The 20th International Joint Conference on Computer Science and Software Engineering (JCSSE2023)

Design and Evaluating a Method Using Project
Corpus for Inferring Software Description
3rd Masahide Nakamura 1,2

1st Kohei Terakawa

2nd Sinan Chen

Kobe University,
1-1 Rokkodai-cho, Nada,
Kobe, 657-8501, Japanodajin@ws.cs.kobe-u.ac.jp

Kobe University,
1-1 Rokkodai-cho, Nada,
Kobe, 657-8501, Japan
chensinan@gold.kobe-u.ac.jp

Abstract—Obsolete software developed in the past is
gradually phased out over time. However, the source codeof such software contains a wealth of information that canbe re-purposed and possesses a high value as an asset.
Thus, understanding the characteristics of existing software
can aid in developing new software. In a previous study,
we proposed a method for inferring the architecture ofan existing system using a project corpus and conductedpreliminary experiments to verify its feasibility. The ﬁndings
revealed that the project corpus could be used to infer a system’s purpose, functionality, and technology. In this present
study, we conﬁrm the validity of the project corpus froma perspective that was not examined in previous studies.
We established three veriﬁcation items and conducted anexperiment in which we employed project corpus to infer
the functionality of the system, the technology utilized in
the system, and the architecture of the system. From theexperiment results, we conﬁrmed that the accuracy of the
project’s inference depends on two factors: ﬁrst, that the
project corpus accurately reﬂects the system’s information,and second, the participants’ familiarity with the words inthe corpus.
Index Terms—software development, technical debt, upcycling, corpus

I. I NTRODUCTION
The gradual obsolescence of software developed in
the past can be attributed to various factors, such asadvances in the development environment and the accrualof technical debt [1]. However, the source code of such
software contains a wealth of reusable and valuable
information that can be leveraged in developing new software by comprehending the characteristics of the existing
software. In order to recycle existing software products,
we are exploring the concept of software upcycling. In
software upcycling, it is crucial to comprehend a preexisting project’s objective and functionality and discoverreusable concepts and designs.
In a previous study, we proposed a method of inferring
the purpose and function of a system using a project
corpus, corpusP j , thereby providing an understanding of
the outline of an existing project. Preliminary experiments
were conducted to verify the effectiveness of the proposed
method. However, certain challenges have persisted in
the initial experiments which were carried out by threeparticipants. However, it is imperative to augment the
number of participants and conduct additional experiments to investigate the efﬁcacy of the proposed tech-

979-8-3503-0050-5/23/$31.00 ©2023 IEEE

1

2

Kobe University,
RIKEN Center for Advanced Intelligence Project,Tokyo, 103-0027, Japanmasa-n@cs.kobe-u.ac.jp

nique. Additionally, there is scope for reﬁnement of theexperimental methodology. Initial experiments indicatedthat technical insights could be gleaned from corpusP j .
Therefore, it is imperative to validate the effectiveness ofthe project corpus from the perspective of comprehending
the technology employed.
This study aimed to ascertain the validity of corpusP jfrom an alternate perspective than that of prior research.
To achieve this goal, we formulated the following researchquestion: (RQ1) Is it possible for corpusP j to discern thesystem’s functionality? (RQ2) Is it feasible for corpusP j
to provide an understanding of the technology employedin the system? (RQ3) Could corpusP j provide a comprehensive understanding of the system? Moreover, thesurvey will be administered to more participants than inpreceding studies, and the ﬁndings will be analyzed.
In our experiment, the corpusP j employed is constructed utilizing the method proposed in the preceding
study. corpusP j is a denomination stipulated in the
proposed methodology to denote a corpus within thecontext of software. A corpus [2] is typically utilized inlinguistics and refers to an extensive database of texts
and utterances. In other words, a corpus in software isa summary of a project created from the information
contained in the project. The proposed method of theprevious study concentrated on class name component
words as key concepts. Class names are assumed to beclosely associated with the objective and functionality ofa project, and class name component words are extracted.
The resulting words are deemed to embody the essenceof the project from. They are extracted and are used
as a reference to infer the outline of the project. The
experiment comprises the following ﬁve steps: (Step 1)
Selection of project and participants. (Step 2) Corpus
preparation. (Step 3) Questionnaire design. (Step 4) Participants’ responses. (Step 5) Evaluation of responses.
The experiment’s ﬁndings indicated that there werequeries with a substantial proportion of accurate answers
and queries with a minimal proportion of accurate answers. The analysis results afﬁrmed that two principal
factors inﬂuenced the comprehension of the project outline: (1) corpusP j must contain adequate information
about the system. (2) It transpires that a certain degreeof familiarity with pre-existing technology and somebackground in software engineering is necessary to makedeductions.

