Chapter 6
Tools and Technologies for Architecture
Knowledge Management
Peng Liang and Paris Avgeriou
Abstract As management of architectural knowledge becomes vital for improv-
ing an organization’s architectural capabilities, support for (semi-) automating this
management is required. There exist already several tools that specialize in architec-
ture knowledge management, as well as generic technologies that can potentially be
used for this purpose. Both tools and technologies cover a wide number of potential
use cases for architecture knowledge management. In this chapter, we survey the
existing tool support and related technologies for different architecture knowledge
management strategies, and present them according to the use cases they offer.
6.1 Introduction
Architecting is a multifaceted technical process, involving complex knowledge-
intensive tasks [195]. The knowledge that is both produced and consumed during the
architecting activities is voluminous, broad, complex, and evolving and thus cannot
be manually managed by the architect. Furthermore, such architectural knowledge
(AK) [193] needs to be shared and reused among a number of different stakeholders,
and across a number of the lifecycle phases. Especially as the size and complexity
of systems increase, more stakeholders need to get efficiently involved in the archi-
tecting process and the knowledge management issues become quite challenging.
The problem is exacerbated in the context of multi-site or global software devel-
opment [75]. Finally the industry has also come to realize the need for efficient
inter-organization AK sharing [76].
Therefore, the management of AK needs to be automated or semi-automated by
appropriate tool support. This can be achieved similarly to traditional knowledge
management tool support, by emphasizing the characteristics of software architect-
ing. For example, tool support for architecture knowledge management (AKM) may
Peng Liang (
B
) and Paris Avgeriou
University of Groningen, The Netherlands, e-mail: liangp@cs.rug.nl,paris@cs.rug.nl
M. Ali Babar et al. (eds.), Software Architecture Knowledge Management,
DOI: 10.1007/978-3-642-02374-3 6, c© Springer-Verlag Berlin Heidelberg 2009
91

92 P. Liang and P. Avgeriou
concern enforcing an architecting process, reusing architecting best practices, docu-
menting architecture decisions, providing traceability between design artifacts, and
recalling past decisions and their rationale. AKM tools can support a wide number
of use cases, thus reducing the complexity of knowledge management in the archi-
tecting process and facilitating the knowledge-based collaboration of the involved
stakeholders.
In knowledge management, a distinction is often made between two types of
knowledge: implicit and explicit knowledge [234]; see also Chap. 4. Implicit (or
tacit) knowledge is knowledge residing in people’s heads, whereas explicit knowl-
edge is knowledge which has been codified in some form (e.g. a document, or a
model). Two forms of explicit knowledge can be discerned: documented and for-
mal knowledge. Documented knowledge is explicit knowledge which is expressed
in natural language or images in documents. Typical examples of documented AK
are Word and Excel documents that contain architecture description and analysis
models. Formal knowledge is explicit knowledge codified using a formal language
or model of which the exact semantics are defined. Typical examples of formal AK
models include AK ontologies [190] or AK domain models [10, 44, 325] that for-
mally define concepts and relations, and aim at providing a common language for
unambiguous interpretation by stakeholders. Organizations can employ three dis-
tinct strategies for managing their knowledge: codification, personalization [7, 143],
and the hybrid strategy which combines the previous two [92]; see also Chap. 1. Fig-
ure 6.1 presents these different knowledge types in the vertical dimension combined
with two knowledge management strategies in the horizontal dimension.
In this chapter, we first present a set of possible use cases that can be supported
by AKM tooling. The set is not meant to be exhaustive; however it is well-grounded
as it comprises a superset of the use cases either implemented or being in the
wish-list of the existing AKM tools. In Sects. 6.2 and 6.3, we present existing tool
support for the codification and hybrid knowledge management strategies, respec-
tively. To the best of our knowledge there are no tools that support purely the
Personalization Strategy Codification Strategy
Type of
Knowledge
Formal
Formal
Documented Documented
Tacit Tacit
Quantity of knowledge within an
organization
Quantity of knowledge within an
organization
Explicit
Implicit
Explicit
Implicit
Fig. 6.1 Pyramid of knowledge types and the associated knowledge management strategies [159]

