Table 8 Qualitative comparison

Error Rate

 Pro.

The error rate is relatively stable and does not vary with changes in workload

Integration errors are relatively fewer and smaller

 Con.

Limited by AI model ability, there is a tendency for more integration errors to occur.

As the workload increases, the probability of integration errors occurring also increases.

Integration Consistency

 Pro.

Fixed AI model and program ensure consistency in the effectiveness of integration.

It is more flexible and facilitates rapid adaptation to new integration rules.

 Con.

Lack of flexibility makes it difficult to respond to changes in integration requirements.

Different understandings of integration rules among staff members lead to poor consistency.

Cost

 Pro.

Overall, it saves a significant amount of labor and time.

No outside staff participation is required.

 Con.

Additional computer experts are needed to design and write relevant programs.

More labor cost and time consumption.

Difficulty of quality management

 Pro.

Locating bugs from program output and logs is relatively simple

By communicating with relative staff, the cause of errors and solutions can be quickly determined

 Con.

Lack of interpretability of the AI model leads to integration results that cannot be explained, and errors cannot be tracked.

Locating errors requires interaction with humans, which is more complex and less predictable.