Query expansion using novel use case scenario relationship for finding feature location

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 5, October 2023, pp. 5501~5516
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i5.pp5501-5516  5501
Journal homepage: http://ijece.iaescore.com
Query expansion using novel use case scenario relationship for
finding feature location
Achmad Arwan1,2
, Siti Rochimah1
, Chastine Fatichah1
1
Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
2
Department of Informatics, Faculty of Computer Science, Brawijaya University, Malang, Indonesia
Article Info ABSTRACT
Article history:
Received Jan 30, 2023
Revised Apr 18, 2023
Accepted Apr 24, 2023
Feature location is a technique for determining source code that implements
specific features in software. It developed to help minimize effort on
program comprehension. The main challenge of feature location research is
how to bridge the gap between abstract keywords in use cases and detail in
source code. The use case scenarios are software requirements artifacts that
state the input, logic, rules, actor, and output of a function in the software.
The sentence on use case scenario is sometimes described another sentence
in other use case scenario. This study contributes to creating expansion
queries in feature locations by finding the relationship between use case
scenarios. The relationships include inner association, outer association and
intratoken association. The research employs latent Dirichlet allocation
(LDA) to create model topics on source code. Query expansion using inner,
outer and intratoken was tested for finding feature locations on a Java-based
open-source project. The best precision rate was 50%. The best recall was
100%, which was found in several use case scenarios implemented in a few
files. The best average precision rate was 16.7%, which was found in inner
association experiments. The best average recall rate was 68.3%, which was
found in all compound association experiments.
Keywords:
Feature location
Latent Dirichlet allocation
Query expansion
Topic model
Use case scenario relationship
This is an open access article under the CC BY-SA license.
Corresponding Author:
Siti Rochimah
Department of Informatics, Institut Teknologi Sepuluh Nopember
Teknik Kimia Street, Surabaya, East Java 60117, Indonesia
Email: siti@if.its.ac.id
1. INTRODUCTION
Feature location is a technique for tracing a function of software into specific sector of source code.
The feature location is useful when a developer wants to fix, change or improve a method in a code. Changes
to code appear to be easy if the amount of code is small or the programmer who wants to fix it is the same
person, which means he or she already understands the variables, parameters, and logic of the code.
However, the changing the code will be difficult when the source code of the project is large or the
programmer who makes the changes is a different person than the creator who has never even worked on the
code. For this reason, programmers need to perform program comprehension first, and studies show that
programmers need approximately 21.5 hours a week (58% of 37.5 hours per week) [1].
There was a big gap between the usage of tokens on software requirement and the tokens of source
code. It is greatest challenge in feature location research. The tokens in the software requirements level use
abstract words such as billing, enter personal health records, and view prescriptions (health record domain). In
contrast, the tokens in the source code are technical or specific on how logic works on source code (e.g.,
AddPHAAction, personnelDAO, and setPassword). Based on this fact, we need some techniques to create a pair
among of tokens in the requirements and source code.

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Survey research on feature locations was previously carried out by Razzaq et al. [2]. Razzaq et al. [2]
collected many papers (170 papers) related to it. The research uncovered various of techniques, measurements,
datasets. As a result, most research using technique of information retrieval from software repositories.
The software repositories were identifier, comments, variables, parameters, and methods [3]–[5]. The
algorithms were varied, such as latent semantic indexing (LSI) [4], latent Dirichlet allocation (LDA) [4], term
frequency-inverse document frequency (TFIDF) [5], vector space model (VSM) [6] and some other deep
learning [3].
The next feature location research was performed by using text/IR processing to research how to
standardize feature locations [4]. It proposes how to standardize the techniques (eight techniques) of comparing
on feature location by experiment in an empirical design. The eight techniques include LDA-Gensim, LDA-R,
LDA-Gibbs, LSI-Gensim, LSI-MATLAB, VSM-Lucene, VSM-MATLAB, and VSM-TraceLab. These
methods were evaluated in twelve case studies to measure the performance. A few arrangements use the
recommendations several other studies. The results show that different underlying techniques perform variously
and that VSM-Lucene and LSI-MATLAB perform superior among other techniques.
The next feature location research was done by applying weighting to structure of source code using
LDA [7]. The study proposes an approach to add extra-information to the class (method, comments, and
variables). It also set with many LDA configurations to define the extent to which set or weight influence the
accuracy of feature locations. The result was comments weight and naming of method could gain better
accuracy, while variables and parameter gain less accurate.
Another study was conducted by creating query repair automation [8]. The research aims to determine
how to improve user queries in finding feature locations. They argue that if the user cannot provide the correct
query, then the machine cannot recommend the correct answer. The techniques used were encoding and using
genetic algorithms to automate query repair. The data used are also specific, namely, the rail system in Spain,
which does not use the unified modelling language (UML) model. The results show that the level of accuracy is
still not good, namely, 14% precision, and 37% recall.
The next research was done by using a structural approach to locate the bug [9]. The research seeks
bug locations by using indexed classes, methods, variables, and comments. The structure was employed in the
study to sum the matching results between the tokens in the query and the tokens in the class, method, and
variable categories. The research also found that the tokens in the variable names have a great match with the
tokens in the bug. The result of this research is that the accuracy of the location of the code associated with the
bug is 60%.
Another study was conducted to determine how and when structural information could help find
traceability links [10]. The structural information includes function calls, inheritance, or realization
relationships on source code. The goal of the research was how to find the initial link point and continue to
seek for links until there are no more points. The results of this study showed an accuracy rate of 58%.
The next research was intended to determine feature facets within the software [11]. It exposing the
new approach by employing pull request, commits, analyzing logs, and systematic code reviews. The natural
language processing was done to tackle the problem on pull request analysis. The logs were help to
identifying the new feature from the pull request and commit and very helpful as entry point for several
features. The results were they were able to use some parameters such as the commit messages, commits
author, pull request, release log as information sources to determine the feature facets.
The next technique was comparing several search strategies using model to find feature location
[12]. The idea to find something within abstraction layer such as model was easier rather than find something
from source codes. It comparing several search algorithms such evolutionary algorithm, random search,
hill-climbing, iterated local search, and the hybrid of evolutionary algorithm and hill-climbing (EHC) to
determine what algorithm was the best amongst all. The EHC was the best search-based strategy which
reach precision 81% and recall 83%. The limitation of research was the dataset originated from industrial
based and the models were not common (induction hob domain-specific language) which mean cannot
replicate easily.
The recent research was how to know the effect of feature characteristic against the performance of
feature location [13]. The study introduced several new metric characteristics such as relative feature size
(RFS), tangled elements (TE), crosscutting in features (CIF), unique lexical coverage (ULC), lexical
saturation (LS) and query size (QS) to evaluate performance of feature location. The evaluation measurement
was precision, recall, mean average precision (MAP), mean reciprocal rank (MRR). As a results, the RFS
was the main regressor of precision and MAP using several algorithms such Page rank, LDA, LSI and VSM.
An ULC was the main regressor of recall using several algorithms such Page rank, LDA, LSI and VSM. The
CIF was the main regressor to MRR several algorithms such Page rank, LDA, LSI and VSM.
The use case scenarios are software requirement documents that explain the input data, logic, rules,
actor, and output data of a function [14]. The programmer implements the source codes based on the

