Proceedings of the Eighth International i* Workshop (istar 2015), CEUR Vol-978
iStar in Practice: On the identification of reusable SD
Context Models Elements
Karina Abad, Juan Pablo Carvallo, Catalina Peña
Computer Science Department
University of Cuenca, Ecuador
{pablo.carvallo;karina.abadr;catalina.pena@ucuenca.edu.ec
Abstract. Modern enterprises rely on Information Systems (IS) required both to
support their operation and provide information required to endorse strategic de-
cisions. Because of their increasing complexity, such systems are usually con-
structed by integrating software components of different nature and origins into
hybrid systems, for which architectural design plays a fundamental role. How-
ever, far from simple, this task is usually cumbersome. In previous work we have
addressed this issue and proposed a four steps, pattern-based approach, aimed to
help in the solution of this problem. In first steps, patterns are described as Con-
text Models, which include recurring elements (actors and dependencies) identi-
fied in several industrial cases. In this work we further address this issue and
present an study aimed at the validation and extension of such patterns, and/or
the identification of new ones, by reviewing recurring elements appearing in 29
semi-industrial IS architectural design processes.
1 Introduction
Modern enterprises rely on Information Systems (IS) specifically designed to manage
the increasing interactions with their context. Enterprise Architecture (EA) [1], is a new
approach involving several levels of architectural design, including IS architecture,
which requires deep understanding of enterprise context and strategies. Enterprise Con-
text Models (CM) are usually built to support this process, assisting enterprise decision-
makers to design and refine their business strategies and enterprise architects to under-
stand what will be required from IS. Far from easy, the construction of such models is
usually a cumbersome task, mainly due to communication gaps among technical per-
sonnel with limited knowledge of enterprise structure, operations and strategy, and their
administrative counterparts imposing pressure and time constraints to the process.1
In order to deal with these problems, in the last few years we have intensively used
the i* notation to bridge the gap among technical consultants and non-technical stake-
holders [2] and proposed the DHARMA method [3], for discovering IS architecture
departing from the construction of CM expressed in i*. The application of the first ac-
tivities of this method in several industrial and academic cases, allowed us to identify
a catalogue of patterns [4], which could be used as templates for both technical and
Copyright © 2015 for this paper by its authors. Copying permitted for private and academic purposes.
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Proceedings of the Eighth International i* Workshop (istar 2015), CEUR Vol-978
managerial personnel in order to improve their understanding. Patterns store knowledge
represented by i* Strategic Dependency models, including generic environmental ac-
tors and their strategic dependencies. The catalogue distinguish two levels of abstrac-
tion, the higher applicable in general to any kind of enterprise and the lower which
considers enterprise strategies describing how a particular enterprise operates.
Although very valuable in practice, we thought that the catalogue could be extended,
with additional levels representing knowledge of more specific enterprise domains. In
this paper we present initial findings in relation to this belief, which emerged after con-
ducting several semi-industrial cases of applications of the DHARMA method.
2 The Case Studies
In the last three years we have conducted 29 semi-industrial cases of application of the
DHARMA method (industrial cases conducted by senior Information Systems Engi-
neering students with support of teachers, for which formal agreements existed, but
were conducted with no cost for participant enterprises). Cases were part of a broader
study conducted in Ecuadorian enterprises, intended to identify CMs patterns meant to
improve the identification of IS architectures (System Actors -atomic software domains
that structure the system-, services that must be covered by them and their relation-
ships). CMs constructed for these processes were used to validate and extend the pat-
terns presented in [4] (by measuring occurrence of the included elements), and to iden-
tify new domain specific ones.
In the study, 25 of the enterprises were small companies, 3 medium size, and the last
one a large manufacturing company. This distribution aligns with the Ecuadorian real-
ity, mainly structured with small companies (97,94%) [6]. Enterprises were categorized
according to NACE Rev 2. Categories included: Manufacturing (wood, textiles, food
and cardboard processing); Wholesale and retail trade (hardware and software, textiles,
leather, home appliances, motorized vehicles and general goods); and Services (basic,
specialized –language- and advanced education, and financial – accounting-)
3 Data Analysis
Actors and dependencies included in the resulting 29 CMs were extracted and placed
in tables specifically designed to support the analysis process. Columns represent mod-
elled enterprises whilst rows list the identified actors (table 2) and their corresponding
dependencies (table 3). Actors identified in the 29 cases were grouped in relation to 8
of the generic actors identified in [4], Suppliers, Consumers, Strategic Partners, Dis-
tributors, Financial Institutions, Regulatory Agencies, Control Agencies, Competitors.