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The 20th International Joint Conference on Computer Science and Software Engineering (JCSSE2023)

II. P REVIOUS S TUDY: R EASONING E XISTINGP ROJECTS ’ D ESCRIPTIONS BASED ON C LASSNAMEW ORD E LEMENTS [3]A. Research Focus
Software upcycling [4] is a technique of software
reuse. Software upcycling transforms existing software
products into valuable software assets (code, services,etc.) by focusing on their design and implementationand incorporating novel modiﬁcations. Consequently, insoftware upcycling, it is crucial to comprehend the objective and functionality of a pre-existing project anddiscover reusable concepts and designs [5]. However, notall software products in organizations and companies aremaintained in an asset-like fashion. In certain instances,
documentation such as READM E is absent or inadequate. In order to foster upcycling, it is necessary to havean overview of the project regardless of the existence orotherwise of documentation.
B. Proposed Method
In the previous study, we proposed a methodology for
obtaining a comprehensive perspective of a pre-existing
project without relying on the READM E or other documentation. The corpusP j is constructed from class nameconstituent words, and the objective and functions of thesystem are deduced from it. The outline is presented inFig. 1. It exempliﬁes applying the proposed methodology
to a project titled MP3PlayService. In step 1, repository
mining is executed to acquire all class ﬁle names ofextant classes, and in step 2, the class names obtained
are partitioned into words, and the resulting word group isdenoted corpusP j . In step 3, the words obtained in Step
2 are arranged in lexicographic order, and a tag cloud[6] image is generated utilizing weights based on word
frequencies. In Step 4, the objective and function of theoriginal project are deduced utilizing corpusP j expressedin two distinct manners.
C. Project Corpus
The proposed method deﬁnes corpusP j as a set ofwords obtained through the class name segmentation
process. A corpus, commonly used in linguistics, refers
to a collection of linguistic data, typically comprising a
database of sentences drawn from various sources suchas books, articles, and other written materials. That is,
corpusP j denotes a corpus in the context of software.
Speciﬁcally, class names incorporated in a project areamassed and separated into words. The acquired words
are deemed to reﬂect the character of the project fromwhich they are extracted, and if the project outline canbe deduced from these words, it is effortless to obtaina comprehensive perspective of the project, even if the
README is absent.
D. Results of Preliminary Experiment
A preliminary experiment utilizing the proposed
method was conducted on three participants. All participants correctly answered more than half of the questions,indicating that the class name component words significantly impacted comprehension of the project outline.

However, there are limitations to the study. The preliminary experiment was conducted with a small sampleof three participants. Thus, it is essential to conduct
further experiments with a larger sample size to assess the
proposed methodology’s effectiveness fully. Discussionof the preliminary experiment revealed that corpusP jprovides technical clues. Some participants provided a
mixture of descriptions of functions and technology used,
and it was impossible to isolate the degree to which they
understood technical matters. Therefore, it is necessary
to verify the validity of corpusP j from the viewpoint ofunderstanding the technology used.
III. E XPERIMENTAL D ESIGN
A. Purpose of Experiment
The goal of this study is to verify the effectiveness ofthe proposed method from a perspective not considered inprevious studies. For this purpose, we set the following
research questions: RQ1: Is it possible for corpusP j
to discern the functionality of the system? RQ2: Is it
feasible for corpusP j to provide an understanding of thetechnology employed in the system? RQ3: Is it possible
for corpusP j to provide a comprehensive understandingof the system?
B. Experiment Overview
An experiment is conducted in order to address theresearch questions. The experiment comprises the following ﬁve steps: (Step 1) Selection of Projects and Participants: 154 projects managed within our laboratory
is selected as participants. (Step 2) Corpus Creation:
A corpusP j is created utilizing the method proposedin a previous study. (Step 3) Question Design: Threequestions is formulated to address the research questions.
(Step 4) Participant Responses: Participants is asked toprovide their answers in natural language using a Google
Form. (Step 5) Scoring of Answers: Individuals withknowledge and understanding of each project evaluate andscore the responses.
C. STEP 1: Selection of Project and Participant
The projects utilized in this experiment are Java-based
projects hosted on Gitlab, which our laboratory maintains.
A random sampling of ten projects is selected from theseprojects and employed as experimental data. The speciﬁcsof the projects employed in the experiments are presented
in Table I, which highlights the details of the projects
utilized in the experiments. The summary and functions
presented in Table I were created by knowledgeable
Gitlab administrators for each project, who extracted
the main functions and created a summary. For thisexperiment, it is desirable to have participants with similar
conditions such as knowledge and experience. Therefore,
for this study, 12 students majoring in information scienceat the same university were selected as participants.
D. Step 2: Corpus Creation
The proposed method from the previous study is utilized to construct corpusP j . Repository mining is executed on ten experimental projects to extract class namecomponent words using GitlabAPI. The obtained data is