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description of the use case scenario; therefore, querying specific area of feature could be done using the use
case scenario. However, the number of research that use the use case scenario as a query is quite rare, which
is caused by the minimum of datasets that include use case scenarios in the projects. Our previous research
[15] was performed by applying NLP (noun tagging and verb tagging) to use a case scenario and used it as a
query for information retrieval. It could predict the feature location with an average precision of 11% and a
recall of 4%. Another our previous research also used clustered use case scenarios as query expansion to find
feature location [16]. The results were quite good on recall rate (56%).
The story on use case scenario is sometimes described another sentence in other use case scenario. Or
it needs more explanation which could be found on other use case scenario. Based on the facts on iTrust data
[17], the usage of use case scenario relationships may be advantageous in finding feature locations.
This study contributes to creating a novel method for feature location by making expansion queries
in feature locations by finding the relationship between use case scenarios. The inner association, outer
association and intratoken association were the original ideas to capture additional tokens from other use case
scenarios. As a result, the query becomes more numerous and could increase precision and recall rate of
feature locations. The expanded use case scenarios were used as queries for information retrieval based on
topic modeling of source code.
2. BACKGROUND
In this section, we will provide a brief introduction to information retrieval, topic modeling, and query
expansion. Additionally, we will elaborate on our approach of the use case scenario relationship model. The
final, we also elaborate how to implement it in a feature location case study.
2.1. Information retrieval
Information retrieval is a prevalent technique in feature location. It composed by a number of
processes, including preprocessing and NLP, and creates a group of token into a corpus [18]. Then, users could
prompt some queries. The token from query was compared against the token with specific similarity methods
such as cosine and Jaccard to gain the high-rank of precision and recall. To ensure the validity results of
information retrieval methods, we employed precision and recall as a common techniques for many researchers
[8], [19].
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =
𝑖𝑡𝑒𝑚𝑠 𝑟𝑒𝑙𝑒𝑣𝑎𝑛𝑡 ∩ 𝑖𝑡𝑒𝑚𝑠 𝑟𝑒𝑡𝑟𝑖𝑒𝑣𝑒𝑑
𝑖𝑡𝑒𝑚𝑠 𝑟𝑒𝑡𝑟𝑖𝑒𝑣𝑒𝑑
(1)
𝑅𝑒𝑐𝑎𝑙𝑙 =
𝑖𝑡𝑒𝑚𝑠 𝑟𝑒𝑙𝑒𝑣𝑎𝑛𝑡 ∩ 𝑖𝑡𝑒𝑚𝑠 𝑟𝑒𝑡𝑟𝑖𝑒𝑣𝑒𝑑
𝑖𝑡𝑒𝑚𝑠 𝑟𝑒𝑙𝑒𝑣𝑎𝑛𝑡
(2)
Items relevant is the amount of files that were attached to an individual feature, whereas the items retrieved
were the number of files that were recommended in this research.
2.2. LDA
LDA [20] is an unsupervised probabilistic procedure to determine the topic distribution on a corpus.
The corpus is extracted from documents (e.g., Source code), which consist of tokens. Each document was given
the probabilistic distribution to determine the topic proportion.
LDA inputs are the documents (𝐷), the number of topics (𝐾), and a set of hyperparameters. The
hyperparameters are:
− 𝑘 is the amount of topics that must be generated from the data,
− 𝛼 is the influence on the topic distributions per document. A lower 𝛼 value results in fewer topics per
document,
− 𝛽 is the affects of the distribution of terms per topic, Lower 𝛽 value results in fewer terms per topic, as a
result need to increase in the number of topics.
2.3. Query expansion
Query expansion is the method of adding the original query with additional words, which could help to
obtain actual user intent [21]. Query expansion has been applied in many applications, such as question
answering, multimedia information retrieval, information filtering, and cross-language information retrieval.
Query expansion was the crucial part of this research by utilizing the use case relationship as an additional word
to help the system understand the actual user intent.

