AnnaPeriniFBK,CenterforInforma3onandCommunica3onTechnology–ICTTrento(Italy)So@wareEngineeringUnith"p://se.)k.eu

TheSUPERSEDEproject

FirstUniversityofKentuckyRequirementsEngineeringWorkshopWednesday,May3rd

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•  AnHORIZON2020project•  Call:H2020-ICT-2014-1(ToolsandmethodsforSo@wareDevelopment)•  Title:SUppor3ngevolu3onandadapta3onofPERsonalizedSo@wareby

Exploi3ngcontextualDataandEnd-userfeedback•  Startedon:May1,2015(Dura3on:36months)ßJuststar5ngY3•  TypeofAc3on:RIA

-  toestablishnewknowledgeand/ortoexplorethefeasibilityofaneworimprovedtechnology,product,process,serviceorsolu3on

-  basicandappliedresearch,technologydevelopmentandintegra3on,tes3ngandvalida3ononasmall-scaleprototypeinalaboratoryorsimulatedenvironment

•  Consor3um:8Partners(4Academic/Research;4Industrial)

WhatisSUPERSEDE?

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•  ProjectOverview•  Mo3va3ons•  ProjectObjec3ve•  UseCases

•  SUPERSEDEatY2•  Glimpseonmainachievements

•  Thewayahead

Outline

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developer

manager

Mo3va3onsExamplescenario:SEnerConESAservice

Thereissomethingwrong

withthecalcula3onof

Aprilconsump3on…

Howcangetthisissuesolved….I’llhavetophone/emaillater...

WhenwillIdoit?Hopetonotforget

L

3projectsunderdeadline…tensof

newrequirementstoimplement,what

shallIconsiderfirst?

Whicharethefeaturesthatwillmakeourusers

happy?Whichoneswillaeractmore

users?

user

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SUPERSEDEVision:

Ø  End-user•  enablingthemtoexpress

feedbackeasily•  makingusersanddevelopers

closer•  providingbe"erso@ware

applica3ons-  context-awareness,

personaliza3on(improveQoE)

Ø  So@wareengineer/providers•  supportthemtotakebeeer

decisionsinevolvingso@wareapplica3onandservicesandtoenactthem

•  supportthemtointegrateandextendtheirserviceswithmechanismsforamoresituateddynamicadapta3on

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Objec3ve:Supportavfeedback-drivenengineeringprocess

•  Obj-A:methodsandtoolstocollectend-users’feedbackandrun5medatawhichwillbeefficient,scalableandadaptable

•  Obj-B:methodsandtoolstoperformanintegratedanalysisofthecollecteddatainordertoestablishasoundbasisforevolu3onandadapta3ondecision-making

•  Obj-C:methodsandtoolstosupportdecision-makingintheevolu5onandrun5meadapta5onofservicesandapplica5onsbasedonuser’sfeedbackandcontextualdata

•  Obj-D:methodsandtoolstoenactthedecisionsmadeandmeanstoassesstheimpactofthesedecisionsbothintermsofQoEandorganiza3onproduc3vity

•  Obj-E:validatedataandfeedback-drivenso@wareevolu3onandadapta3onfortheimprovementofso@warequali3esalongdifferentindustrialusecases

Obj-C

Obj-DObj-A

Obj-B

Obj-E

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Approach:Knowledgeareas/challenges

•  CH1-FeedbackGathering:Mul3modalfeedbackcommunica3onchannels

•  CH2-Run-?memonitoring:Comprehensivemonitoringsolu3on

•  CH3-Bigdataanalysis:Integra3onofheterogeneoussources

•  CH4-User-FeedbackAnalysis:Combiningsen3mentanalysisandconversa3onanalysisapproaches

•  CH5-SoKwareQuality:FrameworkthatintegratesQoEandQoS

•  CH6-Decision-makingsupport:Automatedandmix-ini3a3vedecision-makingtechniques

•  CH7-Run-?meAdapta?on& Personalisa?on:Scalablesolu3ons–customizabletospecificdomainseings

CH6

CH7CH1CH2

CH5

CH3 CH4

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Approach:3UseCaseswillensure

