2026年开发者调查报告 State of Code Developer Survey report

StateofCode

DeveloperSurveyreport

2026

StateofCodeDeveloperSurveyreport

2026

TableofContents

2/57

Introduction

03

Aboutthisreport

04

Howdevelopersare(really)usingAI

05

Vibecheck:DodeveloperstrustAI?

09

ThetopAItools,andhowthey’reused

15

ThesecondactofAI:Agents

20

Meetthenewdevelopertoil

25

ThetrickyrelationshipbetweenAIandcodesecurity

29

Tryingnottoexpandtechnicaldebt

34

AIcodingandtheexperiencegap

38

HowenterprisesandsmallbusinessesareapproachingAI

44

SonarQube:TheessentialverificationlayerforAIcode

50

Appendix:Aboutoursurveydemographics

53

StateofCodeDeveloperSurveyreport2026

returntoTOC↑

3/57

Introduction

Sonaranalyzesover750billionlinesofcodeeachday,whichgivesusaunique

understandingofcode.Thisyear,wekickedoffanewreportseriescalledthe

Stateof

Code

tosharesomeofourknowledgewithdevelopersandtechnologyleadersmorebroadly.

We’vealreadywrittenreportsoncode

reliability

,

security

,

maintainability

,andthe

specificcodingpersonalitiesofleadingLLMs

.Thosereportsfocusedprimarilyonthe

codeitselfandthemodelscreatingit.Nextwewantedtoexpandthatviewtoincludethestateofcodefromtheperspectiveofthepeopledoingthework—thedeveloperswritingcodeandcollaboratingwithAItobuildit.

Specifically,wewantedtogetareadonwhatischangingforthem.AsAIrapidly

shiftsthemechanicsofcoding,weneedtounderstandtheon-the-groundreality—theefficiencies,thefrustrations,andthenewworkflowsemerging.Toensureweaddrealvaluetotheindustrynarrative,wedesignedthisstudytobuilduponthefindingsin

otherleadingdevelopersurveysandtoanswerthepressingquestionswestillhadafterreadingthem.

Aftersurveyingmorethan1,100developers,wesawacriticalnewnarrativeemerging.

Simplyput,theexplosioninAI-generatedcodehasn'tleddirectlytomassiveandmuch-hypedproductivitygainsyet.Instead,averificationbottleneckhasemerged,creating

awholenewsetofchallenges.Aswecoverthisandotherfindings,we’llexplorehow

theAI-codingshiftismanifestingacrosssoftwareengineeringorganizationsacrosstheworld,andhowtheyareadaptingtoaddressit.

StateofCodeDeveloperSurveyreport2026

returntoTOC↑

4/57

Aboutthisreport

The2026StateofCodeDeveloperSurveywasaquantitativeonlinesurveyconductedamongprofessionalsoftwaredevelopers.Fieldworkforthesurveyranthroughout

October2025.

Thefinalsamplesizeforthestudyincluded1,149responses,distributedglobally.All

respondentswere18yearsorolder,employedfull-timeorself-employedinatechnologyrole(thevastmajorityworkedinsoftwareengineering,withsomeothersinfieldsrelatedtoITops,datascience,orproductmanagement),writecodeormanagedevelopers

usingatleastoneprogramminglanguage,andhaveusedAIaspartoftheirjobwithinthepastyear.

Furtherdetailsaboutthereport’sdemographicmakeupareavailableintheappendix.

StateofCodeDeveloperSurveyreport2026

returntoTOC↑

5/57

Howdevelopersare(really)usingAI

72%ofdeveloperswhohavetriedAIuseiteveryday

AI-assistedcodingisofficiallyastandardpartofthedeveloperworkflow.72%ofdeveloperswhohavetriedAIcodingtoolsnowusethemeveryday.

72%ofdeveloperswhohavetriedAIuseiteveryday

Howfrequently(doyou/yourteamorcompany)useAIcodingtoolsinyourdevelopmentworkflow?

72%

everyday

Multipletimesaday42%

Occasionally6%

Weekly7%

Afewtimesaweek15%

Daily30%

n=1,148

Developersalsoreportthat42%oftheircodeiscurrentlyAI-generatedorassisted—asharethattheypredictwillincreasebyoverhalfby2027,andupfromonly6%in2023.

AverageshareofAI-assistedorgeneratedcodecommittedbydevelopers

What%ofthecodeyoucommittedorcontributedwas/willbegeneratedorsignificantlyassistedbyAItools?

