ORGANIZATIONAL KNOWLEDGE MANAGEMENT THROUGH
SOFTWARE PROCESS REUSE AND CASE-BASED REASONING
Viviane A. Santos and Mariela I. Cortés
State University of Ceará, Fortaleza, Brazil
Keywords: Case-based Reasoning, Process Reuse and Improvement, Process Asset Repository, Management Tool.
Abstract. Software process reuse involves different aspects of the knowledge obtained from generic process models
and previous successful projects. The benefit of reuse is reached by the definition of an effective and sys-
tematic process to specify, produce, classify, retrieve and adapt software artifacts for utilization in another
context. In this work we present a formal approach for software process reuse to assist the definition, adap-
tation and improvement of the organization’s standard process. A tool based on the Case-Based Reasoning
technology is used to manage the collective knowledge of the organization.
1 INTRODUCTION
The organization knowledge is defined, in general,
by the tacit and explicit knowledge from several
subunits or groups combined and used to create new
know-ledge (Schulz, 2002). In the software devel-
opment area it is considered a best practice and is
highly recommended since successful and even
unsuccessful experiences can help the organization
learn from the past (ISO, 2006) (PMI, 2004).
The purpose of the process reuse technology is
to support the process definition and continuous
improvement on the basis of standard processes,
accor-ding to norms and quality models, and learned
experiences (Perry, 1996).
Dynamic and context-depending aspects of the
knowledge in software development turn the Case-
Based Reasoning approach (CBR) (Kolodner, 1993)
useful as it provides a broad support for the dynamic
management of the organizational knowledge and
continuous incremental learning.
The systematic reuse and the incremental capture
of feedback may lead to process consolidation. In
this work we describe an approach for building a
reusable processes assets repository in accordance
with the organizational reality to facilitate the orga-
nizational learning and the continuous processes
improvement.
This work is organized as follows: in Section 2
the CBR technology is briefly explained. In Section
3 the process reuse using CBR is presented. In Sec-
tion 4 the tool is briefly described. Finally, consider-
ations are presented.
2 CASE-BASED REASONING
The CBR technology solves problems in a specific
situation through previous similar situations (Pal and
Shiu, 2004). A case comprises a pair problem that
describes the context of an actual case occurrence,
and solution that presents the problem solution. Past
cases are used to hint strategies to solve new similar
problems (Mille, 2006).
A CBR system is composed by 4 basic elements
(Kolodner, 1993): knowledge representation, similari-
ty measurement, adaptation and learning. In this work,
the CBR technology is used to manage the assets
repository, the organizational learning and the retriev-
al and retention of assets.
3 PROCESS REUSE APPROACH
In the proposed approach (Santos et al., 2009), the
main component is the Processes Assets Repository
(Figure 1), which is designed to store reusable process
models and their feature-value representations. The
feature-value representation involves a set of rele-
vant case properties and their values. The Search
Engine uses CBR technology to retrieve similar
223
Santos V. and Cortés M. (2010).
ORGANIZATIONAL KNOWLEDGE MANAGEMENT THROUGH SOFTWARE PROCESS REUSE AND CASE-BASED REASONING.
In Proceedings of the 12th International Conference on Enterprise Information Systems - Databases and Information Systems Integration, pages
223-228
DOI: 10.5220/0002899802230228
Copyright
c
 SciTePress

cases through the similarity measurement on the
basis of process and project features.
Figure 1: Approach for process reuse.
The reutilization involves the adaptation of a pre-
vious solution for a similar case, using an appropriate
method (Mille, 2006). After its definition and execu-
tion in the new project, the new case is evaluated in
order to examine its effectiveness and capture its new
representation. Then, the new case can be stored into
the repository, improving its capacity.
3.1 Representation of Organizational
Assets in the Repository
The similarity concept consists of establishing an
estimation of the utility of a previous case stored in
the repository against the current case (Kolodner,
1993). Table 1 presents the assets representation in
the repository grouped by process and project scopes.
Table 1: Representation of the software process assets.
Scope j Feature
Similarity
Type
Project
1 Life-Cycle Model QVI
2 Complexity QFI
3 Size QFI
4 Team Size NUM
5 Time NUM
6 Software Engineering Know-
ledge
QFI
7 Development Paradigm QVI
Process
8 Development Model QVI
9 Maturity Model QVI
10 Maturity Level QVI
11 Complexity QFI
12 Process QVI
13 Experience on Process Usage QFI
The similarity types are restrictions applied to
the feature representations, to establish its correpon-
dence or co-occurrence among case (Reis et al.,
2001). The similarity types used in this work are:
 Numeric (NUM). Positive integer or real num-
bers
 Qualitative for Fixed Items (QFI). Predefined
Terms
 Qualitative for Variable Items (QVI). Registered
terms with possibility of new items
3.2 Retrieval Process
In CBR, several techniques can be applied for data
retrieval. In (Pal and Shiu, 2004) the algorithm to
calculate the similarity is based on k-NN technique,
where the global similarity (SIM) between two cases
(a and b) is defined by the weighted sum of the local
similarities (sim
j
) for each feature (A
j
).