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2.4. Use case scenario relationship model
Use case scenarios are sets of sentences that describe the step, data input, logic and sometimes data
output of a feature in software. It is used by developers as a key to develop the specific feature of software. The
terms that are used are usually high-level language. The term in the use case scenario is sometimes repeated and
has the same meaning from the software engineer’s perspective.
The use case relationship model is our original and novel approach to capture the indirect query from
the user. This model build intended to enrich the query with additional words from other use case scenarios that
were associated. Based on observations from the iTrust [17] project, the use case scenario has a relationship
with other use case scenarios. This section will elaborate on the concepts and characteristics of use case
relationships that might exist.
2.4.1. Inner association
The first concept was an inner association. An inner association is the kind of association that
describes a use case scenario associated with another use case scenario with the same actor, but it is used as an
alternative or exception. This concept was found based of our deep inspection on use case scenarios, which
some use case scenarios were the alternative of others. The example of inner association were shown on
Figures 1 to 3 (e.g., [E1] [E2] [S3]). The UCS 10 Scenario 1 have two alternative or exception on UCS 10 Error
2 and Error 1. The logical reason was the code of error exception of a function should be related with the main
code of function.
Figure 1. UCS 10 scenario 1
Figure 2. UCS 10 error 2
Figure 3. UCS 10 error 1
2.4.2. Outer association
The second concept was an outer association an outer association was the kind of association that
described a use case scenario associated with other use case scenarios with different actors, but it was used as a
reference. This concept was found after we inspect on the use case scenario which has specific tags such as
“(UC26)” on it. The iTrust software analyst might want to give the mark that show the use case have reference
with others. Based on data, as shown in Figures 4 and 5, the use case scenario 11 mentioned use case 26, which
is marked with “(UC26)”.

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Figure 4. Use case scenario 11
2.4.3. Intratoken association
An intratoken association is the kind of association in which a token of a use case scenario has a
relationship with semantic similarity meaning. For example, the keyword “Patient” might have semantic
similarity with the keyword “blood pressure” (score 0.3750). Based on the dataset, UC10 S1 as shown in
Figure 1 has a relationship with UC9S2 as shown in Figure 6.
3. RESEARCH METHOD
This section explains how our research framework finds the feature location based on query
expansion using the use case relationship. There were five segments of our framework: dataset definition,
modeling the topics of source codes, use case scenario relationship modeling, query expansion using use case
relationships, and the evaluation process. All the structure shown on the Figure 7. The details will be
explained in this section.
3.1. Dataset definition
The dataset we used was iTrust [17]. An iTrust is a Java-based Electronic Health Record system that
was developed at North Caroline State University (NCSU) as a primary case study in a software engineering
class. The version was version 19 (https://github.com/ncsu-csc326/iTrust/tree/v19/iTrust). The dataset contains
approximately forty use cases mapped into 478 trace links of health record features such as personal health
records, patients, diseases, safe drugs, visits, and lab procedures. The iTrust projects are equipped with complete
data such as a use case, use case scenario, and codes. It also has a traceability link, which function as ground
truth. It contains many files of source code (354 files). It also used by many researchers [22], [23].
The dataset was filtered into 20 use case scenarios that could be categorized into a seven kinds of use
case scenarios Table 1. The selection of seven features was done based on the assumption that those features
were the most common electronic health record. After the selection, the inner association, outer association and
intratoken association of the use case scenario were defined manually by the researcher which described in
detail on section 3.3. The trace links chosen were reduced into 102 trace links related to those 20. Source codes
were also reduced into only 68 files since many trace links used the same files.

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Figure 7. Research methodology
Table 1. Use case dataset
No Use case name Use case description
1 UC10E1.TXT ENTER/EDIT PERSONAL HEALTH RECORDS
2 UC10E2.TXT ENTER/EDIT PERSONAL HEALTH RECORDS
3 UC10S1.TXT ENTER/EDIT PERSONAL HEALTH RECORDS
4 UC10S2.TXT ENTER/EDIT PERSONAL HEALTH RECORDS
5 UC16.TXT IDENTIFY RISK OF CHRONIC DISEASES
6 UC1E1.TXT CREATE AND DISABLE PATIENTS
7 UC1S1.TXT CREATE AND DISABLE PATIENTS
8 UC1S2.TXT CREATE AND DISABLE PATIENTS
9 UC23S1.TXT COMPREHENSIVE PATIENT REPORTS
12 UC26S1.TXT VIEW/EDIT LAB PROCEDURE STATUS
16 UC28.TXT VIEW PATIENTS
17 UC9S1.TXT VIEW RECORDS
18 UC9S2.TXT VIEW RECORDS
19 UC11S1.TXT DOCUMENT OFFICE VISIT
20 UC11S2.TXT DOCUMENT OFFICE VISIT
3.2. Modelling the topics of source codes
The source codes from the selected dataset were preprocessed using several subprocesses, such as
tokenizing, stop word elimination, stemming and modeling their topics. The first subprocess was tokenizing,
which was the process of splitting the source code into tokens. The methods include punctuations removal
(.,’-_) using regex and split method/variable name which has camelCase format into token (e.g.,
“updateAllergies split into update allergies”).
4
1
Dataset
Start
Source Code
Preprocessing
(tokenization,
split identifier
stopword
removal,
stemming)
Topic
proportion per
documents
Topic
Modeling
2
Cosine
Similarity
Use case Scenario
Preprocessing
(tokenization,
stopword removal,
stemming)
Use case Relationship
Definition
Use Case
Relations Pair
3
Inference
Topic
Topic
Proportion
Query Preprocessing
(tokenization,
stopword removal,
stemming)
Ranked List of
Relevant Source
Code
Precision Recall
Measurement
Result Analysis
End
Query Expansion
Formulation
(3 sets)
5

