Projects often fail because they overlook stakeholders. Unfortunately, existing stakeholder analysis tools only capture stakeholders' information, relying on experts to manually identify them. StakeSource is a web-based tool that automates stakeholder analysis. It "crowdsources" the stakeholders themselves for recommendations about other stakeholders and aggregates their answers using social network analysis

Finkelstein, A.

Lim, S.L.

Quercia, D.

English

UCL Discovery

StakeSource: Harnessing the Power of Crowdsourcing andSocial Networks in Stakeholder AnalysisSoo Ling LimDept. of Computer ScienceUniversity College LondonUnited Kingdoms.lim@cs.ucl.ac.ukDaniele QuerciaMIT SENSEable City LabCambridgeUSAquercia@mit.eduAnthony FinkelsteinDept. of Computer ScienceUniversity College LondonUnited Kingdoma.ﬁnkelstein@cs.ucl.ac.ukABSTRACTProjects often fail because they overlook stakeholders. Un-fortunately, existing stakeholder analysis tools only capturestakeholders' information, relying on experts to manuallyidentify them. StakeSource is a web-based tool that au-tomates stakeholder analysis. It \crowdsources" the stake-holders themselves for recommendations about other stake-holders and aggregates their answers using social networkanalysis.Categories and Subject DescriptorsD.2.8 [Software Engineering]: Requirements/SpecicationsGeneral Termssocial network analysis, algorithmsKeywordsstakeholder analysis, social networks, recommender systems1. INTRODUCTIONStakeholder analysis is a critical step in software engi-neering. Omitting stakeholders is one of the most commonmistakes in software development [2] and the main causefor project failure [6]. As stakeholders are the source of re-quirements [3], an incomplete list of stakeholders gives riseto missing requirements, which in turn leads to the wrongproduct being built and a failed project. As such, properstakeholder analysis { the process of identifying and priori-tising stakeholders based on their inuence in a project { iscrucial to project success.In our research reported in this ICSE [4], we have de-veloped StakeNet, an approach for stakeholder analysis. InStakeNet, the experts identify stakeholders by asking themto recommend other stakeholders, build a social network ofAlso with the University of New South Wales and NICTA.Citation: S. L. Lim, D. Quercia, and A. Finkelstein. StakeSource: Harness-ingthepower ofcrowdsourcingandsocial networks instakeholderanalysis.In Proceedings of the IEEE 32nd International Conference on Software En-gineering, 2010 (in press).stakeholders from their recommendations, and prioritise thestakeholders using various social network measures. We haveapplied StakeNet to a large-scale software project { the Re-placement Access, Library and ID Card project (RALIC) inUniversity College London (UCL). Results show that Stak-eNet identies a complete list of stakeholders, and priori-tises the stakeholders accurately according to their inuencein the project. Nevertheless, StakeNet requires experts toapproach stakeholders individually to ask for recommenda-tions. As such, it is costly for large projects with manystakeholders. An improvement would be a tool to supportthe process.Existing tools provide little support in the actual identi-cation and prioritisation of stakeholders; they merely holdand process the data provided by the experts in charge ofstakeholder analysis. For example: Stakeholder Analysis Matrix1. The experts makea list of stakeholders and plot them against two vari-ables on a matrix, such as power and interest, or im-portance and inuence. Stakeholder Circle2. The experts enter stakeholderinformation such as name, role, signicance, commentsabout the stakeholder, and whether the stakeholder isactive. Based on the information, the tool generatesgraphs and reports. Stakeholder Checklists (e.g., the Onion Model [1]and the Volere Stakeholder Analysis Template3). Thechecklists contain a set of generic stakeholder roles(e.g., user and regulator). By referring to these roles,the experts derive project specic stakeholder roles(e.g., students and data protection ocer).Perhaps, in smaller projects, these tools may suce. Nev-ertheless, in large-scale projects where no individual has theglobal perspective, the experts using these tools are boundto omit stakeholders. For example, RALIC used the check-list tool to identify stakeholders. Lead requirements engi-neer, Mary4, was unaware that external library users wouldbe