6 Tools and Technologies for Architecture Knowledge Management 93
personalization strategy without any codification. However we will present some
technologies for personalization in Sect. 6.5. Some of the technologies are mature
and can be used off-the-shelf while others are still applied in an experimental setting
and are presented here as a future research challenge.
6.2 Use Cases of AK Management
In this section, we describe use cases for AKM tooling to present the potential set of
tool features in this domain and also set the stage for presenting existing tools and
technologies (discussed in Sects. 6.3–6.5). The use cases define the requirements for
developing an AKM tool, i.e. who would use it (actors), to do what (use cases)? We
came up with this use case model by surveying a series of papers [10, 113, 192, 326],
which provide at least some kind of usage of AK (requirements, services, functions,
use cases, etc.). We formed a superset of use cases by selecting, merging, and gen-
eralizing from the use cases of the individual approaches. Some of them came from
interviews with practicing architects, while others originate from researchers’ expe-
rience. Furthermore some use cases have been implemented in tools, while others
remained in the “wish-list” of researchers.
6.2.1 Actors
Who would use the AKM tool?
• Architects designing a system (or a subsystem of a large system) by making de-
cisions. They keep the tacit AK in mind or transform it from tacit to documented
or formalized knowledge [326].
• Reviewers involved in judging the quality or progress of an architecture [192].
• Requirements engineers who view AK from the problem space [192]. Developers
involved in the implementation of the architecture design and decisions [326].
• Maintainers who evolve or maintain the system and need to understand the
correlation between the decisions they take and existing, relevant AK [326].
• Users of the AKM tool are the entire set of system stakeholders [192]. All the
actors mentioned above are specialized actors of User.
6.2.2 Use Cases
We present the use cases (UC) by grouping them into four distinct categories, as il-
lustrated in Fig. 6.2: Actors either consume AK by using it for specific purposes, or
produce AK by creating new or modifying existing AK [195]; knowledge manage-
ment concerns general low-level functionality to manage AK data; and intelligent

94 P. Liang and P. Avgeriou
AKM System
UC14
Evaluate AK
User
UC24
Assess design
maturity
UC7
Reuse AK
UC22
Cleanup the
architecture
UC15
Conduct a trade-off
analysis
UC19
Notify user about
new AK
UC18
Search/Retrieve AK
UC16
Add/Store AK
UC13
Recover architectural
decisions
UC4
Share AK
UC8
Elicit/Capture AK
UC17
Edit AK
UC2
View AK and their
relationship
UC21
Enrich AK (semi-)
automatically
UC09
Distill AK
UC6
Apply AK
UC3
Trace AK
UC10
Integrate AK
UC1
Learn AK
UC23
Offer decision-
making support
UC11
Synthesize AK
UC5
Identify stakeholder
Consuming AK Producing AK
Intelligent Support
Knowledge Management
UC12
Translate AKArchitect
Developer
Requirements
engineer
Maintainer
Reviewer
Use Case
Legend UCs interact
with Architect
UCs interact
with User
UCs interact
with Reviewer
UC20
AK versioning
Fig. 6.2 Panorama of the AKM use case model
support concerns automating AKM tasks within the architecting process that require
either rigor or intelligence.
We do not claim that the list of use cases is exhaustive, but they do cap-
ture all use cases in the surveyed literature. Some use cases are kept unchanged
from the original source (e.g. Assess design maturity [326]) while others have
been merged (e.g. Reuse AK [326] includes Clone AK [192] and Stimulate reuse
of best practices [113]), or generalized (e.g. Identify stakeholders is generalized
from Identify the subversive stakeholder [192] and Identify affected stakeholders
on change [326]). These use cases, together with their included and specialized
use cases, are discussed within the presentation of the AKM tools in Sects. 6.3 and
6.4. In this section, we very briefly discuss each use case, and refer to the origi-
nal sources for more information. It is noted that the actors are explicitly specified
only for the use cases whose actor is the Architect or the Reviewer and not the
generic User. Also we consider the generalization relationship between use cases
as in [40].