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The next subprocess was stop word elimination and stemming [24]. To eliminate a stop word on the
source codes, we picked the tokens that were too common English (i.e., “is, the, and”). The stemming was the
subprocess that eliminated suffixes or prefixes of the source codes. It intended to determine the root form of the
word. The stemming was done by using the porter algorithm.
The last subprocess was modeling the topics of source codes that had already been preprocessed. The
research use a Mallet [25] which implement LDA to make model topics. The topic parameters variable of LDA
were set to 5 topics that correlated with 7 kinds of use case descriptions. The iteration number parameter was set
to 4,000. It produced several files, a model of topics, an inference file and topic proportion of files (68 files). It
also produces keywords per topic that are used further as translator tokens. The files contain of topic proportion
which used as a ranking recommendation in process of query comparison. As a result, Mallet created five topics
with the top 15 keywords, as shown in Table 2.
Table 2. Top 15 keywords per topic
TOTAL TOPICS: 5 TOPIC 0 WORD & FRQ. TOPIC 1 WORD & FRQ. TOPIC 2 WORD & FRQ. TOPIC 3 WORD & FRQ.
0 long 4:50 2:14 1:6 0:6 patient 86 visit 66 mid 45 factor 67
1 add 3:22 0:18 2:6 1:3 bean 85 offic 57 user 20 risk 43
2 patient 0:86 4:78 3:28 famili 50 record 38 type 18 patient 28
3 bean 0:85 4:61 1:19 mid 41 form 32 log 16 add 22
4 form 1:32 2:2 pid 38 health 25 role 15 disea 19
5 valid 1:23 2:3 trust 36 ov 25 transact 15 checker 10
6 trust 0:36 1:18 2:6 member 36 valid 23 long 14 mid 7
7 mid 4:64 2:45 0:41 parent 34 id 23 password 14 factori 7
8 dao 0:29 1:12 2:5 allergi 31 bean 19 patient 12 health 6
9 empti 2:3 dao 29 trust 18 set 10 record 6
10 set 4:61 2:10 1:10 log 23 patient 17 db 10 current 6
11 pwd 2:2 add 18 log 15 param 9 ethnic 4
12 auth 2:3 fam 17 report 13 pstmt 9 arrai 4
13 user 2:20 grandpar 15 request 13 case 8 american 4
14 role 2:15 prescript 13 pid 13 factori 7 histori 3
15 random 2:4 db 11 dao 12 ad 7 famili 3
3.3. Use case scenario relationship modelling
The use case scenarios relationship modelling process were the most crucial parts of this research. The
use case scenario has a relationship with others through our concept of inner association, outer association and
intratoken associations. To implement the concept, we created a relational database to record the use case
scenario associations Figure 8. The entities were use cases and tokens. The use cases were the entities to use to
save the use case scenario data, while tokens entity was used to save the key/terms from use case scenario for
intratoken association. The use cases have both inner and outer relations.
The inner relation and outer relation association were defined by inspecting the use case scenarios one
by one manually. The inspection includes find specific tags on the use case scenarios. The tags [E1] represent
the inner relation, while tag (UCxx) represents the outer relation, as shown in Figures 1 to 5. The pairs of use
cases were saved into a table in the database as illustrates in Figures 9 and 10.
Figure 8. E-R model of use case relationship models

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Figure 9. Inner association of use case scenarios Figure 10. Outer association of use case scenarios
To define intratoken association, we perform several subprocesses. The first was the extraction of
meaningful tokens from use case scenarios. The meaningful tokens were extracted using Post Tagger [26]
(https://parts-of-speech.info) as illustrated in Figure 11. PostTagger [27] needs a complete sentence to determine
the tag of words. Therefore, in this case, we used unpreprocessed use case scenarios to extract correct tags.
Noun and verb only words were used as token association candidates since it could help reduce the number of
words and increase the success rate [15], [16], [28]. As a result, meaningful tokens were saved on tables to be
processed further Figure 12.
Figure 11. Determine meaningful tokens using pos tag
Figure 12. Samples of meaningful tokens
The second subprocess of intratoken association was created a matrix of word-based semantic
similarity [29] to facilitate the intratoken association easily. All the words were compared one by one and
calculated based on their semantic similarity. The words and similarity degree were saved to a table named the
matrix of semantic similarity tokens. All similar words are used for expansion, which saves fields named
intratoken association.

