⚡ Bolt: O(n) calculation optimization in Dashboard

[alvin000009238]

💡 What: Combined .map(), Math.max(), and .forEach() into a single for loop inside updateStatistics.
🎯 Why: To reduce redundant loop iterations and avoid intermediate array allocations. Specifically, it prevents RangeError: Maximum call stack size exceeded that occurs when using the spread operator (Math.max(...scores)) with potentially massive arrays.
📊 Impact: Guarantees a single O(n) pass over the subject list, minimizing memory allocation/garbage collection overhead, and prevents application crashes on render due to stack size limits.
🔬 Measurement: Verify zero visual regression and improved execution logic within frontend Playwright testing; monitor runtime without throwing stack errors for massive arrays.

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PR created automatically by Jules for task 6954969009140730277 started by @alvin000009238

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[gemini-code-assist]

Code Review

This pull request optimizes the updateStatistics function by merging the maximum score calculation and weighted average computation into a single loop, reducing array allocations and preventing potential stack overflow issues. Feedback suggests using a for...of loop for better readability and employing Math.max within the loop to ensure idiomatic code and consistent NaN handling.

[gemini-code-assist]

Using a for...of loop would improve readability while maintaining the performance benefits of a single pass. Additionally, replacing the if condition with highest = Math.max(highest, score) is more idiomatic and ensures consistent NaN handling. If any scoreValue is NaN, highest will correctly become NaN, matching the behavior of weightedAvg and the original implementation (which used Math.max(...scores)).

    for (const subject of subjects) {
        const score = subject.scoreValue;
        highest = Math.max(highest, score);

        const weight = getSubjectWeight(subject.SubjectName);
        totalWeightedScore += score * weight;
        totalWeight += weight;
    }

[Copilot]

Pull request overview

Optimizes the dashboard’s updateStatistics hot path by removing intermediate array creation and avoiding Math.max(...scores) (which can throw for very large arrays), while keeping statistics rendering in a single O(n) pass.

Changes:

• Replaced subjects.map(...)+Math.max(...)+forEach(...) with a single indexed for loop.
• Computes highest and weighted average in one pass to reduce allocations and prevent spread-related runtime errors.

Comments suppressed due to low confidence (1)

frontend/dashboard.js:153

• This change is specifically meant to prevent Math.max(...scores) from throwing with very large subject lists, but there’s no automated test covering the new statistics calculation or the “massive array” regression. Consider extracting the calculation into a small pure helper (e.g., returns { highest, weightedAvg }) and adding a unit test that runs it on a large input to ensure it doesn’t throw and still returns correct values.

    // 計算最高分與加權平均，合併迴圈以減少陣列分配與迭代，並避免 Math.max 展開大陣列時可能發生的 Stack Overflow
    let highest = -Infinity;
    let totalWeightedScore = 0;
    let totalWeight = 0;

    for (let i = 0; i < subjects.length; i++) {
        const subject = subjects[i];
        const score = subject.scoreValue;

        if (score > highest) highest = score;

        const weight = getSubjectWeight(subject.SubjectName);
        totalWeightedScore += score * weight;
        totalWeight += weight;
    }

    const weightedAvg = totalWeight > 0 ? totalWeightedScore / totalWeight : 0;

    document.getElementById('avgScore').textContent = weightedAvg.toFixed(1);
    document.getElementById('totalSubjects').textContent = subjects.length;
    document.getElementById('highestScore').textContent = highest;

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[Copilot]

scoreValue can be NaN (see getNumericScore() fallback path), and the new loop can leave highest as -Infinity when all scores are non-finite, which then renders -Infinity in the UI. Also, a single NaN score will propagate totalWeightedScore to NaN and make weightedAvg.toFixed(1) show NaN. Consider skipping non-finite scores (and their weights) when computing highest/average, and rendering '--' (or 0) when no valid numeric scores exist.