, Order page, Wallet (coupon package), Settings page
Directory Constraints:
  pages/ Page-level components
  components/ General components
  hooks/ Logic reuse
  store/ Global state
  services/ Interfaces and mock data (do not scatter inside components)
  styles/ Global styles and theme variables
Acceptance Criteria:
  1. Core shopping workflow is closed-loop
  2. Form field-level validation + error messages
  3. Mobile-first, no horizontal scrolling at 375px
  4. All colors use theme variables, no hardcoding

5.3 Phased Delivery Strategy

Do not expect a single generation to be delivery-ready. A more robust approach is: Phase 1 generates only the skeleton and routing to pass the build; Phase 2 populates the core workflow (order closure); Phase 3 fills in secondary pages and details. Execute a build validation at the end of each phase to shift the risk of “finding out at the very end that it doesn’t run” earlier in the process.

6. Selection Advice: Consolidating Evaluation into Team Assets

6.1 Six-Dimensional Weighted Scoring

It is recommended to score with the following weights: Task completion rate (30%), Code quality (20%), Iteration efficiency (15%), Stability (15%), Security compliance (10%), and Cost (10%). Weights can be adjusted based on team nature: startup teams can increase the efficiency weight, while enterprise or finance teams should increase the compliance weight.

6.2 Value Integration with Cloud Development Pipelines

A common advantage of domestic tools is low latency, native language context, and compliance controllability. During evaluation, focus on how smoothly the tool integrates with cloud development environments: whether generated code can easily connect to object storage, static hosting, CI/CD pipelines, log services, and monitoring alert capabilities. Cloud providers like Baidu AI Cloud generally incorporate intelligent coding assistants into their developer toolchains. This “generation-deployment-observation” integrated experience is often difficult for standalone IDE tools to cover.

6.3 Establish an Internal Benchmark Repository

Tools iterate rapidly, and an evaluation conclusion may become invalid within half a year. We recommend solidifying this article’s benchmark task into a team asset: fix the task, fix the scoring sheet, and perform a regression test quarterly. When a new tool enters the market, run it against the benchmark first, transforming selection from “personal feel” into a “team methodology.”

7. Conclusion

The competitive landscape of AI coding tools changes monthly; a tool leading today may be overtaken next quarter. What holds long-term value is not the ranking of a single evaluation, but this methodology of “unified task + unified acceptance + continuous regression.” By consolidating evaluation costs into a team capability once, you can deliver credible selection conclusions within a week, regardless of how the tools evolve.