Demo Apps CityInfoAPI:Management

andOpera5on

SMART CITY INFORMATION PLATFORM

• SmartCityInforma3onAPIprovisionandconsump3on

• EcosystemforSmartCityInforma3onexchange

ESA service Energy Savings Account

• Usercanchecktheirhouseenergyconsump3on

• Calculatecostsandsavingsforenergyefficientac3ons

SMART PLAYER Sport Media Application in Real Time

• Webscas3ngMediaplakormforlargesportevents

• Allowspeopletowatchsportvideosondemand

• Givestatswith:liveresultsandsportinfo

• Applica3oninRealTime

•  theelicita3onofrelevantdomainknowledge

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•  M12 validation: •  Scope: individual components (product parts) •  TRL3: Experimental proof-of concepts validated on realistic scenarios

•  M24 validation •  Scope: first toolset integrating the methods developed in the project •  TRL5: Technology validated in relevant environment (scenarios for software evolution

and dynamic adaptation) •  M36 validation

•  Scope: toolset integrating all the methods developed in the project •  TRL6: Technology demonstrated in relevant environment

Approach:3UseCases

First validation

Second validation

M0 M12 M24 M36

Final validation

TRL=3 TRL=5 TRL=6

•  aprogressivevalida3onofthemethodsandtoolsproducedtoul3matelyprovideevidenceofpoten3alforproduc3vitygains

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ü  ProjectOverview•  Mo3va3ons•  ProjectObjec3ve•  UseCases

•  SUPERSEDEatY2•  Mainachievements

•  Thewayahead

Outline

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SUPERSEDE at Y2 Mainachievements

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FeedbackGathering&Monitoring

•  Unifiedarchitecturerealized-  explicit and implicit feedback

•  Provisionofmul3modalfeedbackgatheringmechanisms

•  Firstheterogeneousanddistributedmonitorsareimplementedandrunning

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SUPERSEDE at Y2 Mainachievements

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•  BigDataarchitecturerunningonUPCcloud(1)•  AUC-agnos3cintegra3on-orientedontologytomodel

ingesteddatastreams•  Combinedspeech-actbasedanalysisandsen3ment

analysisforderivingrequirementsoutofuserfeedback(2)

•  Analysisoftweets(3)

Integratedanalysisofdata

REFERENCES•  (1)Abelloetal.IST(underrevision)ASoKwareReferenceArchitectureforSeman?c-AwareBigDataSystems•  (2)Moralez-Ramirez,Kifetew,Perini,CAISE17AnalysisofOnlineDiscussionsinSupportofRequirementsDiscovery•  (3)Guzman,Alkadhi,Seyff.ANeedleinaHaystack:WhatDoTwi"erUsersSayaboutSoKware?RE16

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BigDataarchitecture

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•  Results: -  Found association between types of speech acts (e.g. Informative, Responsive, Requestive,

etc.) and type of issues (e.g. Enhancement, Defect) -  Distribution of speech acts for the first ten comments for Defect and Enhancement, and

identified common patterns

Speech-act&sen3mentanalysisTextualonlinediscussion

•  Techniques: -  NLP techniques for pre-processing -  speech acts and the sentiment as parameters

for training three machine learning algorithms (Random Forest, J48 and SMO) classify comments into Enhancement, Feature and Defect

REFERENCES•  Moralez-Ramirez,Kifetew,Perini,CAISE17AnalysisofOnlineDiscussionsinSupportofRequirementsDiscovery

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SUPERSEDE at Y2 Mainachievements

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DecisionMaking1)forso@wareevolu3on

DecidethesetofRequirements Nego3ate

onconflicts

DecidePriorityofrequirements

Decisionmakingprocessorchestra5on

Userfeedback

Contextmonitoring

Distributeddecisionmakingteams

Priori5sedRequirements

Thetechniques

REFERENCESP.Buseea,F.Kifetew,D.Munante,A.Perini,A.Siena,andA.Susi.ToolsupportedCollabora3veRequirementsPriori3sa3on.InProceedingsoftheIEEEInt.ConferenceCOMPSAC,Torino,Italy,July4-7,2017Thetoolisaccessibleat:heps://github.com/supersede-project/dm_game

•  Automatedreasoning.Currentlyincluded:•  Analy5cHierarchyProcess(AHP)methodthatwasselectedbecauseitspairwise

comparisonmechanismallowsustoperformafine-grainedanalysisofthemo3va3onsthatleadtoaresul3ngranking,thusexploi3ngatbestthedifferentskillsandexper3seofthedecisionmakers

•  Gene5cAlgorithms(GA)becauseitallowstoovercomesomeofthelimita3onsofAHP,atthecostofareducedgranularityoftheranking

•  Gamifica3on•  Tofosteruserengagement,gameelementsareincluded,suchas:

