Leveraging ChatGPT‑4 for Code Generation, Refactoring, and Testing in Software Development

ChatGPT‑4 shows a significant improvement over ChatGPT‑3.5 in logical reasoning and code generation. The author experiments with ChatGPT‑4 on non‑confidential development tasks to evaluate its impact on productivity.

Scenario 1 – Regular‑Expression Writing : The team needs a regex to validate PromQL strings in an observability platform. Using ChatGPT‑4, they obtain a complex expression and learn that a full parser would be more appropriate. After refining the requirement to focus on precision rather than recall, ChatGPT‑4 produces a simpler, acceptable regex and even writes a unit test.

Scenario 2 – Code Refactoring : A Go function that parses byte‑size strings is submitted to ChatGPT‑4. The original code is shown below. ChatGPT‑4 improves readability, restructures the switch‑case, and generates table‑driven unit tests.

package strings
import (
    "fmt"
    "regexp"
    "strconv"
)
var reOfByte = regexp.MustCompile(`(\d+)([GgMmKkBb]?)`)
// ParseByteNumber parses a size string like "10MB"
func ParseByteNumber(s string) int64 {
    arr := reOfByte.FindAllStringSubmatch(s, -1)
    if len(arr) < 1 || len(arr[0]) < 3 {
        return -1
    }
    n, err := strconv.Atoi(arr[0][1])
    if err != nil {
        return -2
    }
    if n <= 0 {
        return -3
    }
    switch arr[0][2] {
    case "G", "g":
        return int64(n) * (1024 * 1024 * 1024)
    case "M", "m":
        return int64(n) * (1024 * 1024)
    case "K", "k":
        return int64(n) * 1024
    case "B", "b", "":
        return int64(n)
    default:
        return -4
    }
}

ChatGPT‑4 also points out a bug where the regex matches substrings like "XXXX100KBXXX" and suggests a stricter pattern. After several iterations, the final solution balances simplicity and correctness.

Scenario 3 – Implementing Business Logic : The author splits a larger feature (event aggregation in an observability platform) into small, testable units and asks ChatGPT‑4 to implement each unit with accompanying tests. The AI delivers functional code quickly, though some refinements are still required.

Scenario 4 – Bug Fixing : A bug arises from two‑digit variable placeholders ($11) being incorrectly parsed as $1 and 0. The author provides the problematic code and asks ChatGPT‑4 to fix it. The model proposes a corrected parsing algorithm.

Scenario 5 – Naming Suggestions : ChatGPT‑4 assists in generating clearer variable and function names, improving code readability.

Conclusion : Across all scenarios, ChatGPT‑4 demonstrates strong capabilities in code generation, readability improvement, unit‑test creation, and iterative problem solving. While it cannot replace engineers entirely, it can substantially accelerate routine, low‑risk development tasks.