6 Tools and Technologies for Architecture Knowledge Management 95
Consuming AK
• UC1, Learn AK [313]: learn and comprehend the AK, e.g. understand the ratio-
nale of a design decision.
• UC2, View AK and their relationships [196]: view both AK elements and re-
lationships e.g. the architectural decisions made and the relationships between
these decisions.
• UC3, Trace AK [12]: trace between various AK elements, e.g. design decisions,
rationale, and design.
• UC4, Share AK [313]: share knowledge with one or more actors of the system.
• UC5, Identify stakeholder [192, 326]: the architect identifies a stakeholder ac-
cording to certain criteria, e.g. who has the most “weight” on the architectural
decisions.
Producing AK
• UC6, Apply general AK [313]: use application-independent AK, e.g. apply archi-
tecture patterns to solve the problems at hand.
• UC7, Reuse AK [326]: the architect reuses AK in another project context, e.g.
reusing architectural design decisions from an old to a new project.
• UC8, Elicit/Capture AK [10, 196]: elicit and capture AK from various resources,
e.g. individuals, teams, or documents.
• UC9, Distill AK [313]: distill specific knowledge from a system into general
knowledge (e.g. architecture pattern) that can be reused in future systems.
• UC10, Integrate AK [313]: integrate different types of information into con-
crete AK, e.g. integrate stakeholder requirements, system context, and technology
constraints into system requirements.
• UC11, Synthesize AK [313]: the architect applies the design decisions and pro-
duces the system design (e.g. components and connectors).
• UC12, Translate AK [208]: translate the formal AK based on a given AK domain
model into another domain model to facilitate reuse.
• UC13, Recover architectural decisions [326]: the architect reconstructs decisions
with their associated rationale from an existing or 3rd party system.
• UC14, Evaluate AK [313, 326]: the reviewer performs a critical evaluation of the
AK, e.g. to make sure that requirements have been satisfied in the architecture
design.
• UC15, Conduct a trade-off analysis [326]: analyze the architecture by trading off
different quality attributes.
Knowledge Management
• UC16, Add/Store AK [192]: add and store elements of AK into the knowledge
repository.
• UC17, Edit AK [10]: modify or delete AK elements in the repository.
• UC18, Search/Retrieve AK [196, 313]: search through the existing AK using
certain criteria (e.g. keywords and categories, etc.).

96 P. Liang and P. Avgeriou
• UC19, Notify user about AK changes [113]: subscribe to specific AK elements,
and subsequently get notified about changes to them.
• UC20, AK versioning [192]: create and manage different versions of the AK.
Intelligent Support
• UC21, Enrich AK (semi-) automatically [196]: generate AK content proactively,
e.g. automatically distilling and interpreting AK from text without the users’
intervention.
• UC22, Cleanup the architecture [326]: the architect makes sure that all the de-
pendencies of removed AK (e.g. the consequences of an architectural decision)
have been removed as well.
• UC23, Offer decision-making support [113, 196]: provide automated support
for the Architect in the process of making decisions, e.g. through well-founded
advices and guidelines.
• UC24, Assess design maturity [326]: the architect evaluates when the architecture
can be considered as finished, complete, and consistent, e.g. verify whether a
system conforming to the architecture can be made or bought.
6.3 Tool Support for Codification
In this section, we present the AKM tools that support the codification strategy by
discussing for each one: a brief introduction, how they support the use cases listed in
Sect. 6.2.2 (full or partial support) and their special focus (e.g. architecture design
support, evaluation support, etc.). The order of presenting the tools is organized
according to the type of knowledge they support: the SEI-ADWiki supports docu-
mented AKM; ADkwik and ADDSS support both documented and formal AKM;
and the rest support formal AKM.
6.3.1 SEI-ADWiki
This tool is a wiki-based collaborative environment for creating architecture docu-
mentation and is used by students in the Carnegie Mellon University Master of Soft-
ware Engineering program [26]. The tool is not named, so we call it SEI-ADWiki
for ease of reference within this chapter.
Supported Use Cases
6.3.1.1 View AK and their relationships (UC2): users can view the content of ar-
chitecture documents and relationships within that content (e.g. mapping
between architecture views) through a navigation bar.
6.3.1.2 Trace AK (UC3): users can create traceability between architectural artifacts
(documented AK represented in wiki pages) through hyperlinks.

98 P. Liang and P. Avgeriou
6.3.2.3 Share AK (UC4): users can share AK across project boundaries with other
users. Special attention is given to architecture decisions, which are codified
in formal AK and shared according to the SOAD model.
6.3.2.4 Reuse AK (UC7): users can reuse the AK about enterprise application
architectures contained in the architectural decision repository.
6.3.2.5 Harvest AK (generalized UC from UC8 and UC9): users can update the
AK with new decisions, experiences, patterns and rationale gathered by
both successful and failed projects. This UC concerns eliciting/capturing
AK (e.g. decisions) and distilling AK (e.g. patterns) and, therefore it is a
generalized UC from UC8 and UC9.
6.3.2.6 Search/Retrieve AK (UC18): users can search/retrieve the AK from the
tagged wiki pages.
6.3.2.7 AK versioning (UC20): users can manage different versions of the AK by
tracing the wiki changes at the page level.
6.3.2.8 Offer decision-making support (UC23): users can find and reuse appropriate
decisions in the architectural decision repository.
Special Focus
The main difference of ADkwik from other wikis is that ADkwik is an applica-
tion wiki as opposed to a plain standard wiki. It is supported by relational database
underneath whose tables are structured based on the SOAD domain model [346],
while standard wikis also have databases, but the tables are wiki pages. The AK in
ADkwik is also structured according to SOAD model to enable formal AK shar-
ing between stakeholders and projects. Consequently ADkwik combines the open
access of wikis and formal knowledge based on the underneath domain model to
provide efficient AK sharing and management.
6.3.3 ADDSS
ADDSS2 (architecture design decision support system) is a Web-based tool for
storing, managing and documenting architectural design decisions taken during the
architecting process and providing traceability between requirements and architec-
tures through the decisions [67].
Supported Use Cases
6.3.3.1 Learn AK (UC1): users can understand the architectural decisions by view-
ing and replaying the evolution of decisions over time.
6.3.3.2 View AK and their relationships (UC2): users can easily view the AK
and their relationships presented in Web pages and structured according to
specific templates.
2 http://triana.escet.urjc.es/ADDSS.