65%

55%

42%

6%

2023

19%

2024202520262027

n=979

StateofCodeDeveloperSurveyreport2026

returntoTOC↑

6/57

AndAIisnotjustforsideprojectsandexperimentation.DevelopersareusingAIacrossthegamutofsoftwareprojects,fromprototypes(88%)andinternal,non-criticalproductionsoftware(83%)allthewaytocustomer-facingapplications(73%)andevenmission-criticalservices(58%).

DevelopersareusingAIacrossthegamutofsoftwareprojects

Thinkingaboutyourteam/company,whichofthefollowingtypesofworkinvolvestheuseorassistanceofAI?

Prototypes,experiments,proofsofconcept

Productionsoftware

forinternal,non-critical

workflows

Productionsoftware

forcustomer-facing

applications

Productionsoftware

forbusiness-criticalormission-criticalservices

n=1,149

StateofCodeDeveloperSurveyreport2026

returntoTOC↑

7/57

Usecases,andthegapbetweenusageandeffectiveness

JustbecauseAIisusedeverywheredoesn'tmeanit'seffectiveevenly.Whenwelookathow

developersareusingAIversushoweffectivetheyfinditforthosespecifictasks,acleargapsometimesemerges.Inaperfectworld,adoptionwouldincreasemoreorlesslinearlywitheffectiveness.But

inpractice,weseeusecaseswheredevelopershavereportedloweffectivenessbuthigherratesofadoption.

UnderstandingAIusecases

Forwhichofthefollowingtasksisyourteam/companyusingAIcodingtools?

HoweffectiveareAIcodingtoolsforeachof

thefollowingtasksyouoryourteam/companyhasusedthemfor?

100%

Usecaseadoption%whouse

0%Usecaseeffectiveness%ratedextremely/veryeffective100%

Usecase

Effectiveness↓

Adoption

A

Writingdocumentation

74%

74%

B

Explainingorunderstandingexistingcode

66%

78%

C

Vibecoding/creatingnewprojectswithmostlyAI-generatedcode

62%

48%

D

Generatingtests

59%

75%

E

ResearchingtechnicalsolutionsorexploringAPIs/libraries

59%

74%

F

Translatingcodefromonelanguagetoanother

58%

50%

G

Assistingdevelopmentofnewcode

55%

90%

H

Codereview

47%

55%

I

Debuggingcode

44%

65%

J

Refactoringoroptimizingexistingcode

43%

72%

KAddingorupdatingfunctionalityinexistingcode

42%

76%

n=1,149

StateofCodeDeveloperSurveyreport2026

returntoTOC↑

8/57

ThebestexampleofthisisalsothemostcommonusecaseforAI:assistingwithnewcodedevelopment(90%ofdevelopers).Only55%ofthoseusersratedAIas"extremelyorveryeffective"forthattask.

Refactoringoroptimizingexistingcodeshowsasimilareffectivenessgap:while72%ofdevelopersreportusingAItoolsforthisusecase,only43%attesttoitseffectivenessinthattask.

WhereAIreallyshines

ThedatashowsAItoolsaremosteffectiveattasksthatinvolveexperimentationorworkingwithwhat'salreadythere.

ThetaskswhereAIismosteffectiveinclude:

•Writingdocumentation(74%effective)

•Explainingorunderstandingexistingcode(66%effective)

•Vibecoding/green-fieldprototyping(62%effective)

•Generatingtests(59%effective)

DevelopershaveembracedAIasadailypartner,butthey'refindingit'samuchbetter"explainer"and

"prototyper"thanitisa"maintainer"or"refactorer"—atleastfornow.It'shighlyeffectiveatgeneratingnewthings(docs,tests,newprojects)butstrugglesmorewiththecomplex,nuancedworkofmodifyingandoptimizingexisting,mission-criticalcode.

Thetakeaway

Developersarepragmatic:they’vefullyembracedAIasadailyassistant,usingittowrite

documentationandgeneratetests.Buttheyalsoknowitslimits,showinglessconfidenceinits

abilitytohandlecomplex,existingcode.Thisgapbetweenhighusageandselectiveeffectivenessisn'tjustaboutfeatures;it'saboutconfidence.Whenthestakesarehigh,howmuchdodevelopersreallytrustthecodeAIgenerates?Thisbringsustothecoreoftheissue:developertrust.

