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AI-AssistedCoding:
AugmentingSoftware
DevelopmentwithGenerativeAI
ExploringtheIntegrationofGenerativeAIinSoftwareEngineeringtoEnhanceCodingandTeamCollaboration
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GenerativeAIissoftwareengineering’slatestandgreatestevolution.
Bringingtomorrow’ssoftwareengineeringparadigmintoview:augmentedsoftwareteams
Howtomoveforward:Aprovenmethodforasoftwareengineeringtransformation
Assesstheorganization’smaturityanddeveloparoadmapwithclearobjectives.
Runreal-worldexperimentationsandmeasuretheimpactofGenerativeAI
DeployGenerativeAIforSoftwareEngineeringatscale
Capgemini’sexperienceinmeasuringGenerativeAIimpact
Measurementprotocolandreal-worldexperimentations:designedforactionableandreliableresults
Prerequisites&SuccessFactorsLessonslearned
Wecandrivesoftwareengineeringtransformationwithyou
Thedawningofaneweraforsoftwareengineering
2AugmentedSoftwareTeams:HowtodrivemaximumassistancefromGenerativeAI
Disclaimer:Thispaperpresentsforward-lookingperspectivesgroundedinthecuΓΓe∩tla∩dscapeofGe∩eΓativeA/./tΓe卩ectstheΓapidadva∩ceme∩t—both
i∩teΓmsofouΓu∩deΓsta∩di∩ga∩dexpeΓie∩ceofGe∩eΓativeA/tech∩ologies./t
dΓawsi∩sightsfΓomexte?!蒩lΓeseaΓch,tech∩ologyleadeΓsaswellasouΓow∩
expeΓime∩tatio∩swithouΓteamsa∩dwithclie∩ts.Asalways,thefutuΓeΓemai∩su∩pΓedictable,butweca∩establisha∩dimagi∩elikelytΓajectoΓiesa∩doutlooks.
Softwareplaysacriticalroleinmodernbusiness,
whetherit’sintegratedintobusinessappsorproducts.Despiteitsubiquity,theultimatechallengeforsoftwareengineeringorganizationshasalwaysbeentorelease
qualitysoftwarefastenoughtokeepupwithconstantlyacceleratingdemand.
Overtheyears,productivityandqualitystandards
havebeenprogressivelyenhancedthroughthe
adoptionofnewmethodsandtechnologies.Agile
andcontinuousproduct-centricapproaches,software
lifecycleautomation,open-sourceecosystems,cloudnativeandcomposablesoftwarearchitectures,DevOpscontinuum,andlow-code/nocodedevelopmenthaveallbeenintroduced.Andeachhasbuiltontheadvancesofpastiterations.
Butdemandforquickertime-to-valueandbettercostefficiencyisstillaccelerating.Thisleavessoftware
organizationsstrugglingtodeliverattheexpectedpace,withtheexpectedquality,whilecontrollingtechnicaldebtandcosts.
3AugmentedSoftwareTeams:HowtodrivemaximumassistancefromGenerativeAI
GenerativeAIissoftwareengineering’slatestandgreatestevolution.
GenerativeAInowstirswonder
andexcitementacrossmany
applicationfields,notleast
softwareengineering.BillGatesiscallingitslatestadvancementthemostrevolutionarytechnologicalachievementinover40years,1
withthepotentialtodramaticallyimprovethewayorganizations
meetbusinessandITchallenges.
Theintersectionofbusiness
andtechnologywillbeattheheartofgenerativeAI’simpact.
Developmentsononeside
influencetheother.Forexample,
efficientsoftwareengineeringwithhighqualitycanreducetimeto
marketandthusprovidebusiness
valueearlier,givingleading
organizationsbothasoftwareandbusiness-orientededge.