6 Tools and Technologies for Architecture Knowledge Management 101
6.3.5 AREL
AREL4 (Architecture Rationale and Elements Linkage) is a UML-based tool that
aims in creating and documenting architectural design with a focus on architectural
decisions and design rationale [316]. Three types of AK are captured in AREL:
design concerns, design decisions and design outcomes, which are all represented
in UML. An extensive example of the use of AREL in design reasoning is provided
in Chap. 9.
Supported Use Cases
6.3.5.1 Learn AK (UC1): users can understand the design outcome with its asso-
ciated design rationale (concern and decision) based on the AREL causal
model.
6.3.5.2 View AK (UC2): users can view the AK elements and relationships in UML
diagrams.
6.3.5.3 Trace AK (UC3): users can trace design concerns and design outcomes to
design decisions using the UML dependency relationship.
6.3.5.4 Identify AK change impacts (included in UC14): users can identify all the
design decisions and other AK elements, that are directly or indirectly im-
pacted when AK is modified, based on the AREL causal model. This UC
provides information for evaluating AK, e.g. evaluate the impact of AK
change, so this is an included UC of UC14.
6.3.5.5 Elicit/Capture AK (UC8): users can capture AK during the architecting
process using a UML modeling tool. They can also elicit AK from text-
based requirement specifications using UML models.
6.3.5.6 Synthesize AK (UC11): users can implement design decisions into the
system design in UML diagrams, based on the AREL domain model.
6.3.5.7 Conduct a trade-off analysis (UC15): cross-cutting concerns often re-
quire trade-off analysis at multiple decision points, and users can conduct
such an analysis by tracing between design concerns and design outcomes
that implement the cross-cutting concerns (especially the non-functional
requirements).
6.3.5.8 Add/Store AK (UC16): users can save the elicited/captured AK in UML
models.
6.3.5.9 Edit AK (UC17): users can edit the AK through the corresponding UML
models.
6.3.5.10 Search/Retrieve AK (UC18): users can use the search and retrieval func-
tions provided by the UML modeling to find AK elements within the UML
models.
6.3.5.11 Detect architecture design conflicts (included in UC24): users can detect
the design conflicts by looking at the missing links (design gaps) between
design concerns and design outcomes using the AREL causal model. This
4 www.ict.swin.edu.au/personal/atang/AREL-Tool.zip.

102 P. Liang and P. Avgeriou
UC can be used for accessing design maturity and, therefore it is included
in UC24.
Special Focus
AREL represents various AK elements using UML profiles, thus integrate AKM
into a UML modeling tool (e.g. Enterprise Architect). This enables the architect
to record the design decisions as part of the architecture design. AREL focuses on
linking the problem space (design concerns) to the solution space (design outcomes)
through design decisions in a uniform way.
6.3.6 Knowledge Architect
The Knowledge Architect (KA) is a tool suite for capturing, using, translating,
sharing and managing AK. It is based on a common AK repository accessed by
different clients (Document Knowledge Client, Excel and Python Plug-in, Knowl-
edge Explorer and Knowledge Translator) [208]. The tool suite makes extensive use
of technologies developed for the Semantic Web to allow for formal AK manage-
ment. The Knowledge Architect is one outcome of the GRIFFIN project, discussed
in Chap. 8.
Supported Use Cases
6.3.6.1 View AK and their relationship (UC2): users can view the AK entities and
their relationships in the Knowledge Explorer.
6.3.6.2 Trace AK (UC3): users can create traceability between AK entities using
the different client tools.
6.3.6.3 Share AK (UC4): users can share AK entities with other users by storing it
centrally in the Knowledge Repository and accessing it using the various
client tools.
6.3.6.1 Elicit/Capture AK (UC8): users can elicit/capture AK by annotating archi-
tecture documents and models using the KA client tools.
6.3.6.5 Integrate AK (UC10): users can integrate various types of AK (from re-
quirements to design artifacts) into the Knowledge Repository based on a
common domain model.
6.3.6.6 Translate AK (UC12): users can perform automatic translation based on
different AK domain models through the Knowledge Translator [207].
6.3.6.7 Add/Store AK (UC16): users can save the captured (annotated) AK entities
into the Knowledge Repository through the client tools.
6.3.6.8 Edit AK (UC17): users can edit the AK entities through the client tools.
6.3.6.9 Search/Retrieve AK (UC18): users can query the AK entities and their re-
lationships in the Knowledge Repository through its Query Engine, using
the RDF query language.
6.3.6.10 Check completeness of AK (included in UC24): users can check the com-
pleteness of AK in a document (e.g. whether a Decision Topic has been
addressed by at least one Alternative) through the Document Knowledge