StateofCodeDeveloperSurveyreport2026

returntoTOC↑

9/57

Vibecheck:DodeveloperstrustAI?

96%ofdevelopersdon’tfullytrustthatAI-generatedcodeisfunctionallycorrect

It'snosecretthatAIischangingdevelopment.Ourstudyfoundthatdevelopersareseeingrealbenefits,reportinganaveragepersonalproductivityboostof35%.Ontopofthat,morethanhalf(54%)say

they’remoresatisfiedwiththeirjobasaresultofAI.

Andwhile82%ofdevelopersagreeAIhelpsthemcodefaster,and71%sayithelpssolvecomplexproblemsmoreefficiently,thisspeedcreatesanewchallenge:atrustgap.96%ofdevelopersdon’tfullytrustthatAI-generatedcodeisfunctionallycorrect.

Somewhatagree25%

Somewhatdisagree

31%

Neitheragreenordisagree23%

n=1,149

CompletelyagreeCompletelydisagree

4%17%

96%ofdevelopersdon’tfullytrustthatAI-generatedcodeisfunctionallycorrect

TowhatextentdoyouagreewitheachofthefollowingstatementsasitrelatestotheuseofAIcodingtoolsandAI-assistedorgeneratedcode?|Choice:ItrustthatAIcodeisfunctionallycorrect

StateofCodeDeveloperSurveyreport2026

returntoTOC↑

10/57

Theverificationbottleneck

Givenourfindinginthepriorsectionthat96%ofdevelopershaveahardtimetrustingthatAI-generatedcodeisfunctionallycorrect,youwouldthinkthatverificationofAIcodeiswidespread.However,thisisnotthecase:only48%ofdevelopersalwayschecktheirAI-assistedcodebeforecommitting.

48%ofdevelopersalwayschecktheirAI-assistedcodebeforecommitting

TowhatextentdoyouagreewitheachofthefollowingstatementsasitrelatestotheuseofAIcodingtoolsandAI-assistedorgeneratedcode?|Choice:IalwayscheckmyAI-generatedorassistedcodebeforecommittingit

48%

completelyagree

Completelyagree48%

Completelydisagree3%

Somewhatdisagree11%

Somewhatagree27%

Neitheragreenordisagree11%

n=1,149

Thisverificationstepisn'ttrivial.WhileAIissupposedtosavetime,developersarespendinga

significantportionofthatsavedtimeonreview.Nearlyalldevelopers(95%)spendatleastsome

effortreviewing,testing,andcorrectingAIoutput.Amajority(59%)ratethateffortas"moderate"or"substantial."

Infact,38%ofdeveloperssayreviewingAI-generatedcoderequiresmoreeffortthanreviewingcodewrittenbytheirhumancolleagues.(Thiscontrastswithonly27%whosayitrequireslesseffort.)

StateofCodeDeveloperSurveyreport2026

returntoTOC↑

11/57

Whyisitsomuchwork?61%agreethat"AIoftenproducescodethatlookscorrectbutisn'treliable."

That'sacriticalfinding—itmeansAIcodecanintroducesubtlebugsthatarehardertospotthantypicalhumanerrors.Thesamepercentage(61%)agreethatit"requiresalotofefforttogetgoodcodefromAI"throughpromptingandfixing.

61%ofdevelopersagreethatAIoftenproducescodethatlookscorrectbutisn'treliable

TowhatextentdoyouagreewitheachofthefollowingstatementsasitrelatestotheuseofAIcodingtoolsandAI-assistedorgeneratedcode?|Choice:AIoftenproducescodethatlookscorrectbutisn'treliable

61%

agree

Completelydisagree5%

Somewhatdisagree15%

Neitheragreenordisagree19%

Completelyagree17%

Somewhatagree45%

61%ofdevelopersagreethatitrequiresalotofefforttogetgoodcodefromAI

TowhatextentdoyouagreewitheachofthefollowingstatementsasitrelatestotheuseofAIcodingtoolsandAI-assistedorgeneratedcode?|Choice:Itrequiresalotofeffort(inprompting,fixing,etc.)togetgoodcodefromAI

61%

agree

Completelydisagree7%

Somewhatdisagree19%

Neitheragreenordisagree13%

Completelyagree19%

Somewhatagree41%

n=1,149;Percentagesarerounded

StateofCodeDeveloperSurveyreport2026

returntoTOC↑

12/57

WhereAI'simpactisfelt(andwhereit'snot)

Developersareclearlyshippingcodefaster,butwhetherthisconsistentlytranslatestobetteroutcomesislessclear.

