Time complexity cannot be measured in FLOPs. Time complexity needs to be measured in "Big-O" notation. For a thesis you would typically measure average case and worst case (unless those are the same.)
Suppose that your program starts with a loop that initializes 1000 one-byte locations to 0 (for example for some kind of histogram, so independent of data size.) How many FLOPs is that? Is it 1000? Is it 1000 for the zeroing, plus 1000 increments for the for loop, plus 1000 comparisons? Is it 125 of each of those because the compiler unrolls the loop and then combines 8 adjacent byte zeros into a single floating point zero assignment? Is it "zero" because of Demand Page Zero hardware?
Whatever it is, for a given compiler it is constant, and that means that the complexity is constant, O(1) in big-O notation. But FLOPs cares about that.
If the algorithm involves a value to a positive integer power, then is the compiler going to use binary decomposition of the power to determine the optimal sequence of multiplicationqq? Is the compiler going with substitute log(), multiply, exponential and if so does that count as 3 FLOPs? Does the compiler emit some kind of hardware exponential instruction and so only count as one FLOP?
None of these are important to time complexity, so no-one measures FLOPs for that purpose.
FLOPs are a hold over from the days when instructions were performed entirely sequentially, with no pipelining and when there was only one ALU (Arithmetic Logic Unit), and were used to calculate actual runtime. FLOPs divided by MIPS (millions of instructions per second) to get seconds of run time. That is not time complexity.