6 Tools and Technologies for Architecture Knowledge Management 103
Client. Checking completeness of AK is part of accessing design maturity,
so this is an included UC of UC24.
Special Focus
The Knowledge Architect focuses on capturing AK through annotating informa-
tion in different sources (e.g. Word, Excel documents), and sharing it in a central
repository. The tools suite also focuses on traceability management and intelligent
support, as AK entities and relationships are semantically specified in OWL [37].
6.3.7 SEURAT
SEURAT5 (Software Engineering Using RATionale system) is an Eclipse plug-in
that is targeted to rationale knowledge management in an integrated development
environment (IDE), from requirements to design and finally to source code [60][62].
The concept of rationale knowledge in SEURAT is composed of design decisions,
alternative solutions considered, and the reasons (arguments for each solution)
behind the final decisions.
Supported Use Cases
6.3.7.1 Learn AK (UC1): users can understand rationale knowledge and all its
parts. It is represented with a formal argument ontology (for details on the
argument ontology see [59]), which semantically assists the understanding
of the rationale knowledge.
6.3.7.2 View AK (UC2): users can view rationale knowledge within the Eclipse
environment in a hierarchical view – from list of decisions to alterna-
tive solutions and finally to the “arguments” for or against each solution.
Users can also view rationale knowledge through the rationale hierarchy
report (in the same layout as in the hierarchical view) and the rationale
traceability matrix report generated by the tool.
6.3.7.3 Trace AK (UC3): users can trace the rationale knowledge (e.g. “how do
we compare dates?”) to source code (e.g. function compareDates()) di-
rectly in the Eclipse environment using bookmarks. Users can also trace
requirements to the decisions made and captured in the rationale.
6.3.7.4 Elicit/Capture AK (UC8): users can capture rationale knowledge during
source code development. They can also import rationale knowledge from
Word documents where text has been annotated as rationale.
6.3.7.5 Decision evaluation and impact assessment (included in UC14): users can
evaluate decisions by calculating the “support score” for each alternative
solution based on the arguments for and against it. Users can also dis-
able some requirements when stakeholders change their mind about them,
and see which decisions may require re-examination due to the impact
5 www.users.muohio.edu/burgeje/SEURAT/Downloads.html.

104 P. Liang and P. Avgeriou
assessment of requirements. This UC aims at evaluating design decisions,
a type of AK, so this is an included UC of UC14.
6.3.7.6 Conduct a trade-off analysis (UC15): users can conduct trade-off analysis
of a decision based on the “background knowledge” (e.g. “A more flex-
ible solution costs more to develop”) of this decision which is explicitly
recorded in the rationale.
6.3.7.7 Add/Store AK (UC16): users can add rationale knowledge using an editing
interface integrated in Eclipse and store it in a relational database.
6.3.7.8 Edit AK (UC17): users can edit the text of rationale knowledge using the
editing interface.
6.3.7.9 Search/Retrieve AK (UC18): users can search/retrieve rationale knowl-
edge elements through keyword-based search, including requirements,
decisions, alternatives and arguments.
6.3.7.10 Offer decision-making support (UC23): users can get decision-making
support using the “support scores” for each alternative solution.
6.3.7.11 Check for completeness and consistency of rationale knowledge (included
in UC24): users can detect the incompleteness and inconsistency of the
rationale knowledge through inferencing based on the argument ontology,
e.g. for completeness, checks are made to ensure that there are alternatives
proposed for each decision. This UC can be used for accessing design
maturity and, therefore it is included in UC24.
Special Focus
SEURAT is not specifically used for the management of AK but for rationale knowl-
edge. However, in a broad sense, rationale knowledge about architecture design (e.g.
arguments linked from requirements to alternative design solutions) is an important
part of AK. In addition, SEURAT mainly focuses on the application of rationale
knowledge supporting software maintenance [61].
6.4 Tool Support for the Hybrid Strategy
In this section, we present the AKM tools that support the hybrid strategy in the
same structure as in Sect. 6.3.
6.4.1 EAGLE
EAGLE [114, 113] is an AK sharing portal that implements best practices from
knowledge management for improving AK sharing. The main features of EA-
GLE include integrated support for both codified and personalized AK, support
for stakeholder-specific content, and AK subscription and notification. EAGLE is
a result of the GRIFFIN project, discussed in Chap. 8.