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The results of the use case scenario relationship were the data of the inner association of the use case
scenario, outer association of the use case scenario, and intra token association of the use case scenario. All
association have saved on database to make experiment easier to do. Each relationship tested one by one for
their performance of precision and recall.
3.4. Query expansion sets
The words in the use case scenario were used as the initial query to our information retrieval approach.
All words were preprocessed include tokenization, elimination of stop word and stemming. These processes
were intended to ensure that the remaining tokens were meaningful and in the root form of words.
The first subprocess was query expansion using the first step of our novel use case scenario
relationship called the inner relation association. It was done by finding the pairs of use case scenarios that
comply with the rule given in section 2.4.1. For example, the query given was use case scenario 10
(UC10S1.txt). Based on the inner relation association pair in Figure 9, UC10S1 had pairs with both UC10E1
and UC10E2, so their tokens were included as query expansions of UC10S1. Figure 13 illustrated that the step
to obtain the tokens was performed by applying a join query to produce the token pair of inner relation
associations.
Figure 13. The inner relation pairs of UC10S1
The second subprocess was query expansion using the second step of our novel use case scenario
relationship called the outer relation association. It was done by finding the pairs of use case scenarios that
comply with the rule given in section 2.4.2. For example, the query given was use case scenario 11 (UC11S1.txt).
Based on the outer relation association pair Figure 10, UC11S1 had pairs with UC26S1, UC15S1, and UC33S1, so
their tokens were included as query expansions of UC11S1. Figure 13 illustrated that the step to obtain the
tokens was performed by applying a join query to produce the tokens pair of outer relation association.
Figure 14. Outer relation pairs of UC11S1
The third subprocess was query expansion using the third step of our novel use case scenario
relationship called the intra token association. It was done by finding the pairs of semantically similar tokens of
use case scenarios that comply with the rule given in section 2.4.3. The tokens are extracted and put on the
matrix of word-based semantic similarity. For example, the query given was use case scenario 10 (UC10S1.txt).
Each token is compared against all tokens from the matrix similarity. Similar tokens with degree > 0.5 are used
for query expansion. The step to obtain the tokens was performed by applying a join query to produce the token
pair of intratoken associations, as illustrated in Figure 15. The Latent Dirichlet Allocation algorithm defines the
topics unsupervised by iterating to give topics to both documents and tokens. At the end, the documents and the
tokens are assigned to specific topics.
3.5. Evaluation process
The final process where the result evaluation and analysis process. The recommendation of source
codes was generated by comparing topic proportion of query expansion against the topic proportion of all
source code files. Each topic from the query was calculated to measure the Euclidian distance with the topic
of each file using cosine similarity. The ranking of recommendation presented by sorting the cosine similarity
the nearest to furthest. The threshold of similarity was set to 0.3. Precision and recall were employed to
determine the success rate of our methods.

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Figure 15. The intratoken association of UC10S1.txt
4. RESULTS AND DISCUSSION
The experiments were performed in several sets. The first experiment was queried using a use case
scenario without expansion as baseline experiments. The second experiment used query expansion of the inner
association relationship, which means that the use case scenario concatenates with the inner association token.
The third was the experiment using query expansion of the outer association relationship, which means that the
use case scenario concatenates with the outer association token. The fourth was the experiment using query
expansion of the intratoken association relationship, which means that the use case scenario concatenates with
the intratoken association relationship. The final experiment used the query compound of all elements, the use
case scenarios, inner association token, outer association token, and intratoken association. This section
discusses the details of the experiments.
4.1. Experiment without query expansion
The first experiment was performed by using all word from a use case scenario as the query without
query expansion. It used as the baseline of the testing. The words were preprocessed using tokenize, stop word
elimination, and stemming using the porter algorithm. The rest of the words are then put into the query of the
research. The result is depicted in Table 3.
Table 3. Experiment results without query expansion
Query Items Retrieved Items relevant & items retrieved Items relevant Recall Precision
UC1S1 9 3 5 60.0% 33.3%
UC9S1 55 8 8 100.0% 14.5%
UC9S2 12 3 3 100.0% 25.0%
UC10S1 19 7 15 46.7% 36.8%
UC10S2 18 0 1 0.0% 0.0%
UC10E1 29 0 2 0.0% 0.0%
UC10E2 37 0 2 0.0% 0.0%
UC11S1 56 6 6 100.0% 10.7%
UC11S2 21 5 6 83.3% 23.8%
UC16 12 6 8 75.0% 50.0%
UC23S1 43 2 2 100.0% 4.7%
UC23S3 30 12 26 46.2% 40.0%
UC23S4 30 1 2 50.0% 3.3%
UC26S1 34 2 2 100.0% 5.9%
UC26S2 40 3 6 50.0% 7.5%
UC26S3 43 2 4 50.0% 4.7%
UC26S4 37 1 1 100.0% 2.7%
UC28 16 1 3 33.3% 6.3%
(Sum) 541 Sum (62) (Sum) 102 (Avg) 60.8% (Avg) 15.0%
The total number of items retrieved was 541 documents, and the total number of items relevant and
retrieved was 62 documents of 102 documents relevant. The average recall was 60.8%, which means that the
baseline approach could provide 60 documents out of 100 correct documents. The average precision was 15%,
which means it could recommend 15 documents of 100 documents.
The best recall was 100%, which comes from several use case scenarios (UC9S1, UC9S2, UC11S1,
UC23S1, UC26S1, UC26S4). The reason was those files used many words which quite technical, e.g.,
“immunization, diagnoses, and office visit.”, which could also be found on the source codes as implemented
in the field of persistent files such as databases/tables.

