"O(n) issue" is generic for some asymptotic optimization issue. It doesn't literally mean it was running in O(n) and there was a better O(n log n) solution. I

"O(n) issue" is generic for some asymptotic optimization issue. It doesn't literally mean it was running in O(n) and there was a better O(n log n) solution. It could be O(n^2) and there was an O(n) solution or O(n) when there was an O(log n) solution or O(n!) when there was an O(n^2) solution. It just means there was a poor algorithm choice / design decision for some part of the code when a more efficient option is available.