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The 20th International Joint Conference on Computer Science and Software Engineering (JCSSE2023)

Fig. 1. Flow of the proposed method in the previous study.

TABLE ID ETAILS OF PROJECT UTILIZED IN THE EXPERIMENT.

then segmented into class names using Python, creating
corpusP j for each of the ten projects. The acquired
corpusP j are arranged in lexicographic order, and a tag
cloud image generated utilizing the word frequencies as
weights. Table II illustrates the generated corpusP j andtag cloud images.

many technologies and mechanisms you can discern that
this system utilizes. (Q3): Summarize in one or two lines
a general understanding of the system as inferred from
corpusP j . The questions Q1, Q2, and Q3 align withthe research questions RQ1, RQ2, and RQ3, respectively.
Each question will be examined in Section IV and willserve as a response to the research questions.

E. Step 3: Design of Questions
The following three questions are designed to assessparticipants understanding of the project: (Q1): Please list
as many bullet points as possible to outline this system’sfunctionality. (Q2): Please outline in bullet points the

F. Step 4: Answers by participants
The corpusP j generated in Step 2 is presented toparticipants on a Google Form, and they are asked to
input their answer. Two expressions prepared in Step 2 are

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The 20th International Joint Conference on Computer Science and Software Engineering (JCSSE2023)
TABLE IIC ORPUS CREATED IN THE EXPERIMENT.
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TABLE IIIE VALUATION CRITERIA IN EXPERIMENTS .
LPDJHEvaluation

Criterion◎

Correct◯

Mostly Correct△

Partialy Correct , or Partialy Incorrectx

Incorrect?

Unclear
TABLE IVP ERCENTAGE OF CORRECT ANSWERS .

Question Number

Q1

Q2

Q3

1

58%

83%

75%

2

58%

92%

58%

3

92%

67%

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42%

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75%

100%

8

83%

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58%

9

58%

42%

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10

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17%

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G. Step 5: Scoring of Answers
The responses obtained in Step 4 are scored by individuals familiar with the details of the project being
experimented upon.
One who has a good understanding of the experimental
projects conducts the scoring. The evaluation criteria arepresented in Table III. The presence or absence of theelements shown in Table I is roughly evaluated based on
the following 5-level scale within the answers.
IV. E XPERIMENTAL R ESULTS AND D ISCUSSION
A. Outline
The experiment yielded responses from all 12 participants. We deﬁned this experiment’s percentage of correct
answers as the sum of ◎ and ◯ scores. The formula isas follows:of ⊚and⃝
Correct rate(%) = T otalSum
number of answers × 100
The percentage of correct answers to the three questions Q1, Q2, and Q3 for each question is presented
in Table IV. Question 7 had the highest rate of correct
answers across Q1 Q3, while Question 5 had a rate of 0
for all questions.
B. (Result 1) Analysis for RQ1

utilized for the corpusP j . To measure the time required
and answering, participants were instructed to record their
responses’ start and ﬁnish times.

Fig. 2 illustrates the scoring results for each question
in response to Q1. Twelve participants provided a ratingof ◎, ◯, △, ×, or ? Consequently, a total of 12 ratings
were obtained for each question. The graph displays thebreakdown of the scoring results for each question, withdarker areas indicating higher scores. The graph indicatesthat more than 80% of the participants answered ◎ and

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Fig. 2. Breakdown of scoring results for Q1.

Fig. 3. Breakdown of scoring results for Q2.