While70%sayAIhaspositivelyimpactedtheirtime-to-market,lessthanhalf(47%)sayit'shada

positiveimpactontheend-userexperienceoronreducingtechnicaldebt.Thismakessense:ifyou'reshippingcodethatlooksrightbutisn'treliable,you'renotimprovingtheuser'sexperienceorthelong-termhealthofyourcodebase.

AIimpactismostlyseenindeveloperproductivityandtime-to-market,butthere'sroomforgrowthinmultipleotheraspects

WhatimpacthasAI-generatedorassistedcodehadonyourteam/companyforeachofthefollowing?

Verypositiveimpact

Somewhatpositiveimpact

ImpactofAIuseonkeyactivities

%

Total

Developerproductivity

26

63

89%

Time-to-market

19

51

70%

Featureorfixreleasefrequency

13

46

60%

Codequality

13

45

58%

Codemaintainability

11

44

56%

End-userexperience

10

37

47%

Technicaldebt

8

39

47%

Rework/patchcosts

7

36

42%

Defectrates

6

33

39%

Vulnerabilityrates

5

29

34%

Frequencyofoutages/incidents

4

22

25%

Severityofoutages/incidents

4

20

24%

n=1,149

SonarQubeusersreportstrongerpositiveimpactsoncodequality,technicaldebt,reworkcosts,defects,andvulnerabilitiesthannon-users.ThissuggeststhathavingasystematicverificationprocessinplaceiskeytoturningAI'sspeedintoreal-worldqualityimprovements.

StateofCodeDeveloperSurveyreport2026

returntoTOC↑

13/57

Anewsetofskillsandconcerns

Thisnew"verify"stepisalsoredefiningwhatitmeanstobeadeveloper.Whenweaskedwhatskills

willbemostimportantintheAIera,thenumberoneanswerwas"reviewingandvalidatingAI-generatedcodeforqualityandsecurity"(47%).Thiswasfollowedby"efficientlypromptingAItools"(42%).

47%

42%

27%

25%

24%

23%

23%

19%

17%

16%

11%

Collaboratingandmentoringotherdevelopers10%

Codequalityreview&validationisthemostimportantskillfordevelopersintheAIera

WhichofthefollowingskillswillbemostimportantfordeveloperstohaveintheevolvingAI-assistedorgeneratedcodingenvironment?

ReviewingandvalidatingAI-generatedcodeforqualityandsecurity

EfficientlypromptingAItoolstogeneratecodethat

meetsrequirements

Translatingdomainknowledgeintocoderequirements

Architectingcomplexsystemsandintegrations

IdentifyingandmitigatingsecurityrisksintroducedbyAI-generatedcode

Creativeproblem-solvingandinnovation

Maintainingsystemperformance,reliability,and

efficiencyinproductionenvironments

Shapingcodingstandardsandgovernance

IntegratingAIoutputsintoworkflowsandtoolchains

RefactoringanddebuggingAI-generatedcode

Buildingresilientsystems

n=1,149

StateofCodeDeveloperSurveyreport2026

returntoTOC↑

14/57

Ultimately,AIisspeedingupcodegeneration,butit'salsocreatedabottleneckattheverificationstageofsoftwaredevelopment,withmoreworknowrequiredtoreviewcode.

Thetakeaway

It'sclearthatthisnewverificationbottleneckisthecentralchallengeintheageofAI-assistedcoding.Butonecriticalcomponentofcodereviewisknowingtheprovenanceofthecode.

Thisraisesacriticalquestion:whichcodegenerationtoolsareevenbeingused,andhowaredevelopersaccessingthem?Thisleadsustoanotherkeyfinding:therapid,oftenungoverned,sprawlofAItools.

StateofCodeDeveloperSurveyreport2026

returntoTOC↑

15/57

ThetopAItools,andhowthey’reused

CopilotandChatGPTleadthefield

AIadoptionisn'tjusthappening;it'salreadystartingtocoalescearoundfavorites.Thetwomost

dominanttools,GitHubCopilotandChatGPT,areusedby75%and74%ofdevelopers,respectively,withClaudefollowingat48%.

75%

74%

48%

37%

31%

21%

21%

17%

12%

8%

37%

GitHubCopilotandChatGPTarethemostwidelyusedtoolsforsoftwaredevelopmenttasks

Inthepastyear,whichofthefollowingAIcodingtoolsorAIfeatureshaveyouoryourteam/companyusedforsoftwaredevelopmenttasks?