OurCapgeminiResearch
Institutereportshowsthat61%
oforganizationsseeenabling
moreinnovativework,suchas
developingnewsoftwarefeaturesandservices,astheleadingbenefitgenerativeAI.Closebehindare
improvingsoftwarequality(49%)andincreasingproductivity(40%).Organizationsareutilizingtheseproductivitygainsoninnovativeworksuchasdevelopingnew
softwarefeatures(50%)and
upskilling(47%).Veryfewaimtoreduceheadcount(4%).2
AccordingtoanotherCapgeminiResearchInstitutesurveyacross
800organizations,67%of
executivesseethemostpotentialforgenerativeAIintheITfunctiontodriveinnovationandcreate
value3Moreover,accordingto
Forresterresearch,“Off-the-shelfandcustomAIsoftwarespend
willdoublefrom$33billionin
2021to$64billionin2025andwillgrow50%fasterthantheoverallsoftwaremarket,withanannualgrowthrateof18%.”4
Inbothlegacysoftware
modernizationandnewsoftwaredevelopmentcontexts,generativeAIgivesbacktimetosoftware
engineers.Theyarefreedup
tofocusonbusinessdemand,
softwarequality,security,andtheadvancedfeaturesrequiredby
newsoftware.
GenerativeAIalsohasapositive
impactonsoftwareprofessionals’jobsatisfaction.69%ofsenior
softwareprofessionalsand55%
ofjuniorsoftwareprofessionals
reporthighlevelsofsatisfaction
fromusinggenerativeAIfor
softwaredevelopment.78%
ofsoftwareprofessionalsare
optimisticaboutgenerativeAI’s
potentialtoenhancecollaborationbetweenbusinessandtechnologyteams5
GenerativeAIwillsupplement
bandwidth-usingthesame
capacity-forhigherproductivityandefficiency,alongwithfaster
timetomarket.Butonlyif
organizationsandtheiremployeescommittogettingonboard,
keepinginmindthatearlyadopterswilltakealeadingpositionin
thefield.
Inaddition,generativeAIpresentsanopportunitynotonlyto
transformbutalsotostandardizeandenhancethedeliveryof
software.Thebenefitsbrought
byusinggenerativeAIinsoftwareengineeringcanextendtoother
areasofthebusinesstocreateevenmorevalue,reduceITcostsand
minimizetechnicaldebt.
OrganizationsusinggenerativeAIhaveseen7%–18%productivity
improvement6inthesoftware
engineeringfunction,compared
totheirinitialestimates.Creatingliteratureanddocumentation,
andwritingcodeandscriptsshowthegreatesttimesavingwith35%maximumand10%average,and
34%maximumand9%average
respectively.7GenerativeAIalso
hasapositiveimpactonsoftwareprofessionals’jobsatisfaction.69%ofseniorsoftwareprofessionals
and55%ofjuniorsoftware
professionalsreporthighlevelsofsatisfactionfromusinggenerativeAIforsoftware.78%ofsoftwareprofessionalsareoptimistic
aboutgenerativeAI’spotentialtoenhancecollaborationbetweenbusinessandtechnologyteams.8
[1]
/The-Age-of-AI-Has-Begun
[2]CapgeminiResearchInstitute“Turbochargingsoftware”,June2024
[3]CapgeminiResearchInstitute“GenerativeAIinOrganizations”,July2023
[4]GlobalAISoftwareForecast,ForresterResearch,Inc.September29th,2022
[5]CapgeminiResearchInstitute“Turbochargingsoftware”,June2024
[6]Bytotalproductivityimprovementwemeanoverallimprovement
intheproductivityoftheindividualfromalltypesoftasksacceleratedbygenerativeAI.
[7]CapgeminiResearchInstitute“Turbochargingsoftware”,June2024
[8]CapgeminiResearchInstitute“Turbochargingsoftware”,June2024
4AugmentedSoftwareTeams:HowtodrivemaximumassistancefromGenerativeAI
Bringingtomorrow’ssoftwareengineering
paradigmintoview:augmentedsoftwareteams
WebelievegenerativeAIwill
graduallytransformtheway
softwareisdeveloped.Theadventoflargelanguagemodels(LLMs)hasintroducedacompelling
rationaleforaparadigmshifttomoreAI-assisted(augmented)
softwareteams.TheintegrationofgenerativeAIintosoftware
engineeringoffersthepromiseofsignificantlyelevatedproductivityandenhancedquality.The
foundationalprinciplesand
methodsofAgileandDevOpsareretained,includingcollaboration,
adaptability,timetovalue,productcentricity,andcontinuousfeedbackloops.