-  Progress,thatisusercomple3onrateisreportedtoeachuser-  TimePressure,thatistheprocesshasafixeddura3on,andac3onsdonea@ertheprocess

expira3onarediscarder;-  Pointsifica1on,thatisapointaeribu3onmechanismhasbeendesigned,withthepurposeof

providinganincen3veto(i)performthevo3ngtaskquickly,and(ii)performanaccurate

Decision making 2) for dynamic adaptation in the (MAPE-K process)

DMop3mizer

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●

●

●

0.05 0.10 0.15 0.20 0.25

0.4

0.5

0.6

0.7

Optimal Configurations

Cost (Memory consumption)

Valu

e (A

vaila

bilit

y)

●c1

c4

c3

c2

c5

c6

c7

MonitoringData

Mul3-objec3vealgorithmstoop3misethe

configura3onswithrespecttothecurrentparametersvalueandconstraints

Currentconfigura3on

Anexample

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SUPERSEDE at Y2 Mainachievements

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ReleasePlanningTheReplantool

REFERENCES•  DavidAmeller,CarlesFarré,XavierFranch,AntoninoCassarino,DaniloValerio,Valen3n

Elvassore,Replan:aReleasePlanningTool,SANER2017•  DavidAmeller,CarlesFarré,XavierFranch,AntoninoCassarino,DaniloValerio,Towards

Con?nuousSoKwareReleasePlanning,SANER2017

Dashboard:Providesausablehumaninterface

Controller:Collectsallthenecessaryinforma3ontogenerateareleaseplan:features,andresourcesandsendsittotheReplanOp3mizerThereleaseplanthatthislaeerreturnsisthenstoredandsentbacktotheReplanDashboard

ReplanOp3mizer:Gene3cAlgorithms:jMetal4,aframeworkwithanextensiveporkolioofavailablemul3-objec3veop3miza3onalgorithms:NSGA-II,MOCell,PESA-II,SPEA-II

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SUPERSEDE at Y2 Chainofmethods&toolshavebeenvalidated

•  User-feedback(tweets)+Decision-Making+ReleaseplanninghasbeenvalidatedinATOSUCs

•  Eventsfrommonitoreddataweresimulatedtovalidatedynamicadapta3oninATOSUC(bothdecision-makingandenac3ng)

•  …

Tutorial •  Release Planning (ICSE’16), Xavier Franch and

Workshop: •  PRIORE17@REFSQ •  RE17 CrowdRE17

Int Journal Papers: •  JOA 15 •  IEEE software 17 •  REJ, IST submissions

Int. Conferences papers •  PROFES16 •  RE16 •  SANER17 •  CAiSE17 •  COMPSAC17 •  …

Int. Workshop papers •  PRIORE17 •  iStar17

… Industrial Workshop in Berlin on September 2017

SUPERSEDE at Y2 Dissemina3on

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What’s next?

•  Project perspective: -  M30 (October 17) final release of the

tool suite to be validated in realistic setting

-  M36 (April 18) final results for all the components

-  Validation of the tool-suite in realistic settings

•  Struggling to attract external evaluators, please contact me if you are interested to use SUPERSEDE components

•  Integrating components into issue tracking systems

•  Research advancement in data-driven RE -  Further research results in addressing

CH1---CH7

Thank you for your attention Questions?

Figu

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Cre

dits

: Cha

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desi

gned

by

MD

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Mile

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es a

nd ro

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res:

Credits:someoftheslideshavebeenadaptedfrompresenta3onsofNorbertSeyff(FHNW,UZH),AlbertoAbello(UPC),AngeloSusi(FBK),DavidAmeller(UPC))

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SUPERSEDE at Y2 Toolsuitearchitecture

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Chapter 5. Speech-Act based Analysis of User Feedback

from which Assertives, Confirmatives and Concessives are aggregated into the analysis categoryAssertives. The speech act Assertives is considered as a strong belief and intention by a senderwho maintains his/her belief about something.

Table 5.1: Categories of speech acts

Category Subcategory Analysiscategory

Definition (excerpt)1

Constantives

Informatives Not usedAssertives: speech act that is con-sidered as having a strong be-lief and intention by a senderwho maintains his/her belief aboutsomething, e.g., “I know the choco-late is good for your health...”.Suppositive: speech act conveyingthat is worth considering the conse-quences of something regardless ofwhether it is true, e.g. “I supposethe configuration file ...” Reques-tive: speech act expressing sender’sintention that the receiver take theexpressed desire as reason to act,e.g., “I kindly ask you to provideme ...”