6 Tools and Technologies for Architecture Knowledge Management 105
Supported Use Cases
6.4.1.1 Share AK (UC4): users can share AK in both personalized (e.g. news,
events and experience with colleagues) and codified (e.g. best practices,
documents) formats.
6.4.1.2 Find a colleague based on expertise or competence (included in UC4):
users can find the right person, whose personal knowledge may match a
specific AK request. This UC provides information for personalized AK
sharing, so it is an included UC of UC4.
6.4.1.3 Overview of personal information of colleagues (included in UC4): users
can get an overview of “who knows what” and “who is doing what” among
their colleagues. This UC also provides information for personalized AK
sharing, so it is an included UC of UC4.
6.4.1.4 Add/Store best practices (specialized of UC16): users can add best prac-
tices to a repository (codified AK) for reuse and decision making support.
Best practices are a special type of AK, so this is a specialized UC of
UC16.
6.4.1.5 Add/Store architecture document (specialized of UC16): users can add ar-
chitecture documents to a repository according to various AK categories.
Similarly to the UC in 5.1.4, this is also a specialized UC of UC16.
6.4.1.6 Search/Retrieve AK (UC18): users can access generic documentation (dif-
ferent types of company documents) by document title, keywords or
categories, and also search for project-specific AK documentation.
6.4.1.7 Search/Retrieve related AK (included in UC18): users can access exter-
nal information sources to find related AK, such as white papers (codified
AK), seminars and trainings (personalized AK) or other corporate commu-
nication, e.g. discussion board (personalized AK). Related AK is a special
kind of AK, so this is a specialized UC of UC18.
6.4.1.8 Notify user about new AK (specialized of UC19): user can stay up-to-date
about new AK through subscription and notification mechanisms. New AK
is a kind of AK change and, therefore this is a specialized UC of UC19.
6.4.1.9 Offer decision-making support (UC23): Users can get intelligent support
by answering a questionnaire during the decision-making process, and au-
tomatically receiving a number of architectural guidelines that match their
answers.
6.4.1.10 Overview of project stakeholders (included in UC24): users can have an
overview about project stakeholders, e.g. contact information and expertise
area. They can subsequently request all the involved stakeholders to access
the design maturity and, therefore this is an included UC of UC24.
Special Focus
EAGLE focuses on stakeholder collaboration during the architecting process, by
enabling them to connect to colleagues or other involved stakeholders by retrieving
“who is doing what” and “who knows what”. In addition, codified AK in a document
repository or best practice repository can also be easily accessed using advanced
search mechanisms.

6 Tools and Technologies for Architecture Knowledge Management 107
present a number of these technologies to demonstrate their value for AKM tools,
and to assist tool vendors in selecting the appropriate ones for their own needs. The
order of presenting the technologies is organized according to the type of knowl-
edge they support: Web portal, blog and wiki support the hybrid strategy, voting and
ranking support the personalization strategy, and the rest support the codification
strategy.
6.5.1 Web Portal
A Web portal [131, 319] is a Web site that provides integrated modules, like hosted
databases, yellow pages, discussion boards, news push, document management,
email and more. Web portals automatically personalize the content generated from
these modules to provide a personalized experience to users. The yellow pages mod-
ule can record the expertise area, involved projects and contact information of all the
architects in an organization, thus providing support for personalized AK sharing
(UC4). Emails, news push and discussion boards provide communication support
for AK sharing (UC4) through a collaboration space among users. News push also
supports AK changes notification (UC19) when personalized information is changed
(e.g. personnel movement).
This technology is also useful for codified AK management. The hosted cen-
tral databases and client/server architecture can facilitate AK sharing (UC4), and
Web forms can be used for tracing (UC3), eliciting/capturing (UC8), adding/storing
(UC16), and editing (UC17) AK.
6.5.2 Blog and Wiki
Different from yellow pages, blogs and wikis are both editable Web pages by indi-
vidual users who are distributed over the network. Blogs are for single users, and
wikis are designed to enable anyone to contribute or modify content, so they both
support personalized AK sharing (UC4). For example individual users can provide
up-to-date and more reliable personal information, such as their expertise area and
personal knowledge.
As a collaborative Web editing system, wikis also support codified AK manage-
ment for both documented and formal AK. We classify the wikis as general wikis
for documented AKM (e.g. SEI-ADWiki) and semantic wikis for formal AKM
(e.g. ADkwik). Both types of wikis can support the following AKM use cases:
AK viewing (UC2) and AK traceability (UC3), adding/storing AK (UC16), editing
AK (UC17), searching/retrieving AK (UC18), user notification about AK changes
(UC19), and AK sharing (UC4). Some practical experience of applying wikis to
support AK sharing can also be found in [116].

