How to Choose an AI Coding Tool: A Reproducible Evaluation Guide for E-Commerce Projects

1. Why Reproducible Evaluation Matters

Many tool selection articles only provide rankings. Readers cannot tell which version, which set of prompts, or which mode produced the conclusions. The same tool can perform very differently on different dates or with different model configurations. This article therefore breaks the evaluation process into steps that can be executed one by one. Readers can repeat the same task and compare the results in their own projects.

This article groups the tools under test into three categories by function, using aliases throughout. Tool A is a one-click generation IDE built into a cloud platform. Tool B is a locally adapted, controllable interactive IDE based on an open-source IDE. Tool C represents other similar products. Selection decisions should be based on your own test data, not on any existing ranking.

2. Evaluation Environment and Reproduction Steps

Before each test, record the information in the table below. If any item is missing, the result cannot be compared with data from other users.

Record item	Example format
Tool name and version	Full version number of the client
Underlying model and mode	Model name, automatic or manual mode, whether multi-agent is enabled
Operating system and network	OS version, network bandwidth and latency
Runtime environment	Actual versions of Node.js, npm, and React
Prompts	Complete text of each round of input, saved in chronological order
Test date and time window	Exact date, with peak or off-peak period noted

The standard task is to “build a React e-commerce site from scratch.” The requirements include the following four items.

1. Pages: product list, product detail, cart and checkout, payment success, login and registration, and user center (dashboard, orders, coupons, and personal settings).
2. Data: page data should be retrieved through an independent data layer. Mock data is allowed, but it must be stored in a centralized location and must not be placed inside components.
3. Layout: pages should be usable at 375px, 768px, and 1280px widths, with no text overflow and no obstructed buttons.
4. Shared components: the navigation bar and footer should reuse the same component.

The reproduction steps are as follows.

1. Create a new empty directory and place only the same requirements document in it.
2. Each tool receives only the same requirements. After the first round of generation, make no manual modifications.
3. Run npm install, npm run build, and npm run dev in sequence, and record the exit codes and the first error message.
4. If an error occurs, paste the complete error back into the tool as-is and count it as one repair round. Set the maximum number of repair rounds to 5; exceeding this limit counts as a failure.
5. Manually walk through the main flow: product list, product detail, add to cart, checkout, and payment success. Record the result step by step.
6. Take screenshots at the three breakpoints and save them to the same directory for side-by-side comparison.

3. Scoring Criteria

Score each dimension linearly from 0 to the maximum score, and state the basis for the score. The weights in the table below total 100 points.

Dimension	Weight	Quantitative indicator	Scoring method
Deliverability	20	Whether the first build succeeds	Full score for success; 0 for failure
Task completion	25	Number of pages covered, number of main-flow steps passed	Passed steps divided by total steps, then multiplied by the weight
Repair cost	20	Repair rounds, manual time in minutes	Fewer rounds and shorter time yield a higher score
Code architecture	15	Whether mock data is centralized, whether state management is actually implemented	Score after collecting evidence with the commands in Section 4
Responsive and visual	10	Number of issues in the three breakpoint screenshots	Fewer issues yield a higher score
Interaction controllability	10	Whether human confirmation is possible before each change	Three levels: none, partial, full

4. Evidence Commands for Code Architecture

Code architecture should not be judged by impression alone. Use the following commands to preserve evidence.

# Find files containing mock data to determine whether it is centralized
grep -rln "mock\|const products" src | sort
# Find empty directories, such as hooks or context directories that have names but no content
find src -type d -empty
# Find nested src directories with the same name
find . -path ./node_modules -prune -o -type d -name src -print
# Count files that heavily use inline styles in JSX
grep -rln "style={{" src --include=*.jsx --include=*.tsx | wc -l

Interpret the results as follows. An empty directory only shows that the tool created a folder; it does not mean that state management has been implemented. Check whether the directory contains exported functions or contexts. If mock data is scattered across multiple components, integrating real APIs later will require extensive changes.

5. Common Failure Cases and How to Verify Them

The following six types of issues are common in AI-generated projects. Each type includes a verification method so that you can confirm it during retesting.

1. First-run error. Common causes include missing dependencies, incorrect import paths, or nested directories with the same name that prevent module resolution. Check the first error in the npm run build output and save the complete log.
2. Missing form validation. Submit an empty registration form, then enter an invalid email address, and observe whether prompts appear and whether they are accurate.
3. Insufficient button text contrast. Use browser developer tools to check contrast. Normal text should have a contrast ratio of at least 4.5:1, as required by WCAG AA.
4. Narrow-screen layout misalignment. At 375px width, check whether the login form and product cards overflow or overlap.
5. All images are placeholders. Count the proportion of placeholder images among all images to determine whether manual asset supplementation is needed.
6. State management exists only as a shell. Use the find command in Section 4 to locate empty directories, and confirm whether hooks and contexts contain actual code.

Several observations from the original evaluation, such as a first-run error in one tool or readability issues with buttons in another, need to be confirmed through the steps above. They should not be treated as final conclusions before retesting is complete.

6. Selection Checklist and POC Steps

Conduct the POC (proof of concept) according to the following steps.

1. Select two or three candidate tools.
2. Run a POC within one workday using the same requirements.
3. Score each item according to the criteria in Section 3 and save all evidence.
4. For the two highest-scoring tools, run a second round of testing in a different time window and record the generation wait time.
5. Create a written decision record stating the reasons for selection and the reasons for rejection.

Check item	Pass criterion	Recording method
First build	The first npm run build succeeds	Exit code and log file
Main flow	All five steps pass, with a pass rate of at least 80%	Screenshot or operation record for each step
Repair rounds	No more than 3 rounds	Prompt and original error text for each round
Mock data	Centralized in one directory	grep command output
Three-breakpoint check	No blocking errors	Three screenshots
Wait time	The team sets an upper limit based on business needs	Start and end time of each generation

Thresholds should be adjusted by the team according to its own situation. The table above is only an example, not an industry standard.

7. Risks and Compliance Notes

• Do not paste real API keys, user data, or internal business documents into prompts. When sensitive information is involved, use placeholders instead.
• Run npm audit on generated code to scan for dependency vulnerabilities, and use npx license-checker --summary to check dependency licenses.
• Cloud-based generation depends on the network. Queuing may occur during peak periods. Record wait times during the POC and confirm the provider’s availability commitments.
• Perform static security scanning on generated code, focusing on cross-site scripting, hardcoded secrets, and insecure form submission.
• Read paid terms, data retention periods, and model training policies item by item. The contract text should be treated as authoritative.

8. Three Selection Judgments for 2026

First, selection should look at first-run success rate and repair rounds before generation speed. A tool that generates quickly but cannot run may take longer in total.

Second, the design of mock data and the state layer determines the cost of integrating real APIs later. During selection, check whether these two parts have clear boundaries from the beginning.

Third, AI is suitable for generating prototypes and project skeletons. Core business logic, payment flows, and permission control still require human review and testing.

By completing the evaluation using the method above, teams can make decisions with their own data instead of relying on external rankings.