SEKE 2012 Proceedings - Knowledge Systems Institute

Step 1: To start the process, the user (requirements
engineer, requirements analyst, project manager, developer,
etc.) enters basic information about the new problem. Figure 1
shows the UML class diagram of the representation of a
problem.
Step 2: In the “CBR Component” component, the data of
the new problem are compared with the Case Base. The
Attribute-Value pairs of the problem object are compared with
the various cases existing in the Case Base, aimed at an initial
selection of similar cases. The similarity between two cases c 1
and c 2 is calculated by the formula below, based on
Wangehheim [7, p.112], which defines the similarity by the
weighted average of the similarity between the values of each
index of c 1 and c 2 :
Sim A (c 1 ,c 2 ) = n (Vs i x P i ) (1)
P i
Where,
• n: number of discriminatory attributes of a case
(indexes)
• Vs i : similarity between the values of index i in c 1 and
c 2 , which is attributed per parameter.
• P i : weight of index i, attributed per parameter.
For each index i, must be set a weight P i , which defines the
importance of the index in the calculation of similarity between
cases, and a value of similarity Vs i between the possible values
of each index. In this work, in order to define the values of
similarity between the possible values of each index, specialists
in engineering requirements of a company from the federal
government of Brazil analyzed the level of relationship or
dependence between each value. For example, in Table 1 we
present the similarity values (Vs) defined by specialists for
index Artifact: the artifact “Use Case” is related – from the
highest to lowest level –to the artifacts “Use Case” (100%),
“Business Rule” (80%), “Functional Requirements” (70%),
“Non-Functional Requirements” (30%), “Traceability Matrix”
(30%) and “Technology” (0%). It is important to point out that
these values were used in the evaluation of this proposed
approach, but may be customized for each company or
situation of use.
TABLE 1. SIMILARITY BETWEEN THE POSSIBLE VALUES OF THE INDEX
ARTIFACT.
CU = Case of Use | RN = Business Rule |
RF = Functional Requirements|
RNF = Non-Functional Requirements|
DV = Vision Document | MR = Traceability Matrix
In this work, the indexes are artifact, causer and cause.
These were chosen because they are more discriminatory of a
case. After all, a cutoff value must be applied to select similar
cases. To illustrate the calculation of similarity, let us suppose
the following values for the indexes of a new problem (case
c 1 ): Causer = “Organization” (OR); Artifact = “Business Rule”
(RN); Cause = “Wrong Business Rule Specified” (RNEE).
Considering the following values for a case c 2 , existing in the
Case Base: Causer = “Process” (PR); Artifact = “Traceability
Matrix” (MT); Cause = “Wrong Traceability Matrix Specified”
(MREE). Applying the values in formula (1), we have the
following:
Sim A(c 1,c 2) = (VsOR.PR x 1) + (VsRN.MR x 3) + (VsRNEE.MREE x 5)
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Sim A(c 1,c 2) = (0.5 x 1) + (0.3 x 3) + (0 x 5) = 0,15
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Step 3: If no similar case is returned in step 2, the input
case is a new case and will be stored in the Case Base.
Step 4: In this step, the Cases Initially Similar are refined
through using NLP techniques. Actually, the “NLP
Component” defines the cases for reuse based on the number of
words in common between the textual description of the input
problem and the description of the pre-selected cases in step 2.
For this, the Vector Space Model technique and the
morphosyntactic analysis are applied to the texts in question. In
detail, the frequency of each word in the “Noun” and “Verb”
word classes, contained in the texts is calculated and those
cases with m (defined by parameter) words in common with the
input case are defined for reuse. The word classes “Noun” and
“Verb” were chosen because they express things and actions,
respectively, and therefore are the words that matter most to the
semantic value of the text [8]. For example, Figure 3 shows the
morphosyntactic analysis [9] of the descriptive text of one of
the cases from the Case Base (pre-selected in step 2).
Considering the description of the input problem “Search
functionality defined in wrong fields.” [Funcionalidade de
pesquisa definida com campos errados], the words (nouns and
verbs in Portuguese) of the input case are {funcionalidade,
pesquisa, campo}, all with a frequency of 1. In the pre-selected
case (Figure 3) the words are {campo, pesquisa}, all with a
frequency of 1. Two words were identified as being in common
between the cases.
FIGURE 3. MORPHOSYNTACTIC ANALYSIS OF THE DESCRIPTION OF A PRE-
SELECTED CASE.
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