Table cells are used to state the cases in which listed actors/dependency were identified.
Total column adds up the number of occurrences of elements in each row, whilst per-
centage gives the relation among the totals and the number of case studies.
At the end, a total of 54 actors and 189 dependencies were identified in the 29 cases.
All of the actors are instances of the generic actors identified in [4], which makes evi-
dent the validity of knowledge included in the proposed patterns in relation the this kind
of elements. 23 out of 54 actors identified appear in at least 17% of the cases; 14 of
them in at least 24% of the cases.
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Proceedings of the Eighth International i* Workshop (istar 2015), CEUR Vol-978
Table 1. The case studies
Size Dependencies
Actors
Medium
Resour
Tasks
Goals
Small
Large
Total
Enterprise Industry
Soft
C - 10.71 Manufacture of bread; manufacture of fresh
Panadería Centenario X
1 pastrygoods and cakes 11 8 10 6 0 24
2 Sport Chavis X C - 14. 13 Manufacture of other outerwear 12 10 11 13 1 35
C - 16.29 Manufacture of other products of wood; manufacture
FABRICA X of articles of cork, straw
3 S - 96.03 Funeral and related activities 9603 11 6 12 7 0 25
4 CARTOPEL X C - 17.1 Manufacture of pulp, paper and paperboard 16 10 14 0 0 24
5 Forjart X C - 24 Manufacture of basic metals 13 7 10 1 3 21
6 ElectroUnion X C - 27.5 Manufacture of domestic appliances 20 11 13 12 0 36
7 Muebleria BienStar X 13 8 7 1 1 17
8 FEMUSA Mobiliarios X C - 31.0 Manufacture of furniture 12 4 4 5 1 14
9 SANTANA Muebles X 9 11 15 6 2 34
G - 45.1 Sale of motor vehicles,
Importadora Tomebamba X
10 G - 46.43 Wholesale of electrical household appliances 14 6 11 8 0 25
G - 45.1 Sale of motor vehicles,
JCEV Cia. Ltda. X
11 G - 46.43 Wholesale of electrical household appliances 10 10 8 13 1 32
12 TECNISUR X G - 45.2 Maintenance and repair of motor vehicles 8 7 7 5 3 22
13 Trebol Roses X G - 46.22 Wholesale of flowers and plants 15 11 12 8 2 33
14 CAPEDI X G - 47.1 Retail sale in non-specialised stores 14 9 13 8 0 30
15 All Design X 8 8 10 6 2 26
16 Giga Computers X 17 14 11 8 2 35
G - 47.41 Retail sale of computers, peripheral units and
17 APC Tecnología X 4 5 10 9 0 24
software in specialised stores
18 HOLIDATSERV X 8 7 8 7 1 23
19 TOTAL COMPU X 9 12 10 9 1 32
20 Dress Up Store X 9 10 9 6 3 28
G - 47.51 Retail sale of textiles in specialised stores 4751
21 KRISTEN X 12 10 15 12 1 38
22 Sodilibro X G - 47.61 Retail sale of books in specialised stores 11 8 5 4 1 18
23 enlinea.com X G - 47.7 Retail sale of other goods in specialised stores 10 6 8 5 1 20
G - 47.72 Retail sale of footwear and leather goods in
Calzado Turismo X
24 specialised stores 6 3 3 6 0 12
K - 64.99 Other financial service activities, except insurance
ByB Asesoría contable y tributaria X
25 and pension funding 11 9 13 6 2 30
26 Jardín ABC X P - 85.1 Pre-primary education 12 6 3 9 4 22
27 Colegio Técnico Sudamericano X P - 85.3 Secondary education 23 6 7 9 1 23
28 CORNATEC Cía. Ltda. X 15 15 12 11 1 39
P - 85.59 Other education
29 Golden Bridge X 23 14 15 7 0 36
Table 2. Excerpt of identified actors and their occurrence in the 29 cases conducted.
