Fix incorrect DROP_SIZE usage

[Mike1117]

Previously, measure() only recorded timings for indices in the middle range [DROP_SIZE, N_MEASURES - DROP_SIZE), while update_statistics() assumed all entries were available from index 0.

This mismatch led to statistical analysis on unmeasured (zero-filled) samples, potentially skewing results or preventing detection thresholds from being reached.

Now:

• measure() records all samples
• update_statistics() discards DROP_SIZE samples on both ends
• Sample accounting matches ENOUGH_MEASURE estimation

Change-Id: Ibb1515043da5f56d72fe34fd5c78e2283df9a993

[jserv]

Show more experiment results about "statistical analysis on unmeasured (zero-filled)
samples, potentially skewing results or preventing detection thresholds
from being reached."

[Mike1117]

To validate the claim about "statistical analysis on unmeasured (zero-filled) samples", I added debug output in update_statistics() to print all exec_times[i] and corresponding classes[i].

Below are two outputs:

---

Before fix (measure() only measures middle range)

Note: exec_times[i] = 0 for unmeasured indices (head and tail), but update_statistics() still included them starting from i = 10.

i = 10 | class = 1 | exec_time = 0
i = 11 | class = 1 | exec_time = 0
i = 12 | class = 0 | exec_time = 0
...
i =   18 | class = 0 | exec_time = 0
i =   19 | class = 1 | exec_time = 0
i =   20 | class = 0 | exec_time = 1178
i =   21 | class = 1 | exec_time = 1140
i =   22 | class = 1 | exec_time = 1064
...
i = 129 | class = 1 | exec_time = 1064
i = 130 | class = 0 | exec_time = 0
...
i =  143 | class = 1 | exec_time = 0
i =  144 | class = 0 | exec_time = 0
i =  145 | class = 1 | exec_time = 0
i =  146 | class = 1 | exec_time = 0
i =  147 | class = 0 | exec_time = 0
i =  148 | class = 1 | exec_time = 0
i =  149 | class = 0 | exec_time = 0

This shows:

• [10,19] and [130,149] contain zero values
• These were being pushed into t_push(...) → skewing statistical results
• Consider the t-value definition:
  $t = \frac{\bar{X}_1 - \bar{X}_2}{\sqrt{\frac{s_1^2}{N_1} + \frac{s_2^2}{N_2}}}$

Zero-filled execution times will

• Reduce the sample means $\bar{X}_1$, $\bar{X}_2$
• Inflate variances $s_1^2$, $s_2^2$

Both of them will suppress the t-value, especially when the sample size is still small, and will cause the test to overestimate the number of additional measurements required.
Furthermore, this may delay or entirely prevent the detection of actual timing leaks, resulting in false negatives.

---

After fix (measure() records all, update_statistics() uses DROP_SIZE range)

i = 0 | class = 0 | exec_time = 1102
i = 1 | class = 1 | exec_time = 1101
...
i = 149 | class = 1 | exec_time = 1063

• Now all samples have proper timing values
• update_statistics() starts from i = DROP_SIZE and end in i < N_MEASURES - DROP_SIZE.
  This avoids head/tail measurements to eliminate measurement bias caused by warm-up effects
• Results become consistent and threshold will be reached as expected

---

This supports the need for consistent range handling between measurement and statistical analysis.