GitHubCopilot

ChatGPT

Claude/ClaudeCode

Gemini/DuetAI

Cursor

Perplexity

OpenAICodex

JetBrains

AmazonQDeveloper

Windsurf

Others

n=1,149

StateofCodeDeveloperSurveyreport2026

returntoTOC↑

16/57

But,thedatarevealsacomplexandfragmentedpicture:onaverage,developmentteamsjugglefourdifferentAItools.

TheaveragedevelopmentteamusesfourdifferentAItools

Basedonaggregatedanswerstothefollowingquestion:Inthepastyear,whichofthefollowingAIcodingtoolsorAIfeatureshaveyouoryourteam/companyusedforsoftwaredevelopmenttasks?

Percentageofteams(%)

Frequency

Average:4

20

15

10

5

0

1

2345678910

NumberofAItoolsused

n=1,149

Forengineeringleaders,thismeansthatwhileAIisboostingproductivity,it’salsointroducinga“bringyourownAI”(BYOAI)culturethat’srunningaheadofofficialgovernance.

StateofCodeDeveloperSurveyreport2026

returntoTOC↑

17/57

Thepersonalaccountproblem

Italsoturnsoutthatmanyofthetoolsdevelopersareusingaren’tfullyapprovedbytheirworkplaces.

Acrossthetop10AItools,ourdatashowsthat35%ofdevelopersareaccessingthemthroughpersonalaccountsratherthanwork-sanctionedones.

Over50%ofdevelopersuseChatGPTthroughpersonalaccountswhile78%useGitHubCopilotthroughworkaccounts

Inthepastyear,howhaveyoupersonallyusedeachofthefollowingAIcodingtoolsorAIfeaturesforsoftwaredevelopmenttasksaspartofyourwork?

Throughpersonalaccount

Throughwork-sanctionedaccount

Perplexity

63%

31%

ChatGPT(forcoding-relatedtasks,e.g.generation,debugging,explanation)

52%

47%

GoogleGeminiCodeAssist/DuetAI(inGoogleCloud,IDEs)

40%

55%

Claude/ClaudeCode

36%

53%

OpenAICodex

35%

52%

Command-lineAIcodingtools

33%

57%

JetbrainsAIAssistant(orsimilarAIfeaturesin

JetBrainsIDEs)

28%

58%

Cursor(AI-nativeIDE)

27%

64%

AmazonQDeveloper(formerlyCodeWhisperer)

17%

72%

GitHubCopilot/CopilotChat

17%

78%

n=1,147;basesvary

StateofCodeDeveloperSurveyreport2026

returntoTOC↑

18/57

ChatGPTisaperfectexample.While74%ofdevelopershaveuseditinthepastyear,52%ofthoseusersareaccessingitviatheirpersonalaccounts.ThistrendisevenmorepronouncedwithtoolslikePerplexity,where63%ofitsusersarelogginginviapersonalaccounts.

Thisshadowadoptioncreatesamassiveblindspotforsecurityandcompliance.Whendevelopers

usepersonalaccounts,theymightbefeedingsensitivecompanyorcustomerdataintopublicmodels,creatingseriousrisks.

Interestingly,thisisn'tauniversalproblem.Sometoolsareclearlybeingadoptedthroughofficial

channels.GitHubCopilotandAmazonQDeveloper,forexample,showmuchlowerpersonalaccount

use(17%forboth),suggestingamoreformal,top-downrollout.AndCursorissimilar,showingonly27%inpersonalaccountusecomparedto64%onwork-sanctionedaccounts.

Whoisusingwhichtools?

Thedataalsoshowsthattoolchoicevariesbasedoncompanysizeanddeveloperexperience.

•Companysize:Smallercompanies(SMBs)aremorelikelytobeusingChatGPT,Claude,JetBrains,andcommand-lineAItoolsthantheirlargercounterparts.

ChatGPT,ClaudeandJetBrainsarefavoritesamongSMBs

Inthepastyear,whichofthefollowingAIcodingtoolsorAIfeatureshaveyouoryourteam/companyusedforsoftwaredevelopmenttasks?

9

SMBMid-marketEnterprise

75

79*737676

69

57*

47

37

4240

313328

34

22222021*

232319

131416*12

1515

10

%

GitHub

Copilot

ChatGPTClaudeGemini/CursorPerplexity