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The worst recall was 0, which appeared on UC10S2 and UC10E2. The reason was that it was
implemented in few files of source code, and as a result, the item relevance became limited (1 & 2 documents
only). The words on the use case scenario UC10S2 were “HCP choose height weight graph. presented chart
chosen measurements patient spanning 3 calendar years data, averaged quarters (January-March, April-June,
July September, October-December)”. It is quite specific and directed and might not be shared among use
case scenarios.
The best precision was 50%, which came from UC16. It was also the most ideal since the recall was
quite superior (75%). The reason was that the sentences of UC16 contain balanced words on both abstract
and detail (technical) topics. Another reason was that UC16 was implemented in some files (8); as a result,
the number of relevant items became 8 documents. The words on UC16 were “Personal Health Records LHCP
chooses chronic disease patient. data database analyzed risk factors disease determine exhibits risk factor. Risk
factors for chronic diseases included diabetes type 1 and type 2 heart disease. chosen patient satisfies
preconditions chosen chronic disease, the LHCP warning message patient exhibits risk factors. message display
risk factors patients exhibit”. It contains many words (e.g., personal health records, disease, patient, diabetes,
type 1, type 2, and heart disease). shared among use case scenarios, which is why it could obtain the best results.
4.2. Experiment using query expansion based on the inner use case relationship
The second experiment was a query using token of use case scenario with expansion from the inner use
case relationship. It was preprocessed using tokenize, stop word elimination, and stemming using the porter
algorithm. The preprocessed tokens originating from the use case scenario were concatenated with additional
tokens from the inner use case scenario to build a query for information retrieval. The result is depicted in Table 4.
Table 4. Experiment results using inner relationships as query expansion
UC1S1 4 2 5 40.0% 50.0%
UC9S1 14 2 8 25.0% 14.3%
UC9S2 12 3 3 100.0% 25.0%
UC10S1 39 12 15 80.0% 30.8%
UC10S2 18 0 1 0.0% 0.0%
UC10E1 31 2 2 100.0% 6.5%
UC10E2 37 0 2 0.0% 0.0%
UC11S1 21 5 6 83.3% 23.8%
UC11S2 13 3 6 50.0% 23.1%
UC16 12 6 8 75.0% 50.0%
UC23S1 23 2 2 100.0% 8.7%
UC23S3 29 11 26 42.3% 37.9%
UC23S4 28 1 2 50.0% 3.6%
UC26S1 33 2 2 100.0% 6.1%
UC26S2 51 3 6 50.0% 5.9%
UC26S3 44 2 4 50.0% 4.5%
UC26S4 37 1 1 100.0% 2.7%
UC28 12 1 3 33.3% 8.3%
(Sum) 458 (Sum) 58 (Sum) 102 (Avg) 59.9% (Avg) 16.7%
The amount of items retrieved was reduced to 458 documents, and the amount of items relevant and
retrieved was also decreased to 58 documents of 102 documents relevant. The average recall was 59.9%, which
means that the baseline approach could provide 60 documents out of 100 correct documents. The average number
of precisions was increased to 16,7%, which means it could recommend 17 documents of 100 documents.
The best recall was 100%, which comes from several use case scenarios (UC9S2, UC10E1, UC23S1,
UC26S1, UC26S4). The reason was about the same, which those files used many words which quite technical,
e.g., “immunization, diagnoses, and office visit”, which could also be found on the source codes as implemented
in the field of persistent files such as databases/tables.
The worst recall was also the same as 0, which appeared on UC10S2 and UC10E2. The reason was
about the same, which it implemented in few files of source code, and as a result, the item relevance becomes
limited (1 & 2 documents only). The words on the use case scenario UC10S2 were “HCP choose height weight
graph. presented chart chosen measurements patient spanning 3 calendar years data, averaged quarters (January-
March, April-June, July September, October-December)”. It is quite specific and directed and might not be
shared among use case scenarios.
The best precision was 50%, which came from UC16 and UC1S1. The reason for UC16 is still the
same, which contains balanced words on both abstract and detail (technical) topics. UC1S1 had an intra
extension from UC1E1, which could help capture additional tokens. The words on UC1S1 and UC1E1 were
merged into “health care profession enter patient user iTrust medic record email provide assign mid secret key

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initial password person reset edit accord data format value default null appropriate number edit enter view
secure question prompt enter editor correct format requires data field input match specific patient”. The words
also on stemmed form. It contains many words (e.g. patient, medical record, mid, key person, and password)
shared among use case scenarios, which is why it could obtain the best results.
4.3. Experiment using query expansion based on the outer use case relationship
The third experiment was a query using tokens of use case scenario with expansion from the outer
use case relationship. It were preprocessed using tokenize, stop word elimination, and stemming using the
porter algorithm. The preprocessed tokens originating from the use case scenario were concatenated with
additional tokens from the outer use case scenario to build a query for information retrieval. The result is
depicted in Table 5.
The amount of items retrieved was reduced to 479 of 541 documents, and the amount of items
relevant and retrieved was also decreased to 55 documents of 102 documents relevant. The average recall
was 60.7%, which means that the baseline approach could provide 60 documents out of 100 correct
documents. The average number of precision was approximately the same at 15.4%, which means it could
recommend 15 documents of 100 documents.
The best recall was 100%, which comes from several use case scenarios (UC9S2, UC10E1,
UC23S1, UC26S1, UC26S4). The reason was about the same, which those files used many words which
quite technical, e.g., “immunization, diagnoses, and office visit” which could also be found on the source
codes as implemented in the field of persistent files such as databases/tables. The advantage of the outer layer
had an impact on UC23S3, UC11S1, and UC11S2 as shown in Figure 16. The precision of both UC11S1 and
UC11S2 increased to 19% and 27.8%, respectively, with a baseline precision for UC11S1 of only 10% and
UC11S2 of 23.8%. Meanwhile, the precision of UC23S3 also increased to 8% from 4%.
limited (1 and 2 documents only). The words on the use case scenario UC10S2 were “HCP choose height
weight graph. presented chart chosen measurements patient spanning 3 calendar years data, averaged quarters
(January-March, April-June, July September, October-December)”. It is quite specific and directed and might
not be shared among use case scenarios.
Table 5. Experiment results using outer relationships as query expansion
Query Items Retrieved Items relevant and items retrieved Items relevant Recall Precision
UC1S1 4 2 5 40.0% 50.0%
UC9S1 14 2 8 25.0% 14.3%
UC9S2 12 3 3 100.0% 25.0%
UC10S1 46 12 15 80.0% 26.1%
UC10S2 18 0 1 0.0% 0.0%
UC10E1 31 2 2 100.0% 6.5%
UC10E2 37 0 2 0.0% 0.0%
UC11S1 26 5 6 83.3% 19.2%
UC11S2 18 5 6 83.3% 27.8%
UC16 12 6 8 75.0% 50.0%
UC23S1 25 2 2 100.0% 8.0%
UC23S3 30 6 26 23.1% 20.0%
UC23S4 28 1 2 50.0% 3.6%
UC26S1 34 2 2 100.0% 5.9%
UC26S2 51 3 6 50.0% 5.9%
UC26S3 44 2 4 50.0% 4.5%
UC26S4 37 1 1 100.0% 2.7%
UC28 12 1 3 33.3% 8.3%
Figure 16. Data of outer relationship
The best precision on this phased was 50%, which came from UC16 and UC1S1. After we inspect the
data, we found that the UC16 contains balanced words on both abstract and detail (technical) topics. This results