aected, hence failed to identify them despite using thetool. As a result, UCL libraries remained with the old access1http://www.mindtools.com/pages/article/newPPM 07.htm2http://www.stakeholder-management.com/3http://www.volere.co.uk/templates.htm4The names in this paper have been changed for reasons ofprivacy.control system. Mary is not to blame. In such a large projectwith more than 60 stakeholder groups, the odds were low forher to know about the external library users, unless Janet,the library systems manager, told her about Matthew, thememberships ocer, who then revealed that external libraryusers would be aected. An important question then arises:how do we make that happen?The answer comes from a new approach that enables us toexploit the knowledge contained in diverse communities ofpeople [7]. Crowdsourcing { or in this case, stakeholder-sourcing. Instead of having the experts approach stake-holders for recommendations, the tool should automaticallysource the stakeholders for help. Ideally, by using the tool,all the experts need to do is start o with an initial incom-plete set of stakeholders, and the tool should automaticallyreturn a complete and prioritised list of stakeholders. Thisis what we set out to achieve with StakeSource described inthe next section.2. STAKESOURCEStakeSource relieves the experts from the burden of stake-holder analysis by providing four features.Feature 1: Identify StakeholdersStakeSource returns a complete set of stakehold-ers based on initial stakeholders that the expertsprovide.Example. StakeSource asks Mary to list down the stake-holders she knows about in the format <name, role, emailaddress>. For example, Mary makes an entry <Janet, li-brary systems manager, j.lib@ucl.ac.uk>. Based on the listof stakeholders that Mary enters, StakeSource returns a listof stakeholders and their roles, including external libraryusers { the stakeholder Mary overlooked.How StakeSource does it. StakeSource identies stake-holders by asking stakeholders to recommend other stake-holders. To use the tool, the experts start by creating theproject and entering the project details such as project de-scription and scope items. StakeSource uses these details toinform stakeholders about the project when it asks for theirrecommendations.StakeSource prompts the experts for initial stakeholderroles by asking them to list down the user, developer, legis-lator, and decision-maker roles in the project, using a wiz-ard. For each role, the experts tell StakeSource about theinitial stakeholders by entering their names, roles, and emailaddresses. StakeSource sends an email to each initial stake-holder, asking him or her to recommend other stakeholders.The email contains a link that will bring the stakeholderto a recommendation form (Figure 1) with hints about howto recommend. Each stakeholder recommendation is in theformat <name, role, salience (the level of inuence a stake-holder has on the project), email address>. If a stakeholderis aware of a role but is not aware of the stakeholders withthat role, he is allowed to recommend only the role. In thatcase, StakeSource associates that recommendation to all thestakeholders with the same role who are identied so far.Each time a new stakeholder is recommended, StakeSourcesends an email to the new stakeholder requesting recommen-dations about other stakeholders. This procedure is calledthe snowballing technique [5], whereby the group of stake-holders identied by StakeSource builds up like a snowballFigure 1: Recommendation form.rolled down a hill. Eventually, few additional stakeholderroles are identied in each round of requests. When no ad-ditional roles are identied in one round of requests, theexperts stop the process, and StakeSource returns a list ofidentied stakeholders and their roles.Feature 2: Prioritise StakeholdersStakeSource prioritises stakeholder roles and stake-holders by their characteristics using social net-work measures [4] such as betweenness centrality,closeness centrality, and PageRank.Example. Mary wants to mediate between stakeholderswith conicting requirements. She chooses the betweennesscentrality measure (Figure 2 Panel A) to prioritise brokersbetween disparate clusters of stakeholders. Among the bro-kers, StakeSource identies \Estates" as a good broker, andwithin Estates, Martin is a better broker than Richard.How StakeSource does it. StakeSource aggregateseach stakeholder's private judgements about a