AssertivesAssertivesConfirmatives

ConcessivesSuggestives

ResponsivesSuppositivesResponsives

DirectivesRequestives

RequestivesQuestionsRequirements

Expressives

Thank Not usedAccept AcceptReject RejectNegative opinion Negative

opinionPositive opinion Positive opinion

Attach (non-linguistic)URL link

AttachCode lineLog file

5.1.2 Automated Tagging of Speech Acts

The procedure followed to build our method includes a gathering of seed words and the com-putation of their frequencies. This was done in order to have evidence of a presence of perfor-mative verbs in messages of OSS community discussions. The resulting seed words composesets of words that are used later to elaborate rules for tagging speech-acts. In the following weexplain such steps.

Selection of seed words for the OSS context. To find out what are the words reifying the intentionswe want to identify, we examined crawled messages from Apache OpenOffice bugzilla2 to findregularities in written messages. We analysed empirical data from other OSS project to notbe biased in defining the kind of words regularly used by participants in online discussions.Following a supervised selection of seed words, we executed the process depicted in Figure 5.2,from step (a) to (f):

1Definitions taken and adapted from the work by Bach et al. [BH79]2https://issues.apache.org/ooo/

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Speech-act categories

Theobjec3ve(forSE)

•  Supportdecision-makersinusingexplicit(1)andimplicit(2)feedbackfromtheuserstoevolvethesystem

–  (1)Getfeedbackfromyourstakeholderscon3nuously

–  (2)Monitortheircontextandtheopera3onalcontextofthesystemtheyareusing

–  Analysethecombineddatatoiden3fynewstakeholders’requirementsorrequestsforsystemadapta3on/reconfigura3on

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The SIEMENS use case SMART CITY INFORMATION API PLATFORM

Demo Apps

CityInfoAPI:Managementand

Opera5on

•  SmartCityInforma3onAPIprovisionandconsump3on

•  EcosystemforSmartCityInforma3onexchange

•  AdvancedSmartCityAppsandServicesforenergyproviders,gridoperatorsandci3zens

•  Run3memonitoringofplakorm•  APIaccesspaeernsandKPIs

WithSUPERSEDE•  Collectandanalysedeveloperandpublisher

feedback•  Suppor3ngplakormevolu3on•  Improvingmaintenanceplanning•  PerformingSLA-drivenserviceadapta3on

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Interactive Energy Savings Account

The SEnerCon use case

•  SENERCON(so@waredeveloper,management)HomeEnergyEfficiency-Energyevalua3onapplica3on(heps://www.energiesparkonto.de)

WithSUPERSEDE•  Setupaneffec3vecommunica3on

channelbetweenend-usersanddeveloperstoimprovetheso@waredevelopmentbasedontheend-users

needs•  Helpdeveloperstofindproblemsinthe

so@warefasterandgivethemthepossibilitytodecidewhicharethemostimportantfeatures/bugstoworkon

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SMART PLAYER Sport Media Application in Real Time

The ATOS use case

•  Webscas3ngMediaplakormforlargesportevents

•  SportsEventLive•  Allowspeopletowatchsportvideos

ondemand•  Applica3oninRealTime•  Givestatswith:liveresultsandsport

info•  Mul3-audioindifferentlanguages

WithSUPERSEDE•  Win-winforend-usersandcontent

providers•  Dynamicconfigura3onontheproduc3on

side•  Collectandanalyseuserfeedbackinreal-

3me•  ATOS’businessservicescomposi3ontool•  Improveend-userqualityofExperience

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Meta data & ontology (1)

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Meta data & ontology (2)

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Examples of queries

Decision Making for adaptation

•  Thanks to the analysis of monitoring data, that allows to trigger a change in the configuration of the system

•  Search for new (possibly alternative) configurations given feature model, current configuration, monitoring data, constraint from the trigger

AnexampleofAdapta3oncondi3on

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Release Planning & Enacting Adaptation

Research topics •  Models@runtime - representations of runtime behavior

•  Variability expressed as feature models •  AOM on UML for system modeling (e.g., the target use case) •  Adaptability managed by our own DSL

•  Generic (highly reusable) top-down MDE approach •  M2M/M2T transformations for the models@runtime •  Platform specific adaptation hooks

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Results obtained: Integration Framework Technical Design

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Results obtained: Front-end Interface Technical Design

FRONT-END

Micro-Service

WP module

produce

call

read/write read/write

dispatch

read/write