ByworkingwithAIassistants
poweredbyLLMs,augmented
softwareteamscanautomate
mundanetasks,andpromote
morenuanced,datadriven
decisionmaking.Thisoptimizesthesoftwarelifecycleandhelpstoachievemoremilestonesalongtheway.
Wecallthisapproach
conversationalsoftware
engineering.Thisiswheresoftware
teamsprompttheAIassistant,
askingittogeneratesnippetsofcode,troubleshootissues,orevenhelpindesigningandarchitectingthesoftware.Theideaisto
makethesoftwaredevelopmentworkflowmoredynamicand
interactive,throughcontinuous
conversationswiththeAIassistantinthedevelopmentenvironment.It’sawaytostreamlinetasksandsolveproblemsmoreefficiently,
enablingsoftwareteamstofocusonmorecomplexandcreativeaspectsthatboostoverallproductivity.
Themodernevolutionofpair
programmingwithgenerative
AIAugmentedsoftwareteams
caneffectivelybenefitfromand
optimizeusageofgenerativeAI
througharevisedorganizationandnewwaysofworking,guidedby
thefollowingprinciples:
?Augmentedpairprogramming:
Thebasicunitofanaugmentedsoftwareteamisoneormultiplepairsofsoftwareengineers
workingintermittentlywitheachotherorindividuallywiththe
AIassistant.
?AI-humancollaboration:TeammembersworkindividuallywiththeAIassistanttoautomate
repetitivetasks,understandandsolveproblems,orbrainstorm
ideas,leveraginganLLM’sspeedandknowledge,whileensuringhumancreativity.
?Human-humancollaboration:
AfterconversationswithanAIassistant,humansreviewthegeneratedoutputandenhanceitsquality.
?Seniorcoordination:Aseniorleadoverseestheaugmentedteam,coordinatingefforts
andresolvingconflicts,while
ensuringcontrolandvalidationtopromoteasmoothworkflowandhealthyteamdynamic.
[9]CapgeminiResearchInstitute“Turbochargingsoftware”,June2024
AccordingtoCapgeminiResearchInstitute,thebiggestgapin
essentialprerequisitesisusually
accesstoplatformsandtools,
includingIDEs,automationand
testingtools,andcollaboration
tools.Only27%oforganizationsclaimtohaveaboveaverage
availabilityofthese.Oftesting
domainprofessionals,24%say
theyhaveaccesstothesetools,
comparedto19%ofproject
andprogrammanagement
professionals,furtherhighlightinggapswithinthesoftware
engineeringfunction.9
5AugmentedSoftwareTeams:HowtodrivemaximumassistancefromGenerativeAI
DeepdiveonCodingAssistants
Applyingthetechnologytosoftwareengineeringwillsignificantlyassistsoftwareteamsinthemultitudeoftaskstheyperformacrossthetraditionalsoftwaredevelopmentlifecycle(SDLC).
Forexample,softwareengineersusegenerativeAIwhentransformingbacklogstoriesintosoftware,throughdesignandcodingactivities.GenerativeAIcanbeharnessedtocreatedesignoutputssuchasuserinterfacemockups,
entitymodels,andapplicationprogramminginterfaces(APIs).Thisleadstoasignificantproductivityimprovementwithoutcompromisingquality,asdesignoutputsarealwaysreviewed,updated,andvalidatedbysoftwareengineersortechnologyleads.
AlthoughadoptionofgenerativeAIforsoftwareengineeringisstillinitsearlystages,functions.GenerativeAIisexpectedtoplayakeyroleinaugmentingsoftwareworkforcewithbetterexperience,toolsandplatforms,andgovernance,assistinginmorethan25%ofsoftwaredesign,development,andtestingworkby2026.10
GenerativeAIisalsopoisedtoredefineconventionalprogrammingpracticesbyshiftingthefocusfromcodingtopromptengineeringandcodeproofreading,asconfirmedbyAndrejKarpathy,anOpenAIcomputerscientist,whorecentlysaid:“thehottestnewprogramminglanguageisEnglish.”11Asanexample,usingplainlanguage,softwareengineerscandescribetheintendedfunctionalityofasoftwarefeature,thenreview,update,andvalidatethe
generatedoutput.Therearemanyotherexamples,suchasauto-completionofcode,generatingcodeforunittesting,(retro)documentation,andcodemigrationfromonelanguagetoanother.