n
j
jjjj
bAaAsimwbaSIM
1
))(),((),(
(1)
The weight (w
j
) reflects the relevance of a fea-
ture (A
j
) concerning the similarity of cases. This
factor is determined by the user and is measured by
the values: High (100), Medium (50) and Low (10).
The features considered more important for the
problem resolution from the user’s viewpoint, pos-
sess higher weights.
The local similarity is calculated in accordance
with the similarity type of each feature. For features of
NUM and QFI similarity types, it considers the com-
putation of distance (d
j
) between each feature values
in the cases a and b, as presented in the formula (2).
),(1
1
bad
sim
j
j


(2)
This measurement must be normalized (Ricci et
al., 2002) to avoid over influence of a metric by the
great range of values of the features. The normaliza-
tion process uses smallest and greatest values in the
repository to linearly produce values between 0 and 1.
The distance between two features of NUM or QFI
similarity types are calculated on the basis of a pro-
portionality relation between the values, as expressed
below:
() min( ) () min( )
(,)
max( ) min( ) max( ) min( )
jjjj
j
jj jj
Aa A Ab A
dab
AA AA







(3)
Finally, to calculate the distance between features
of QVI similarity type, a taxonomy is used to hierar-
chically represent the relationships among the terms.
In a taxonomy, as deeper the nodes are located in the
hierarchy, greater is the similarity value. In the same
way, whenever the nodes are closer to the root of the
ICEIS 2010 - 12th International Conference on Enterprise Information Systems
224

taxonomy the similarity goes to zero.
Considering n
a
and n
b
different nodes in a
taxonomy, the similarity between those nodes,
sim
j
(n
a
, n
b
), proposed by (Wu and Palmer, 1994)
consists of:


 
rppbpa
rp
baj
nnNnnNnnN
nnN
nnsim
,2,,
,2
),(



(4)
where N(n
a
, n
p
) and N(n
b
, n
p
) are the number of edges
in the path from the corresponding nodes and their
common parent in the hierarchy, and N(n
p
, n
r
) is the
number of edges among this common parent node and
the root of the taxonomy. If the nodes n
a
and n
b
are
common, the formula (4) isn’t applicable. In this case,
the similarity between those nodes is considered equal
to 1.
3.3 Adaptation Process
Adaptation involves the process to transform the re-
trieved results into an appropriated solution for the
current problem. The adaptation process can be rea-
lized following different approaches (Pal and Shiu,
2004). In this sense two approaches can be suggested:
if the similarity measurement of the retrieved process
in the top of the ranking is satisfactory, a minimal or
null adaptation can be required. In other case, when
none of the retrieved processes fulfills the require-
ments in appropriate manner, a compositional ap-
proach can be used.
3.4 Learning Process
The learning process is done through the feedback
about the performance of the new case, when the
project is closed (Pal and Shiu, 2004). The case per-
formance evaluation consists of 3 steps detailed as
follows.
3.4.1 Global Similarity Comparison
When the project is closed, the representation for the
executed process can be different from the representa-
tion used in the recovery phase.
The comparison between both representations,
called Global Similarity Comparison (GSC), is
based on a proportionality measurement attending
the occurrences of changes in the representation
along the project execution. The measurement is
obtained on the basis of the global similarity mea-
surement (SIM), calculated according to the Section
3.2. The GSC measurement is presented in (5) and
evaluates the similarities between the selected base-
case representation (a) against the preliminary re-
presentation (b) and the representation of the ex-
ecuted process (b’).
100
),(
)',(
100 









baSIM
baSIM
GSC
(5)
When the GSC returns zero, it means that the con-
texts similarity values stay the same and the user’s
choice about the selected base-case should not be
changed and the user evaluation should contribute to
the learning process. In the other hand, if the GSC is a
value greater than zero, it means that the real context is
more similar to the selected base-case than the prelimi-
nary context, which possibly the user has match the
appropriate case to meet the project or organization
needs. Otherwise, if the GSC is a value less than zero,
it means that the real context is less similar to the se-
lected base-case than the preliminary context, and
denotes that the user’s choice was inappropriate and
several modifications were required.
3.4.2 Reuse Degree
The Reuse Degree (RD) is another evaluation metric
that attends the reuse percentage of the selected base-
case (a), against the new case after the project’s end
(b’). It is obtained by the mapping of all activities
components contained in cases a and b’, such as name,
type, artifacts, resources, roles, connections, etc., in
order to establish the reuse level. The RD formula is
presented below:
(,')
1
q
n
SimC a b
q
C
N
RD
mN