Importadora Tomebamba
ByB Asesoría contable
Panadería Centenario
FEMUSA Mobiliarios
Cornatec Cía. Ltda.
Muebleria BienStar
Santana muebles
Calzado Turismo
Colegio Técnico
Giga Computers
JCEV Cia. Ltda.
Dress Up Store
APC Tecnología
HOLIDATSERV
Golden Bridge
Trebol Roses
ElectroUnion
Sport Chavis
Total Compu
CARTOPEL
enlinea.com
TECNISUR
Jardín ABC
Percentaje
All Design
FABRICA
KRISTEN
Sodilibro
CAPEDI
Forjart
Total
Generic actor Actor
Supplier X X X X X X X X X X X X XX 19 66% X X X X X
Raw material supplier X X X X X X X X 11 38% X X X
Parts supplier X 1 3%
Finished goods supplier X XX 3 10%
Supplies X X X X X 5 17%
Telecomunications supplier X X X X X 5 17%
Technology supplier X X X X X 5 17%
Basic services supplier X X X X X X X X 8 28%
Suppliers
Transport supplier X X X X X X 6 21%
Insurance and patent supplier X X X X X 5 17%
General Services supplier X X X X X X X X X 9 31%
Wholesale supplier X 1 3%
Retail supplier X 1 3%
Local supplier X X X X X X 6 21%
National supplier X X X X X X X 7 24%
International supplier X X X X 4 14%
Direct customer X X X X X X X X X X X X X X X X X X X X X X X X X 25 86%
These statistics point to that fact that they can be used as check list to support the
New customer X 1 3%
Important customer X X X X X X X X X X 10 34%
identification of actors in future cases. However we think that a more interesting finding
Wholesale customer X X 2 7%
Confident customer X 1 3%
is the fact that actors grouped into generic actors define orthogonal dimensions that can
Frequent customer X X X 3 10%
Retail customer X X X X X X X 7 24%
be used to categorize them (see table 4 for an excerpt). For instance, Actors categorized
Direct Customers
Employee customer X 1 3%
Specific area customer X X X X X 5 17%
under the Suppliers generic actor define at least three dimensions: Location (local, na-
Public institutions X X 2 7%
tional, International); Kind of supply (products –raw materials, supplies or technology-
Private organizations
International custommer
X X X
X
3 10%
1 3%
, or services); and Volume (wholesale or retail). The importance of this finding will be
Cash customer
Credit customer X
X
X
1 3%
2 7%
illustrated in section 4. Primary product or service
Secondary product
X
X X
1 3%
2 7%
It is important to notice that CM in most of the cases also included generic actors,
Municipality X X X X X X X X X X X X X X X 15 52%
Fire offices X X X X X X 6 21%
(even when more specific instances have been identified) e.g. generic actor Suppliers
Trade union X X 2 7%
Internal Revenue Service X X X X X X X X X X X X X X X X X X X X X X X X X X X 27 93%
Ecuadorian Social Security Institute X X X X X X X X X X X X 12 41%
Control Agencies Superintendent of companies X X 2 7%
Ministry of education X X 2 7%
Ministry of labor relations X X X X X 5 17%
Others (INCOP, ARCSA) X X 2 7%
Customs (SENAE) X X X X 4 14%
International standards agency X 1 3%
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Proceedings of the Eighth International i* Workshop (istar 2015), CEUR Vol-978
and the instances Row Materials, Technology, Basic Services etc., included in Table 2.
This fact supports the need of the “is-a” generalization-specialization construct in-
cluded in i*, as a mean to support the grouping of dependencies shared by instances of
a more generic actor. These dependencies representing intentional aspects common to
all of them in relation particular organizational processes.
Similarly to actors, some dependencies are instances of more generic ones, included
in patterns presented in [4], but also some additional ones were identified. 52 out
of the 189 dependencies appeared in at least 17% of the cases; 36 of them in at least
24% of cases. Dependencies are related to specific actors and stored together with them
in the patterns catalogue. Therefore, they can also be used as check lists to identify
dependencies to be included in CM of future cases, e.g. by using the instantiation rules
proposed in [4].