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about the same with the previous phase (non query expansion and inner use case relationship). The outer use
case precision rate mostly underperforms against inner relationship, except on UC11S2 which 4% better. The
reason was the token related with it could extent the search result.
4.4. Experiment using query expansion based intratoken use case relationship
The fourth experiment was a query using tokens of use case scenario with expansion from the
intratoken use case relationship. It was preprocessed using tokenize, stop word elimination, and stemming using
the porter algorithm. The preprocessed tokens originating from the use case scenario were concatenated with
additional tokens from the intratoken use case scenario to build a query for information retrieval. The result is
depicted in Table 6.
The amount of items retrieved was reduced to 475 of 541 documents, and the amount of items
relevant and retrieved was also decreased to 53 documents of 102 documents relevant. The average recall
was 59.3%, which means that the baseline approach could provide 59 documents out of 100 correct
documents. The average number of precision was approximately the same at 15,5%, which means it could
recommend 15 documents of 100 documents.
The best recall was 100%, which comes from several use case scenarios (UC9S2, UC10E1, UC23S1,
UC26S1). The reason was about the same, which those files used many words which quite technical, e.g.,
“immunization, diagnoses, and office visit.”, which could also be found on the source codes as implemented in
the field of persistent files such as databases/tables.
The advantage of the intratoken had an impact on UC10S2 and UC10E1. The precision of both
UC10S2 and UC10E1 increased to 4.8% and 6.7%, respectively, and the baseline precision for both UC10S2
and UC10E1 was 0%. The precision of UC23S1 also increases to 8% from previous precision (4.7%).
limited (1 & 2 documents only). The words on the use case scenario UC10S2 were “HCP choose height weight
graph. presented chart chosen measurements patient spanning 3 calendar years data, averaged quarters (January-
March, April-June, July September, October-December)”. It is quite specific and directed and might not be
shared among use case scenarios.
The best precision was 50%, which came from UC16 and UC1S1. The reason for UC16 is still the
same, which contains balanced words on both abstract and detail (technical) topics. This results about the same
with the previous phase (non query expansion and inner use case relationship). The outer use case precision rate
mostly underperforms against inner relationship, except on UC11S2 which 4% better. The reason was the token
related with it could extent the search result.
Table 6. Experiment results using intratoken relationships as query expansion
UC1S1 4 2 5 40.0% 50.0%
UC9S1 23 3 8 37.5% 13.0%
UC9S2 18 3 3 100.0% 16.7%
UC10S1 35 11 15 73.3% 31.4%
UC10S2 21 1 1 100.0% 4.8%
UC10E1 30 2 2 100.0% 6.7%
UC10E2 30 0 2 0.0% 0.0%
UC11S1 17 3 6 50.0% 17.6%
UC11S2 16 3 6 50.0% 18.8%
UC16 12 6 8 75.0% 50.0%
UC23S1 25 2 2 100.0% 8.0%
UC23S3 32 11 26 42.3% 34.4%
UC23S4 29 1 2 50.0% 3.4%
UC26S1 45 2 2 100.0% 4.4%
UC26S2 56 4 6 66.7% 7.1%
UC26S3 44 2 4 50.0% 4.5%
UC26S4 26 0 1 0.0% 0.0%
UC28 12 1 3 33.3% 8.3%
4.5. Experiment using query expansion-based compound of all elements
The fifth experiment was query using tokens of use case scenario with expansion from the compound
of all elements from the inner, outer and intratoken use case relationships. It was preprocessed using tokenize,
stop word elimination, and stemming using the porter algorithm. The preprocessed tokens originating from the
use case scenario were concatenated with additional tokens from the compound of all element token use case
scenarios to build a query for information retrieval. The result is depicted in Table 7.