(6)
where n is the number of activities from b’ and m is
the number of activities from a. N
SimC
is a function
that returns the number of similar components of a
specific activity (q) between a and b’. To consider a
component similar, it is necessary to establish a simi-
larity threshold (90% considered). Since this metric
evaluates the level of reuse, then great variation in the
new case should result in low reuse. The N
C
represents the number of activity components con-
tained in a case and is useful to identify the level of
reuse of the selected base-case.
3.4.3 Success Level
The success level is a subjective metric fed by the user
whenever an executed process is evaluated. This me-
tric is stored as feedback information about the base-
case. This evaluation is represented by a value in the
range 0 to 10.
ORGANIZATIONAL KNOWLEDGE MANAGEMENT THROUGH SOFTWARE PROCESS REUSE AND
CASE-BASED REASONING
225

This information is useful to the future adoption of
the base-case, and contributes for the continuous im-
provement of the process, since cases with greater
success levels will be prioritized in the search engine
results.
3.5 Retention
The retention consists in the incorporation process of
what is useful in a new problem resolution (Kolodner,
1993) (Pal and Shiu, 2004). Retain continually is fun-
damental to increment the repository with new solu-
tions. In this research, that phase occurs after the eval-
uation of the executed process, in such way to extract
the knowledge for later use and to integrate cases in
the repository.
Depending on the user evaluation, the user may
choose to transform reused process in a base-case by
removing its specific project details and leaving only
the suitable information to reuse in other projects and
also store its context representation.
4 KNOWLEDGE MANAGEMENT
TOOL OF PROCESS ASSETS
A component to support the proposed approach was
implemented in the context of an existing Process-
centered Software Engineering Environment (PSEE),
called WebAPSEE PRO (QR Consult, 2009). We-
bAPSEE PRO which aims to provide automated sup-
port for software process management, including
process reuse infrastructure and functionalities. We-
bAPSEE PRO was the chosen tool to implement the
proposed approach because it has a complete meta-
model and a graphic formalism that allows to design
a variety of functionalities for the process reuse
management.
The extension proposed by this approach aims to
support the dynamic management of the organization-
al knowledge and continuous incremental learning.
This component allows the definition of the organiza-
tional assets representation, in order to retrieve the
ranking of the most similar and successful base-cases.
In addition, the tool is already support the process
adaptation and allows the evaluation of the reused
process model performance through the metrics pre-
sented in Section 3.4. In this step, a minimum Success
Level can be specified to the executed process. Final-
ly, the retention process is provided to promote the
improvement of the process interpretations in solving
new problems.
4.1 Component Specification
The proposed approach was specified by UML dia-
grams such as use cases, class diagrams and activi-
ties diagrams (Santos, 2009).
The WebAPSEE PRO state machine involves four
states. The draft state consists of the template initial
state. The defined state means standard template,
which allows template reuse. The pending state means
old template version when there is a template in draft
state. And outdated state means old template version
when there is a template in defined state.
4.1.1 Use Case Approach
Table 2: Overview of the use cases approach.
Use Case Description Status
1- Add Scope The user adds the scope used to group th
e
context representation features of th
e
software process assets. The approach pre
-
defined scopes are Process and Project, bu
t
actually one can add new scopes.
New
2- Add Feature The user adds context representation featur
e
of the software process assets.
New
3- Make Standard
Template
The user selects the desired template t
o
b
ecome standard. After that, the templat
e
changes its state from draft to define
d
(WebAPSEE PRO State Machine, Figure 2).
It is an existing functionality (Costa an
d
Sales, 2007).
Kept
4- Classify
Template
The user registers the base-case contex
t
classification (Table 1).
New
5- Retrieve
Templates
The system searches for the similar base-case
s
in the assets repository. It is an existin
g
functionality which was updated to use th
e
proposed context representation.
Updated
6- Evaluate
Closed Process
The user evaluates the process after it
s
closure.
New
6.1- Classify
Closed Process
After its closure, the user registers the rea
l
context representation.
New
6.2- Evaluate
Reuse
The system evaluates the reuse in th
e
executed process related to the selecte
d
process model.
New
6.3- Evaluate
Contexts
The system evaluates the contexts b
y
com
p
aring the preliminary and real globa
l
similarity values.
New
6.4- Evaluate
Success Level
The user evaluates the executed process b
y
providing a note in the range 1 to 10.
New
7- Retain Process The user chooses to retain the execute
d
process.
New
7.1- Generalize
Closed Process
The system generalizes the executed proces
s
and transforms it in a tem
p
late by removin
g
the details and instances (Costa and Sales
,
2007).
Kept
7.2- Create New
Template Version
The user can choose to create a new templat
e
version. It is an existing functionality (Cost
a
and Sales, 2007).
Kept
7.3- Create New
Template
The user can choose to create a new tem
p
lat
e
(Costa and Sales, 2007).
Kept
7.4- Assign
Characteristics
The system saves the provided templat
e
characteristics.
New
7.5- Assign
Success Level
The system saves the provided tem
p
lat
e
success level.
New
In order to attend the new requirements, the We-
bAPSEE PRO architecture incorporates new func-
tionalities. The main actor of the tool is the Manager
ICEIS 2010 - 12th International Conference on Enterprise Information Systems
226