Table 3. Excerpt of generic dependencies found in the 29 cases for the actor supplier.
Importadora Tomebamba
Panadería Centenario
GIGA COMPUTERS
SANTANA Muebles
APC TECNOLOGÍA
Muebleria BienStar
Calzado Turismo
HOLIDAT SERV
TOTAL COMPU
Sudamericano
Dress up store
Golden Bridge
Dependency Type Actor
ByB Asesoría
Electro Union
Trebol Roses
ALL DESIGN
Sport Chavis
CORNATEC
CARTOPEL
SODILIBRO
enlinea.com
TECNISUR
Jardin ABC
Percentaje
FORJART
FABRICA
Direction
FEMUSA
CAPEDI
Kristen
JCEV
Total
Technology, products or services acquired --> Goal X X X X X X X X X X X X X X X X X X X X X X X X 24 69% Supplier / Service supplier
Technology, products or services --> Resource X X X X X X X X X X X X X X X X X X X X 20 38% Supplier
Payment made <-- Goal X X X X X X X X 8 83% Supplier
Quality of products and services --> Soft Goal X X X X X X X X X X X X X X X X X X X X 20 21% Supplier
Supplier
Local supplier
Timely delivery --> Soft Goal X X X X X X X X X X X X X X X X 16 3% Transport supplier
Timely billing --> Soft Goal X X X 3 34% Basic services supplier
Timely payments <-- Soft Goal X X X X X X X X X X X X 12 55% Supplier
Payment facilities/credits --> Soft Goal X X X X X X X X X X X 11 17% National supplier
Llow prices --> Soft Goal X X X X X X X X X X 10 14% Supplier
Discounts --> Soft Goal X X X X X 5 17% Supplier
Catalog --> Resource X X X X X X X X X 9 34% Supplier
Product/Service invoiced --> Goal X X X X X X 6 28% Supplier
Paymento documents
Bills --> x X X X X X X X X
Cash/Check <-- Resource X X X X X x X X X X X X X 16 21% Supplier
Product/services information Table 4. Dimension found for Customers generic actor.
--> Resource X X X X X X X 7 17% Supplier
Product, service or technology warranty --> Soft Goal X X X X X X X 7 24% Supplier
Technical support Generic actor -->Dimension
Soft Goal Actor Instances
X X Associated X dependencies
X X X Type Software supplier
6 3%Direction
Last minute missing supplies --> Resource X Widespread promotions Goal1 31% Local
--> supplier
Potencial
Promocional samples Resource Services
<-- supplier
Availability --> Soft Goal X X X Membership
X X X card X providedX X X Goal10 3% Basic
--> services supplier
Special introduction prices provided Soft goal Supplier
-->
New
Continued purchase Frecuency
<-- Soft Goalor X X Membership card X 4 24% Wholesaler
X Resource -->
Product shipped Volume
--> Goal X Personal
X information
X registered X Goal <--
X 5 69% Transport supplier
Refund and returns accepted --> Goal X X VIP benefits
X granted
X X Goal5 55% Supplier
-->
Transport/deliver product --> Task X Personalized X attention X X X X Soft
X goal3% Transport
7 --> supplier
Important
Large purchase order <-- Soft Goal VIP card X Resource -->
1 24% Wholesaler
Import processed --> Goal Important
X high volume order placed Goal <--
1 10% International transport supplier
Import license <-- Resource Product availability guaranteed X Goal1 41% International
<-- transport supplier
Product distribution agreement signed Soft goal <--
Wholesaler Increase sales through the distribution chain Soft goal <--
Product distribution agreement Resource <--
Product distribution chain achieved Soft goal -->
Distribution
Restocking in small quantities provided Goal -->
channel
Retailer Approach consumers through an specific location Soft goal <--
Customers
Increase sales through individual stores Soft goal <--
Specialized customer service infrastructure Soft goal -->
Specific market
Trained stuff for specific needs Soft goal -->
Segment
Specific documents Resource -->
Deferred payments Goal -->
Credit flexibility Soft goal -->
Credit Acceptance of various credit cards Soft goal -->
Payment
Voucher Resource -->
method
Warranty documents Resource <--
Cash rebates Goal -->
Cash
Money Resource <--
Technology, products or services provided Goal <--
Timely payments Soft goal <--
Products, services, technology Resource <--
Invoiced purchases Goal -->
Quality of products or services Soft goal -->
Bill Resource -->
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Proceedings of the Eighth International i* Workshop (istar 2015), CEUR Vol-978
Because of problems with i* semantics, and the descriptions used by modelers in
different cases, mapping of similar dependencies is not as straightforward as mapping
actors. For instance, for the generic actor Supplier we found the objective "Payment
Made" in 8 out of 29 cases. However, when later analyzed, it became evident that sys-
tems engineers were using other types of dependencies to state the same intentional
aspect, in order to emphasize aspects that were relevant for their administrative coun-
terparts, e.g. the soft goal "timely payment" or the resources "payment documents" or
"cash/check". In addition to semantics, variations can be attributed to lack of experience
of engineers, the existence of “unfamiliar” industrial glossaries or the fact that some
dependencies were omitted as redundant.