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The amount of items retrieved increased to 604 of 541 documents, and the amount of items relevant
and retrieved also decreased to 62 documents of 102 documents relevant. The average recall increase was
68.3%, which means that the baseline approach could provide 68 documents out of 100 correct documents.
Unfortunately, the average precision dropped to 10%, which means that it could recommend 10 documents of
100 documents.
The best recall was 100%, which comes from several use case scenarios (UC9S2, UC10S2, UC10E1,
UC11S1, UC11S2, UC23S1, UC26S1). The reason was the increase in words due to the impact of all
association tokens on the query. The worst recall was also the same as 0, which appeared on UC10E2. The
reason was about the same, which it implemented in few files of source code, and as a result, the item relevance
becomes limited (1 & 2 documents only).
The best precision was 33%, which came from UC1S1. The reason for this is that UC1S1 contains
balanced words on both abstract and detail (technical) topics. UC16 no longer has the best precision since it has
many additional words for query expansion. The advantage for compounds of all elements was the increase in
recall. Most documents were returned, and only UC10E2 did not return correct recommendations (recall 0),
while the precision was reduced since the research produced many documents.
Table 7. Experiment using the compound of all element token relationships as query expansion
Query Items Retrieved Items relevant and items retrieved Items relevant Recall Precision
UC1S1 21 3 5 60.0% 14.3%
UC9S1 26 4 8 50.0% 15.4%
UC9S2 19 3 3 100.0% 15.8%
UC10S1 36 12 15 80.0% 33.3%
UC10S2 21 1 1 100.0% 4.8%
UC10E1 30 2 2 100.0% 6.7%
UC10E2 30 0 2 0.0% 0.0%
UC11S1 57 6 6 100.0% 10.5%
UC11S2 57 6 6 100.0% 10.5%
UC16 24 7 8 87.5% 29.2%
UC23S1 23 2 2 100.0% 8.7%
UC23S3 28 5 26 19.2% 17.9%
UC23S4 29 1 2 50.0% 3.4%
UC26S1 40 2 2 100.0% 5.0%
UC26S2 56 4 6 66.7% 7.1%
UC26S3 44 2 4 50.0% 4.5%
UC26S4 26 0 1 0.0% 0.0%
UC28 37 2 3 66.7% 5.4%
4.6. Result analysis and comparison
The sets of experiments have been performed and produce several results. Each of the results was
compared to each other to measure how effective our methods were at finding feature locations. The chart is
shown in Figure 17. Based on the chart, the best recall was the experiments using compound off all relationships
(inner, outer, and intratoken). The main reason was that the number of tokens was huge since all use case
relationships are included here. The compound produces less precision since it takes all tokens, which impacts
the increasing number of documents as a dividing factor in precision measurement.
Figure 17. Comparison of average precision and recall
0.15 0.167 0.154 0.155 0.107
0.608 0.599 0.607 0.593
0.683
0%
20%
40%
60%
80%
100%
Use Case Only Inner Relation
Expansion
Outer Relation
Expansion
Intratoken
Expansion
All Compound
PERCENTAGE
RATES
EXPERIMENTS
Average Precision
Average Recall

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The best precision was the experiment using the inner relation. The worst recall was the experiment
using intratokens, and the worst precision was all compounds. The Inner performs with the best precision
because many use cases have inner relations among them, and the total tokens related were higher than the outer
and intratokens.
5. CONCLUSION
This research introduces the novel concept of the use case relationship. It includes inner association,
outer association, and intratoken association. The use case relationship was implemented and tested on the topic
modeled source using the LDA algorithm. Query expansion based on inner, outer and intratoken associations was
tested to find feature locations. The precision and recall rates were used to measure the success of the approach.
The best precision rate was 50% found in UC16, which contained tokens that were balanced on both
the abstract side and technical side. The best recall was 100%, which was found in several use case scenarios
implemented in a few files. The best average precision rate was 16.7%, which was found in inner association
experiments. The inner association could help attract more tokens among other associations (outer, intratoken),
which made average precision better than baseline (use case without expansion). The best average recall rate
was 68.3% on all compound experiments since it contains all expansion tokens.
In the future, we plan to extend the methodology on the source code processing side by applying some
structural exploration. The association among identifiers, methods, and comments could also be arranged on
some data modeling to gain better precision and recall in feature location. The additional weighting of source
code elements might also beneficial as an alternative methodology.
ACKNOWLEDGEMENTS
This work was supported by the Ministry of Education, Culture, Research, and Technology of
Indonesia through Doctoral Dissertation Research (Penelitian Disertasi Doktor) under grant no.
1417/PKS/ITS/2022.
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BIOGRAPHIES OF AUTHORS
Achmad Arwan received an M.S. degree in informatics engineering from Institut
Teknologi Sepuluh Nopember, Surabaya, Indonesia in 2015. He is currently pursuing a
doctoral degree in informatics engineering Institut Teknologi Sepuluh Nopember. His research
interest includes software mining repository, database, and software development. He can be
contacted at email arwan@ub.ac.id.
Siti Rochimah received her Ph.D. degree in software engineering from Universiti
Teknologi Malaysia in 2010. Currently, she is the head of the Software Engineering laboratory
with the Department of Informatics, Institut Teknologi Sepuluh Nopember. She authored and
coauthored more than 50 articles related to software engineering. Her research interests include
software quality, software traceability and software testing. She can be contacted at email
siti@if.its.ac.id.
Chastine Fatichah received her Ph.D. from the Tokyo Institute of Technology,
Japan, in 2012. She is currently full professor at the Institut Teknologi Sepuluh Nopember,
Surabaya, Indonesia. Her research interests focus on artificial intelligence, data mining, and
image processing. She can be contacted at email chastine@if.its.ac.id.

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