AveryimportantvalueofgenerativeAIisthatitsupportsdevelopersalreadyduringcoding.Itcaneithersuggestcleancodedirectlyorevaluateexistingcodetoimprovesoftwarequalityifitfindsissues.
[10]CapgeminiResearchInstitute“Turbochargingsoftware”,June2024
[11]
/karpathy/status/1617979122625712128?lang=en-GB
6AugmentedSoftwareTeams:HowtodrivemaximumassistancefromGenerativeAI
Howtomoveforward:
Aprovenmethodforasoftwareengineeringtransformation
Tostartthisjourneywith
generativeAI,organizations
willreaphugebenefitsfrom
partneringwithanexperiencedandtrustedadvisor.InadditiontohavingconsiderableAIexpertise,thispartnershouldbereadyto
overseeexperimentationsandformcollaborativeprojectteamsthateventuallyflourishintojointsoftwarehouses.
Avarietyoffactors,suchas
companysize,staffingpyramid,industry-specificgoals,meanthateachorganizationalunit
progressesatadifferentpaceandwithadifferentmodel.Therefore,eachwillhavedistinctneedsandchallengestoconsiderbefore
generativeAIcanbecomeapartofdailyoperations.
It’simportanttofocusonthreeareas:
?Assesstheorganization’smaturityanddeveloparoadmap.
?Runexperimentationsandmeasureimpact.
?Deploymentatscale.
Assesstheorganization’smaturityanddeveloparoadmapwithclearobjectives.
Withinacompany,thereare
usuallymanyvariationsofanSDLC.Typically,everyprogram/project/producthasitsownSDLCversion.Ameticulousassessmentand
deepunderstandingoftheSLDCofanITdomainwillrevealthe
currentmaturityofitssoftwareengineeringprocesses.And
whethertheyalignwithindustrybestpractices.Theevaluation
willalsoidentifyareasfor
improvement,particularlywherebottlenecksorinefficienciesoccur.
Basedonthisassessment,
objectivescanbedefinedby
selectingthemostpromising
transformationenablers,aswellasidentifyingtheirassociatedrisks
andchallenges.SomeenablerswillbepoweredbygenerativeAIwhile,forothers,itwillbemoreamatterofapplyingsoftware
engineeringbestpractices.It’simportanttodefinekeymetrics
tomeasuretheoutcomesofthefuturetransformationandinvolvestakeholdersearlytofostera
collaborativeenvironment.
Finally,acomprehensivevalue,
accessibility,andriskanalysiswillserveasabasisforestablishingaroadmapforchange.Alloptionswillbeweightedandprioritized
tomakeinformeddecisionsaboutresourceallocationasdifferent
domains,teams,androlesaredefinedforeachoption.
7AugmentedSoftwareTeams:HowtodrivemaximumassistancefromGenerativeAI
Runreal-worldexperimentationsandmeasure
theimpactofGenerativeAI
Withtheprioritiesset,it’stime
toselectthebestgenerativeAI
toolstoachievethem.Andput-upguardrailstomanageanylegal
andcybersecurityrisks,while
controllingcosts.Measurementandcontinuousimprovementwillbepivotal.Asgenerative
AIisinsertedintomoreSDLC
processes,organizationsneed
togaugeitsimpact.Thismeansmeasuringcriticalaspectssuch
asproductivityenhancements,
softwarequality,time-to-market,anddeveloperexperience.A
feedbackloopshouldbeputinplacesothepaceandscopeofthedeploymentcanbeadaptedtoaccountforinefficiencies.Orrespondtoevolvingneedsandspecificobjectives.Critically,
theexperimentationsand
measurementsshouldberelevanttothebusinesscontextand
industryenvironmentoftheorganization.