4 Reusing Knowledge Elements
At this point, we have shown important evidence supporting reusability of the proposed
patterns and their elements. Because of this, we can sustain that a good way to construct
i* SD-based CM, instead of departing from scratch, is to reuse the elements included
in the proposed patterns, going through them as a checklist and adopting those that are
relevant for the enterprise context being modeled. Furthermore, in [4] we have defined
several pattern instantiation rules specifically designed to support this process.
However, in this paper we argue that there can be and alternative and more system-
atic way to reuse CM elements (actors and dependencies), to construct complete i* SD-
based CM from scratch and eventually automate this process. An important aspect
emerging from this work, introduced in section 2, is the identification of several or-
thogonal dimensions useful to classify instances of generic the actors (see table 4 for
an excerpt in relation to the Customer generic actor). Each of these dimensions has a
set of associated value labels, representing potential actor instances (identified from
CM of the 29 case studies). These labels have sets of generic dependencies (also iden-
tified from the 29 case studies) associated to them. Based on this table, practitioners
(system engineers and administrative staff) can systematically identify a large number
of actors on their operational context, by selecting and combining labels from each di-
mension. To illustrate the approach, let’s consider the first two labels of three of the
Customer’s categorization dimensions in table 4, frequency/volume, distribution chan-
nel, and payment method. In this case, 12 combinations representing potential instances
of actors in the context of the organization are possible: Potential Wholesaler Credit,
Potential Wholesaler Cash, New Wholesaler Credit, New Wholesaler Cash, Important
Wholesaler Credit, Important Wholesaler Cash, Potential Retailer Credit, Potential
Retailer Cash, New Retailer Credit, New Retailer Cash, Important Retailer Credit, and
Important Retailer Cash.
Let’s assume that in a particular case the New Wholesaler Credit Customer is se-
lected from this set of combinations, then all the dependencies associated to labels in-
cluded in the name are potential dependencies to be included in the CM of the organi-
zation, see figure 2. In this way, identification of dependencies can also be automated.
Multi-inheritance shall be used in order to avoid duplication of dependencies in cases
were several instances of a same generic actor include occurrences of the same labels
on their names. Also dependencies associated to the generic actor have to be included
in the model for the reasons explained in section 3.
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Proceedings of the Eighth International i* Workshop (istar 2015), CEUR Vol-978
Fig. 1. Final generic i* model
5 Conclusions
In this paper we have presented an approach to automate construction of i* SD based-
CM, which reuses elements (actor and dependencies), included in the patterns presented
in [4]. Elements in these patterns have been validated, and patterns have been extended
with the results of 29 semi-industrial IS architectural design process, conducted in the
last three years. All of these projects used the DHARMA method, which requires en-
terprise CM to be constructed as departing activity for a IS architectural design.
We have also proposed a method to systematize the identification of context actors and
dependencies, and eventually automate the construction of i*-based CM. it is important
to remark that the proposal is based in a significant amount of empirical evidence which
makes it highly useful. We are currently finishing the construction of a tool to support
the method and exploring the ontological representation of patterns in order to improve
CM construction, by automatically recommending the elements to be included in them.
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