Console User, which, according to the WebAPSEE
PRO architecture, executes the available services. The
new functionalities of the proposed approach and the
related pre-existing WebAPSEE PRO functionalities
are illustrates in Table 2, indicating which one is new,
updated or kept.
4.1.2 Class Diagram
According to the WebAPSEE PRO architecture, an
abstract process is represented by the class Template,
and concrete process by the class Process. The
Project and ProcessModel classes represent the
project data and the process model, respectively.
Figure 2 presents the class diagram to support the
proposed approach.
In the Template class was included a new
attribute, called sucessLevel, which aims to store the
user assigned evaluation grade for the template. This
evaluation is later used in the calculation of the tem-
plate success level average in the instantiated
processes.
The new classes were created (in gray) and
grouped in the package Reuse. The SimilarityType
class represents the similarity types of the Section 3.1.
The Feature class represents the features of the Table
1.
class Classes Diagram
Reus e
Project
- id: int
- nam e: ch a r
- begin_date: date
- end_date: date
+ Project() : void
Process
- id: char
- pState: c har
+ Process() : void
ProcessModel
- pmState: char
- requirements: char
- staticOk: boolean
+ ProcessModel() : void
Template
- id: char
- templateState: char
- successLevel: double[]
+ T e m pl ate() : vo id
SimilarityType
- id: char
- description: char
+ SimilarityType() : void
Featur e
- id: char
- name: char
- description: char
+ Feature() : void
FeatureValue
- id: char
- value: char
ProcessInstantiation
- globalSim: double
AssignedFeatureValue
- va l u e: ch a r
SearchFeatureValue
- value: char
- localSim: double
Conte x t
- id: char
- nam e: ch a r
- description: char
has
-super
0..1
hierarchy
-su bs *
10..*
*
1
* *
*
AssignedFeatureValue
*
*
ProcessInstantiation
*
-hasProcessModel
1
-belongsTo
1
ref e r s
1
Figure 2: The proposed approach class diagram.
The FeatureValue class represents the possible
values for the features. The AssignedFeatureValue
associative class represents the registered values for
the features to a specific template. The ProcessInstat-
iation associative class represents the global similarity
value from the selected base-case against the new
case. The SearchFeatureValue associative class
represents each feature value of the new case and its
local similarity against the base-case.
4.2 The Component Prototype
On the basis of the previous sections, the prototype of
the component was implemented. In this section, the
most relevant screenshots are illustrated to allow the
visualization of the previous specification. The tool
was previously fed with the contexts, the similarity
types, the features and its values (based on the Section
3.1).
Each template in the repository should become
standard, state change from draft to defined, to pro-
ceed the characterization by assigning its feature val-
ues to be used in the search engine (Figure 3).
Figure 3: Template characterization and overview.
Figure 4: Ranking of the templates matching.
ORGANIZATIONAL KNOWLEDGE MANAGEMENT THROUGH SOFTWARE PROCESS REUSE AND
CASE-BASED REASONING
227

The search engine, using the retrieval process, re-
turns a ranking of similar base-cases matching the
new case (Figure 4). With the ranking of similar tem-
plates, the user can select and instantiate a base-case,
adapting the new case to the organizational/project
needs.
At the end of the process execution, the user may
evaluate the performance of the new case and choose if
it is worth to retain displayed in Figure 5.
Figure 5: Process performance evaluation and retention.
When the user chooses to retain the new case, the
repository is incremented with new context represen-
tations and new success level, improving the future
search engine results to the organization (Figure 6).
Figure 6: Search engine results after retention.
Finally, depending on the requirements provided
to the search engine and the success level filter, the
most similar and successful base-cases will be priori-
tized, promoting the reuse of successful experiences
and the process continuous improvement in the organ-
ization.
5 FINAL CONSIDERATIONS
The proposed approach is based on CBR and pro-
motes the reutilization of process assets as a start
point for the elaboration of a standard process to meet
the organizational needs. The cases are classified
according to a set of relevant features to allow an
efficient normalized retrieval. To ensure the learning
process, it provides a case evaluation at the project’s
end. After that, the organization may decide the pur-
pose of the new case.
This management tool allows the construction of
the dynamic organizational knowledge and foresees
the continuous improvement of the process through
the permanent feedback to the repository involving
the incorporation of its successes and failures. The
learning capability of CBR systems contribute to the
adoption of better and more efficient solutions.
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