Buttobetrulycompetitive,
organizationsneedmorethan
internalmeasurement.At
Capgemini,wehavedevelopedanindustrializedvaluemeasurementprotocolthatevaluatesthe
objectiveimpactofgenerative
AIacrossanorganization’smanySDLCs.Itisusedtomeasureandcompareanorganization’smetricsagainstourbenchmark,which
factorsinallourrelatedinternal
andexternalgenerativeAIprojects.Thisgivesorganizationsaclearviewofhowtheystackupagainsttheirpeers.
Toputthisintoperspective,
theadoptionofgenerativeAI
forsoftwareengineeringisstill
initsearlystageswith9in10
organizationsyettoscale.27%
oforganizationsarerunning
generativeAIpilots,and11%havestartedleveraginggenerativeAIintheirsoftwarefunctions.
Threeinfour(75%)large
organizations(annualrevenuegreaterthan$20billion)haveadopted(piloted/scaled)
generativeAIcomparedto23%oftheirsmallercounterparts(annualrevenuebetween$1–5billion).12
DeployGenerativeAIforSoftwareEngineering
atscale
Afterexperimentingwith
generativeAIthroughreal-world
pilotsandfullscaledelivery,
manyorganizationswillwantto
broadentheirapplicationsand
possiblyinvolvehundredsoreventhousandsofdevelopers.For
suchlargedeployments,careful
considerationmustbegiventotheorganizationalandHRimplications.
Sincevariousrolesmayneedto
change,it’simportanttointroduceagenerativeAIupskillingprogramthatwillhelpshapethenew
softwareengineeringpyramid,
andaddressthewaysofworkinginrespecttotheskillsrequired.Thiswilllaythefoundationfor
astrategicplanthatgradually
integratesgenerativeAIintothesoftwarelifecycle.Afurtherstep
maybetooffercoachingand
assistancetosteeremployeesastheypreparetoworkalongsideahostofnewgenerativeAItools.
Finally,adedicatedteamshould
besetuptodefinebusinesscases,measureprogress,andensuretheoutcomesmeetexpectations.Thisisessential,asthepaceandscopeofeachdeploymentwillinvariablyneedadjustment.
[12]CapgeminiResearchInstitute“Turbochargingsoftware”,June2024
8AugmentedSoftwareTeams:HowtodrivemaximumassistancefromGenerativeAI
Capgemini’sexperienceinmeasuring
GenerativeAIimpact
Ourexperienceworkingwith
clientsacrossallsectorstells
usthatit’snotaquestionofif
generativeAI-poweredsoftwareengineeringwilldisruptand
reinventanorganization,but
ratherwhen,howquickly,andhowradically.Therearetwoquestionswehearthemostfromour
clients.First,howwillgenerative
AI-basedassistanceaffectsoftwareproductivityandquality.And
second,howwillsoftwareteams’organizationandwaysofworkingchangebecauseofit?
Atthebeginningof2023,
Capgeministartedalarge-scaleglobalprogramtoexperimentwithusecasestomeasure
generativeAI’simpactthrough
experimentations,bothinternallyandjointlywithclients.Theaimwastoseewhereandhowgenerative
AIcanaugmentthemanytasks
softwareteamstypicallyperform.Wedeployedareliableand
consistentmeasurementprotocolandnowweareprogressively
consolidatingourresultsintoa
repositoryforinternalandexternalusage/benchmarking.
Measurementprotocolandreal-worldexperimentations:designedforactionableand
reliableresults
Measuringproductivitywithin
softwaredevelopmentposes
inherentcomplexitiesdueto
themultifacetednatureoftheSDLC,thedynamicenvironmentitoperatesin,andthesubjective
andintangibleaspectsofmanyofitscomponents.Acomprehensivemeasurementapproachmust
encompassbothqualitativeand
quantitativefactors,tailoredtothespecificcontextoftheproject.
9AugmentedSoftwareTeams:HowtodrivemaximumassistancefromGenerativeAI
Similarly,assessingsoftware
qualitypresentschallengesasit
involvesvariousdimensionssuchasfunctionality,performance,
reliability,usability,maintainability,security,andscalability,each
requiringitsownsetofmetricsandcriteria.