108 P. Liang and P. Avgeriou
Generic wikis simply document AK in the wiki pages. On the other hand, seman-
tic wikis provide semantic support through formal models (e.g. semantic annotation
and semantic query of AK). In addition, wikis have also been used in requirements
engineering to support requirements knowledge management in a codification strat-
egy, e.g. for documented requirements [91] and formal requirements knowledge
[212].
6.5.3 Voting and Ranking
Voting and ranking is a method to evaluate and rank the characteristics (e.g. cred-
ibility and reliability, etc.) of objects or people by different users in an online
community. It has been widely applied in many social networking systems (e.g.
LinkedIn) and C2C business platforms (e.g. eBay) for the evaluation and ranking of
personal information.
The personal information recorded in Web portals, wikis and yellow pages has
unavoidably personal and subjective bias (e.g. the expertise of an architect). Using
the voting and ranking mechanism can partially mitigate this problem, and provide
more credible personal information. For example ranking the expertise of different
stakeholders on a technology platform by other members of an organization helps to
create reliable “who knows what” information, and thus efficient personalized AK
sharing (UC4).
6.5.4 Natural Language Processing
Natural language processing (NLP) is concerned with the understanding of human
natural languages by computers. Since documentation in natural language is domi-
nant in AK resources (most documented AK is in natural language), it is beneficial
to introduce NLP techniques in AKM tools.
Several AKM use cases have been supported by NLP techniques. The LSA
(Latent Semantic Analysis) technique has been used to elicit/capture AK (UC8)
semi-automatically [45]. Text mining techniques have been used to enrich AK
(UC21) [114], and offer decision-making support (UC23) [196].
6.5.5 Ontologies
Ontologies are formal structures supporting knowledge representation, manage-
ment, sharing and reusing [120], and have been widely used in various fields, such
as the Semantic Web. They represent explicitly the semantics of structured and

6 Tools and Technologies for Architecture Knowledge Management 109
semi-structured information and so enable sophisticated automatic support for ac-
quiring, maintaining and accessing information [90]. Formal AK, as a kind of formal
knowledge, can be represented by ontology models (see for example [190]). Various
ontology techniques have been explored in AKM tools, including ontology model-
ing, ontology database, ontology mapping, and ontology-based inferencing. These
techniques will be elaborated in the following paragraphs.
Ontology modeling can be used to describe domain concepts and their relation-
ships. In this respect, AK ontology models are composed of AK domain concepts
(e.g. Design Decision, Alternative, and Risk) and relationships (e.g. addressedBy
and containedIn). Related standards on ontology specification to specify the ontol-
ogy modeling results have been defined by the W3C, e.g. RDF [184] and OWL [37],
with ontology modeling tools support, e.g. Prote´ge´7. Combined with ontology mod-
els, an AKM tool can support the following use cases of formal AK management:
UC1 (Learn AK) - the ontology concepts and relationships can help users understand
the meaning of AK; UC2 (View AK and their relationships), UC3 (Trace AK) and
UC22 (Clean up architecture) - these use cases are supported by using the semantic
relationships defined between AK concepts.
Ontology databases store data in ontological data models. For example the RDF
store Sesame [53] stores data in the RDF triple format. Ontology databases provide
semantic querying using their specific query language, e.g. SPARQL [258] of the
W3C, SeRQL for Sesame. For example, one can query the ontology database by
posing question like “Tell me all the alternative solutions addressed to decision topic
’the control method over the data processing pipelines’ which are not in conflict
with each other”. The following use cases can be supported by ontology databases
in formal AK management tools: Add/Store AK (UC16) in ontological data models,
Search/Retrieve AK (UC18) by query languages, and Share AK (UC4) after getting
the query results.
Ontology mapping is an activity to semantically relate two ontologies [112]. It
provides a semantic translation between heterogeneous ontologies and therefore en-
ables knowledge sharing in a semantically sound manner [169]. This is essential for
sharing AK that originates from different organizations and is based on different
AK ontologies. UC4 (Share AK) and UC12 (Translate AK) can be supported by
ontology mapping techniques (see e.g. the Knowledge Translator [208]).
Ontology-based inferencing concerns retrieving knowledge and creating deduc-
tive knowledge based on ontology models with logic-based reasoning [333]. In
AKM tools, the inferencer can be mostly used to automatically infer the relation-
ships that exist between the formal AK entities, e.g. an inverse relationship between
AK elements (for traceability) or a mapping relationship between elements from
different AK domain models (for translation). The inferencer can support the fol-
lowing use cases: Search/Retrieve AK (UC18) e.g. by the SeRQL query language
of OWLIM inference engine [181], Check for completeness (included in UC24)
e.g. by the KA client tools [208], and Translate AK (UC12) e.g. by the Knowledge
Translator [208].
7 http://protege.stanford.edu/.