stakeholder'sinuence in the project into a collective decision. Using thestakeholders' recommendations, StakeSource draws a socialnetwork with the stakeholders as nodes, and their recom-mendations as directed edges: S1 links to S2 if S1 believesS2 to be a stakeholder. StakeSource provides two levels ofsocial network: scope item and project. At the scope itemlevel, the salience in each recommendation determines theweight of the link. At the project level, StakeSource com-bines scope-level recommendations such that if S1 recom-mends S2 in N number of scope items, N determines theweight of the link.StakeSource prioritises the dierent stakeholder charac-teristics for a stakeholder S using dierent social networkmeasures [5] as follows. Betweenness centrality ranks S by summing the num-ber of shortest paths between other pairs of stakehold-ers that pass through S.Figure 2: The three panels (A, B, and C) of StakeSource's user interface. Load centrality ranks S by the total amount of recom-mendations passing through S from adjacent stake-holders. Closeness centrality ranks S based on S's distance toall other reachable stakeholders from S. PageRank ranks S in terms of S's relative importanceto all other stakeholders. The rank is dened recur-sively and depends on the number and rank of all thestakeholders that recommend S. Degree centrality ranks S based on the number of di-rect connections S has, both to and from other stake-holders. Out-degree centrality ranks S based on the number ofrecommendations that S makes and the recommenda-tions' weights. In-degree centrality ranks S based on the number ofrecommendations that S receives and the recommen-dations' weights.For each measure, StakeSource produces a prioritised listof stakeholder roles, and for each role a prioritised list ofstakeholders. To improve the quality of prioritisation, theexperts can merge similar recommended roles (e.g., PhD stu-dent and research student), and dierent names referring tothe same person.Feature 3: Identify Potential ProblemsStakeSource highlights stakeholders who may haveproblems with their involvement or communica-tion.Example. StakeSource tells Mary that security headNicholas may have involvement problems (e.g., lack of timeor interest in the project) and more eort is required to keephim engaged. Mary adjusts the sensitivity slider in Figure 2(Panel B): the more sensitive the tuning, the more stake-holders with potential problems are returned. This helpsMary explore stakeholders with potential problems in thevarious states of sensitivity.How StakeSource does it. For potential involvementproblems, StakeSource nds stakeholders who rank high indegree centrality but low in betweenness centrality. For po-tential communication problems, StakeSource nds stake-holders who rank high in betweenness centrality but low incloseness centrality. The slider determines the allowable dif-ferences between the rank. When the slider is in a moresensitive mode, the allowable dierence is less.Feature 4: Display Stakeholder InformationFor each stakeholder, StakeSource displays thefollowing information: name, role, photo, the scopeitems they are recommended for, and commentsfrom other stakeholders. It also visualises thestakeholder's position on the network, who theyrecommend, and their rank as dierent kinds ofstakeholders.Example. Using the search function, Mary nds Richard'snode and the network diagram in Figure 2 (Panel C) high-lights his node. Mary clicks on the node to pop up Richard'sprole and nd out what he looks like, his role in the dif-ferent scope items, unavailability on Wednesdays, and rankas a dierent kind of stakeholder. Hovering over Richards'snode with the mouse pointer reveals his recommendations.The prioritisation panel shows that Richard is a more suit-able stakeholder than John for the \Estates" role. From thenetwork visualisation, Mary is able to see that Richard sitsbetween two clusters, suggesting that he is a good broker.How StakeSource does it. When a stakeholder rec-ommends someone, he can also add public and private notesabout the person. The network diagram displays each stake-holder as a node, with his outgoing and incoming recommen-dations, and a rank number for each stakeholder character-istic. Clicking on the node highlights the stakeholder's rowin the prioritisation panel and displays the stakeholder's de-tails.3. IMPLEMENTATIONFor StakeSource's implementation, we have the follow-ing key requirements, which