Moreover,solicitingfeedback
fromsoftwareengineersutilizingGenerativeAIonadailybasisis
crucial,consideringitsimpacton
thedevelopmentenvironmentandwoΓkHow.TheΓefoΓe,it’simpeΓativetodeviseandimplementapracticalmeasurementprotocol.Thisshouldbefocusedonunderstanding
Ge∩eΓativeAl’si∩Hue∩ceo∩codi∩9andunittestingwithinbespoke
applicationdevelopment,providingacleaΓi∩si9hti∩toitse阡ects.
Themeasurementprotocolisa
combi∩atio∩ofseveΓaldi阡eΓe∩ti∩9Γedie∩tsa∩dawell-de?∩ed
processtoproducetocreate
comprehensible,actionableandreliableresults.Thisincludesa
measurementapproach,metrics,team,a∩dawell-de?∩edpΓocess.
Measurementapproach:A
robustmeasurementapproach
includesstepsformeasuring
progress,includingplanning,
settingbaselines,andrunning
anexperimentation.Theright
measurementmetricsarekey,
andwellexploretheminthenextchapter.Tosupporttheapproach,metricstoolslikeSonarQube,
CAST,Jira,anddevelopersurveysareusedforcollectingand
analyzingdata.Prerequisites
andsuccessfactorsforaproperandconsistentmeasurement
areteamstability,duration,
technology,legalconsiderations,
andcybersecurity.Anormalization
processmusthandlevariability
duringexperimentationexecution,adjustingmetricstochangesin
teamsize,capacity,orcomplexity.
Metrics:Codingvelocityservesasapivotalmetrictomeasureteamproductivity,focusingoncoding
andunittestingactivities,typicallyqua∩ti?edbyimpleme∩tedstoΓypoints.Otherdimensionsmust
alsobeevaluated-likevelocityperdeveloperseniorityandvelocity
percomplexityofuserstories.ThiscompΓehe∩siveappΓoacho阡eΓs
insightsintohowGenerativeAIi∩Hue∩cespΓoductivityacΓoss
di阡eΓe∩tdevelopeΓskilllevelsandtheintricaciesofsoftwaredevelopmenttasks.
Besidesthat,whenitcomesto
testing,themetricforUnitTest
Coverageisessential.Ithelpstoassessthequalityandreliability
ofsoftware.Tokeepitsimplewefocusoninstructioncoverage(C0)asthisismeasuredbymostofthetools.
Ontopofthoseourprotocol
includesmanymoremetricslike
codee代cie∩cy,codesecuΓity,codesmells,codeduplication.
Team:Inthesingleteamapproach,oneteamsequentiallyexecutes
abacklogofuserstoriesof
consistentsizeorcomplexity,
comparingperformancewithandwithoutGenerativeAIassistance.
Ontheotherhand,themultiple
teamsapproachinvolvesparallel
executionofthesamebacklogbyatleasttwoteamswithdi阡eΓe∩t
toolsetups.Forexample,onewithGenerativeAItoolsandtheother
withoutGenerativeAItools.This
allowsforsimultaneousassessmentofGe∩eΓativeAl’se阡ectsacΓoss
di阡eΓe∩tteamdy∩amics.
Theseniorityorcapabilities
ofateamareimportantfor
normalization,andsoits
mandatorytoknowwhatkindofteammixisworkingonthede?∩edbacklo9.Wedisti∩9uishaseniorteamofhighlyskilledindividualsrepresentingthe
9oldsta∩daΓdofpΓo?cie∩cy.
Conversely,thewell-balanced
teamconsistsofagoodmixof
seniorsandjuniors,necessitatingsomelevelofcoachingtypically
facilitatedbyseniormembers
alongsidedailywork.Finally,the
juniorteamfeaturesfewseniors,withtheprimaryfocusoncoachingjuniormembersdevelopment
andproductivity.
Process:Oncealltheingredientshavebee∩de?∩ed,apΓocessisneededtoensurehighquality
Γesultsa∩dΓeducesidee阡ectsduetoestimationinaccuracyandthe
humanfactor.
1.De?∩etheteam(s)oΓ9a∩izatio∩andtheexperimentationscopeandtimeline.
