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English 17:18 Science & Tech

Senior Developers are Vibe Coding Now (With SCARY results)

Movieclips · 27,237 views · Added 1 month ago

Subtitles (494 segments)

00:00

AI generated code is causing some

00:02

serious problems. Security

00:03

vulnerabilities that are introducing

00:05

real threats into applications, sloppy

00:08

code, and bloated pull request. This is

00:10

what the latest reports are showing. It

00:12

turns out that while AI is helping us

00:14

write code faster, it's also degrading

00:16

the quality of our code. And not only is

00:19

it producing more bugs, it's producing

00:21

an entirely new kind of issue

00:23

altogether. Now, even if you're not

00:25

using AI to write code yet, you should

00:27

still know about the type of issues that

00:29

are out there because in one way or

00:31

another, this kind of affects all of us.

00:33

In this video, we're going to break all

00:34

of this down. We want to look at the

00:36

kind of problems these reports are

00:37

seeing. We're going to talk about what's

00:39

causing these issues, and I also want to

00:41

take a look at what we can do to

00:43

mitigate these risks.

00:47

Seeing how things changed from the start

00:49

of early 2025 to the second half of the

00:52

year made me realize that I can no

00:54

longer ignore this AI shift. I saw the

00:57

same senior developers who at the

00:59

beginning of the year brush it off as AI

01:01

slob or fancy autocomplete start

01:04

embracing it like never before. One

01:06

report that stood out to me was done by

01:08

a cloud provider. And this report

01:10

surveyed 791 senior developers all with

01:14

10 or more years of experience. And this

01:16

report stated that 32% of the senior

01:19

developers they surveyed said they had

01:21

shipped AI generated code. Now to be

01:24

honest, these numbers seem to fluctuate

01:26

depending on who does the survey and

01:28

what time of the year this was done, but

01:30

generally speaking, this number seems to

01:32

be pretty accurate based on what I've

01:34

seen. Whether you're convinced that this

01:36

is a good idea or not isn't really the

01:38

point anymore. The fact is there's a lot

01:40

of decision makers who are buying in and

01:42

for now this seems to be the direction

01:44

we're headed in. So what are these

01:46

reports finding? Well to start this

01:49

report by Veraricode found that 45% of

01:52

code generated by AI failed security

01:55

test and introduced OASP top 10 security

01:58

vulnerabilities into code. If you don't

02:00

know what OASP is, it's a globally

02:02

recognized foundation that provides

02:04

guidelines and information on software

02:06

security. And every year they release a

02:09

list of top 10 security vulnerabilities

02:11

that applications face. On this list

02:13

includes things like cross-ite scripting

02:16

attacks, SQL injections, misconfigured

02:19

access controls, and much more. So these

02:22

are not minor issues. What's even worse

02:24

is that these results remained largely

02:26

unchanged even as models dramatically

02:29

improved. Another report which was done

02:31

by code rabbit reviewed 470 open- source

02:34

GitHub poll request and this report had

02:37

similar findings when it came to

02:38

security being an issue in AI generated

02:41

code. This report showed that on average

02:44

AI poll requests had 10.83 issues per PR

02:48

while human generated code had 6.45

02:51

issues per poll request. That's 1.7

02:54

times more issues in AI generated code.

02:57

Now, if we break this down by severity

02:58

levels, AI underperformed in all metrics

03:01

here. When it comes to critical issues,

03:03

it was 1.4 times higher. Major issues

03:06

were 1.7 times higher. And when it comes

03:09

to minor issues, it was nearly double

03:11

for AI generated code. Now, let's take a

03:13

minute to see what's actually happening

03:15

here. So, this report breaks these down

03:17

into four categories. We can see that we

03:19

have logic and correctness, code quality

03:22

and maintainability, security findings,

03:25

and performance issues. For logic and

03:27

correctness, the two that stand out for

03:29

me are going to be the incorrect

03:30

dependencies and sequence and

03:32

misconfiguration. I see this quite a bit

03:35

where I'm working with a newer library

03:37

or package and anytime I'm trying to

03:40

write some code, if it can't figure it

03:42

out based on the latest version, I'm

03:43

going to get imports that are based on

03:46

an older version or simply just off of

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