are informed by our recent re-search [4]. Widely available and easy to access. StakeSourceworks when a sucient number of stakeholders con-tribute with their recommendations. Simple and intuitive to use. The ease of recom-mendation is vital to encourage stakeholders' contri-bution. Easy to make recommendations without com-promising quality. Closed-ended recommendationswhere stakeholders are provided with an existing listof stakeholder roles are easier to complete but returna less complete set of roles. Interactive stakeholder analysis UI. The expertsshould be able to interact with the UI to learn aboutthe stakeholders (e.g. by clicking on a stakeholder'snode in the network, the prioritised list should high-light the stakeholder's row).Based on the key requirements, we have made the follow-ing design decisions for StakeSource. Web-based. To make the tool widely accessible, wehave implemented it as a web application using stan-dard web technologies such as HTML, CSS, XHTML,PHP, and JavaScript. We have selected MySQL fordata storage. Standard interface with simple terms. We haveimplemented the recommendation forms (Figure 1) us-ing standard survey interface from the Smarty Tem-plate Engine5. On top of that, we have used widelyknown terms whenever possible. For example, we havechosen the term \level of inuence" over \salience" be-cause beta testing revealed that \level of inuence" iseasier for stakeholders to understand. Finally, we havesupplied tool tips and pop-up help for dicult termi-nology. Future work involves translating the technicalterms for the measures in the stakeholder prioritisationfeature to correspond to specic end-user goals. Autocomplete. For the stakeholder role entry, wehave opted for autocomplete using jQuery rather thana drop-down list.5http://www.smarty.net/ Existing components. We have implemented thestakeholder analysis user interface (Figure 2) in Flex,and reused two well-established software components.(a) Kap Visualizer6. We use the Flex Visualizer forits customisable and interactive network visuali-sation.(b) NetworkX7. We use the Python package for itsimplementation of the social network measures.StakeSource is available from our project website8.4. CONCLUSIONSThe lead requirements engineer, Mary, omitted a majorstakeholder, causing the libraries in University College Lon-don to remain with the old access control system. HadMary used StakeSource for RALIC, the project would haveachieved its objective of a unied access control system acrosscampus.StakeSource is a simple but surprisingly powerful and use-ful tool. It proposes a shift from current practices where ateam of experts conduct stakeholder analysis, to a crowd-sourcing approach that involves all stakeholders. In doingso, it reduces the experts' workload and lowers the likeli-hood of overlooking stakeholders. The tool has now beenused in the UCL Admissions System Project9, a large-scalesoftware project involving more than 70 stakeholders. Weplan to use the tool for projects in other elds, such as en-vironment policy planning.5. ACKNOWLEDGMENTSWe thank Jason Lau for his contribution in the softwaredevelopment, Abdigani Diriye and Enzian Baur for their ad-vice in the user interface, and Peter Bentley for his feedbackon the paper.6. REFERENCES[1] I. Alexander and S. Robertson. Understanding projectsociology by modeling stakeholders. IEEE Software,21(1):23{27, 2004.[2] D. C. Gause and G. M. Weinberg. ExploringRequirements: Quality Before Design. Dorset HousePublishing, 1989.[3] M. Glinz and R. J. Wieringa. Stakeholders inrequirements engineering. IEEE Software, 24(2):18{20,2007.[4] S. L. Lim, D. Quercia, and A. Finkelstein. StakeNet:Using social networks to analyse the stakeholders oflarge-scale software projects. In Procs. of the 32nd Int.Conf. on Soft. Eng. (to appear), 2010.[5] J. Scott. Social Network Analysis: A Handbook. Sage,2000.[6] Standish Group. The CHAOS Report, 1994.[7] J. Surowiecki. The Wisdom of Crowds. Anchor, 2005.6http://lab.kapit.fr/display/kaplabhome/Home7http://networkx.lanl.gov8http://www.cs.ucl.ac.uk/research/StakeSource/9http://www.ucl.ac.uk/isd/community/projects/azlist-projects

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StakeSource: harnessing the power of crowdsourcing and social networks in stakeholder analysis

StakeSource: harnessing the power of crowdsourcing and social networks in stakeholder analysis

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