2.De?∩ethemeasuΓeme∩tapproach.
3.Validatetheprerequisitesandthesuccessfactors.
4.Conductabaselineforthemetrics,without
GenerativeAIassistance.
5.Executetheexperimentationsprintswith
GenerativeAIassistance.
6.Collectandnormalizethemetricsandthefeedback.
7.Consolidateandreportthemeasuredresults.
10AugmentedSoftwareTeams:HowtodrivemaximumassistancefromGenerativeAI
Prerequisitesandsuccessfactors
Successfulexperimentation
hingesonmanagingthefollowingprerequisiteseffectively:
?Teamstabilityisvitalfor
achievingcomparableresults.Thisincludesconsistentsize,seniority,andunwavering
processes.
?Baseliningnecessitatesa
minimumofthreesprints,whileGenerativeAIexperimentationsbenefitfrom6-9sprints.Awell-definedbacklogwithdiverse
userstoriesisessentialinputforthesprintsandshouldbeoperatedaccordingly.
?Measurementtoolsmustbe
readilyavailableforanaccurateassessment.Allteammembersneedaccess.Compliancewith
regulations,encompassinglegal,security,andprivacyaspects,
isimperativethroughouttheprocess.
Baseliningiscrucialfor
understandingthecurrentvelocityandqualityofthetraditionalwaysofworkingwithoutGenerative
AItools.It’srecommendedthat
insightsaregatheredfromthe
lastthreesprints,excluding
GenerativeAIassistance.Coding
velocitydatashouldbegatheredfromteambacklogmanagementandcollaborationtools,aswell
ascodequalityreportsfrom
staticcodeanalysistools.Ifdata
fromthelastthreesprintsisn’t
available,threesprintswithout
GenerativeAIassistancemustbeconductedbeforeproceeding
totheGenerativeAIphase.The
baselinemustbecalculatedbasedonthis.Additionally,thedeveloperexperiencesurvey,basedonthe
experiencewithoutGenerativeAI,isconductedduringthe
baselinephase.
Forasuccessfulexperimentation,it’simportanttomaintaina
constantteamsizeandcarefully
curatethebacklogtoincludeusecasesofvaryingcomplexities.Theteammustbeequippedwiththenecessarysetuptoeffectively
utilizetheGenerativeAItool.
Additionally,assigningadedicatedtoolexperttoeachteamcan
significantlyenhancesuccessandefficiency.Monitoringtheteam
involvesensuringtimelytask
updatesonplatformslikeJIRAorAzure,withteamleadsresponsiblefortrackingandensuring
compliance.Inanaugmented
teamapproach,peerreviewsareessential,andtheGenerative
AItoolshouldbeutilizedasan
assistantthroughouttheprocess.
11AugmentedSoftwareTeams:HowtodrivemaximumassistancefromGenerativeAI
Lessonslearned
Likeallorganizations,weare
learningfromGenerativeAIinrealworldandrealsizeconditions.
Belowareourhigh-levellearnings:
ExperimentationFramework:OurexperimentationswithgenerativeAIincustomsoftwareengineeringtypicallyinvolveonetomultiple
teams,spanningaminimum
durationofsixsprints(preferablynine),withadiversebacklogof
userstoriesandcomplexitymix.
Differentteamconfigurations
offervaluableinsights.Typical
configurationsmightinclude,
existingteamstransitioningto
usinggenerativeAI.Newteams
integratinggenerativeAIlater.Andshadowteamsworkingalongsideexistingones.
BaselineEstablishment:Solid
baseliningiscrucialtoensure
robustandrepresentative
comparisonsacrossvarious
metrics.Theseincludevelocity,quality,security,time-to-market,anddeveloperexperience.
Quantifiableresultsfrom
experimentationshowcaseproductivityimprovements.
ToolingPerspective:
Experimentationsinthecodingandunittestingstagesofthesoftwarelifecyclecovermultipletechnologycombinations.Thisincludesvendor-packagedsolutionswithgenerativeAIextensionsandfoundational
LLMsenhancedwithcontextualizedpromptengineeringtechniques.
Pro
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