110 P. Liang and P. Avgeriou
6.5.6 Plug-in
A plug-in consists of a program that connects and interacts with a host system (e.g.
a Web browser or an email client) to provide a specific function on demand. This
technology is quite beneficial for promoting AK usage through tools that archi-
tects have being working and are familiar with. Typical tools that architects use
through the architecting process include word processors for architecture documen-
tation, spreadsheets for quantitative architecture evaluation and UML modelers for
architecture design.
Examples of the tool plug-in technology include the KA Document Knowledge
Client (Word plug-in) and AREL (UML modeling tool plug-in). Both plug-ins sup-
port the following AKM use cases: Learn AK (UC1), View AK (UC2), Trace AK
(UC3), Elicit/Capture AK (UC8), Synthesize AK (UC11), Add/Store AK (UC16)
and Edit AK (UC17).
6.5.7 Version Management
This technology concerns the management of multiple revisions of the same unit of
information. The versioning function of wikis, SVN (Subversion) and CVS (Con-
current Versions System) are typical examples of this technology. Wikis can track
the version changes at the page level. For every page, it is easy to look up earlier
versions, display the differences between two versions, and revert to an older ver-
sion. SVN and CVS provide similar functions for the version management of files
which can be used to record documented AK (e.g. Word documents) and also for-
mal AK (e.g. RDF files). AK evolves rapidly during the architecting process, and
effective version management of AK can support directly AK versioning (UC20)
and indirectly viewing the change history of AK (includes UC20).
6.5.8 Web 2.0
Web 2.0 aims to enhance creativity, information sharing, collaboration and func-
tionality of the Web. Interesting techniques in Web 2.0 for codified AKM include
push and pull mechanisms, tags and context-aware mashups. Push and pull mecha-
nisms (e.g. RSS – Rich Site Summary) can be used to notify user about AK changes
(UC19) for subscribed users [114]. Tags can be used to search/retrieve AK (UC18)
for Web pages that have been tagged. Context-aware mashups can be used to view
AK and their relationships (UC2) e.g. all the inter-dependent elements of an archi-
tectural decision can be shown in a dynamic mashup Web page, which combines the
AK elements from more than one source into a single integrated Web page [17].

6 Tools and Technologies for Architecture Knowledge Management 111
6.6 Summary
For the effective usage of AK in the architecting activities, the AKM tools have
been recognized as a great contribution [7]. In this chapter, we provide a survey of
current tools and technologies with respect to the AKM strategies they adopt and the
use cases they support. We hope to help AKM tool developers in understanding the
state-of-the art and practice and get inspired in building their own tools. We expect
that depending on their specific needs and organizational context, they will mix and
match the appropriate technologies and ideas from existing tools, in order to build
customized AKM tools. We are confident that, as more AKM tools are built, more
AK will be used in practice and shared among organizations and thus contribute to
establishing AKM in the daily organizational practices.
It is noted that the following use cases, identified in Sect. 6.2, have not been
fully supported (implemented) by existing AKM tools: UC5 (Identify stakeholder),
UC11 (Synthesize AK), UC13 (Recover architectural decisions), UC14 (Evaluate
AK), UC21 (Enrich AK (semi-) automatically), and UC24 (Assess design maturity).
We regard these use cases as the future challenges, which AKM tool developers
can work on to provide more added value to existing AKM. We also believe that
some promising technologies can be the key for implementing these use cases,
such as NLP for intelligent support (advices and guidelines for making decisions)
[196], context-aware text mining for the elicitation of user interests about AK, and
ontology inferencing for the enrichment of AK.
Acknowledgements This research has been partially sponsored by the Dutch Joint Academic
and Commercial Quality Research & Development (Jacquard) program on Software Engineer-
ing Research via contract 638.001.406 GRIFFIN: a GRId For inFormatIoN about architectural
knowledge.