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-1

-00:00:00,110 --> 00:00:02,570

-So let me use a slightly expanded version of this

-

-2

-00:00:02,570 --> 00:00:05,560

-grid. Although we're indeed looking at a needle in a haystack.

-

-3

-00:00:05,560 --> 00:00:09,560

-And failing inputs are generally sparse, very sparse, in the input

-

-4

-00:00:09,560 --> 00:00:12,890

-domain. However, they tend to be dense in some parts of

-

-5

-00:00:12,890 --> 00:00:15,860

-the domain. Like here or here. So how can we leverage

-

-6

-00:00:15,860 --> 00:00:18,920

-this? The fact that the failures are dense in some subdomains?

-

-7

-00:00:18,920 --> 00:00:22,290

-As it turns out, the domain is naturally split into partitions.

-

-8

-00:00:22,290 --> 00:00:25,340

-Where partitions are areas of the domain that are treated homogeneously

-

-9

-00:00:25,340 --> 00:00:28,070

-by the software. And this is what happens, that normally,

-

-10

-00:00:28,070 --> 00:00:31,020

-failures tend to be dense in this partitions. So the way

-

-11

-00:00:31,020 --> 00:00:34,000

-to leverage this characteristic of failures, is that we don't know

-

-12

-00:00:34,000 --> 00:00:36,950

-want to pick inputs randomly, in the input domain. Just here

-

-13

-00:00:36,950 --> 00:00:39,460

-and there. Rather we want to do two things. First we

-

-14

-00:00:39,460 --> 00:00:43,300

-want to identify partitions of our domain. And second we want

-

-15

-00:00:43,300 --> 00:00:46,950

-to select inputs from each partition. And by doing so, we

-

-16

-00:00:46,950 --> 00:00:50,300

-can dramatically increase our chances to reveal faults in the code.

-

-17

-00:00:50,300 --> 00:00:54,170

-So the name that is normally used for this process, is partition testing.