○ for questions 3 and 7, indicating a high percentageof accurate responses. Conversely, for questions 4 and5, the percentage of accurate responses is low, and thescoring results reveal that more than 70% of the answers
are marked with X or ? representing a majority of the total
number of correct answers. The percentage of accurate
responses to questions 4 and 5 could be higher. To
examine the cause of the higher percentage of correct
answers, we focus on the LifeActivityMailer in question
3. As depicted in the overview, the project comprises
two functions: detecting the value of a sensor box andnotifying the owner of the sensor via email upon detectionof a change. Seven out of twelve participants accurately
described these functions, resulting in a score of ◎. The
corpus for question 4, as presented in TableII, is listed
below.
•Box Detection Listener Location Mailer Mailer
Mailer Mailer Manager Model Notify SBCPD
SBCPD Score Sensor Service Service Solver Stream
Task

The lexicon presented here pertains to the dual functionality of LifeActivityMailer. The lexical items associated with the functions are listed below: (Function 1:
Detection of the value of a sensor box) Sensor, Box,
Score, Detection (Function 2: Notiﬁcation of the sensor
owner via email) Mailer, Notiﬁcation
It is intuitive to infer that a sensor detects a value.
Thus it is reasonable to assume that most participants
focused on the words Sensor, Score, and Detection from
corpusP j . Thus, it was revealed that the participants
are inferring the system’s functionalities by connecting
semantically related words together. To investigate thefactors contributing to the low rate of correct responses,
we focus on question 5. As illustrated in TableI, thisproject pertains to a screen design for another project.
Given that the project was created using the Android SDK[7], the corpus includes component names speciﬁc to
the Android SDK, such as Activity and Fragment. These
concepts are utilized in Android application development
and are not germane to the speciﬁc functionality of the
project. These ﬁndings suggest that when the corpus lacks
adequate information for inference, it becomes challenging to deduce the functionality.

C. (Result 2) Analysis for RQ2
Fig. 3 illustrates the scoring results for each question in
response to Q2. It can be observed that for questions 1, 2,
6, 7, and 8, a majority of answers were scored as ○ and◎, indicating a high percentage of correct answers. Conversely, questions 4, 5, and 10 exhibited a low percentage
of correct answers, with a conspicuous presence of X or
? scores. Most participants provided correct answers byreferencing speciﬁc terminology within the corpus andutilizing analogies to describe functions. For instance,
consider Question 6. As shown in the TableI, the functionof Question 6 is to exchange user information with a
database. 10 of the 12 participants listed MySQL as thetechnology they use, and at the same time, they wrote
about CRUD in their answers to Q1. The presence of theword ”MySQL” suggests that the participants interpreted
that the system has a function to perform CRUD to the
DB. The above results indicate that when words related tothe technology used are present in the corpus, participants
use technical terms to infer the system’s behavior. On theother hand, some examples were interpreted differently bydifferent participants. In Question 4, more than half of the
responses were answered ?(Unclear). As shown in TableI,
the function of Question 4 is to post the data obtained
by BLE to the DB. BLE [8] is one of the extensionsof Bluetooth. It can communicate at meager power and isused for various sensors. However, only three participants
described BLE as a technology used and read the functionof BLE as a sensor. The amount of information that canbe read from the technology used was found to vary fromparticipant to participant.
D. (Result 3) Analysis for RQ3
Fig. 4 illustrates the scoring results for each question
in response to Q3. As opposed to the graph of RQ1, questions with high and low percentages of correct answers
are prevalent. However, the percentage of correct answers
for questions 3, 6, 8, and 9 is lower than that for Q1.
The results of our examination of the virtual-care-giver inquestion 8, which exhibited a lower percentage of correct
answers than in Q1, reveal that the system comprises
various types of services provided by a virtual agent [9].
All 12 participants correctly identiﬁed at least one system
function, such as video playback or conversation throughquestions. Notably, 11 out of 12 participants correctly

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The examination of the research question highlighted thatthe knowledge and experience of the participant impacts
the inference process utilizing corpusP j . In future research, it would be beneﬁcial to investigate not only theexperience and knowledge of participants but also thespeciﬁc words they focused on and the information they
extracted.

Fig. 4. Breakdown of scoring results for Q3.

identiﬁed the scheduling function. However, only 3 outof 12 participants accurately comprehended the overall
functionality of the virtual agent in Q3. Many of theincorrect answers indicated that participants perceived thesystem primarily as a video playback and schedule management system, indicating a need for an understandingof the multiple functions of the system and the inability
to infer an overview by synthesizing these functions.
E. Discussion
The analysis of RQs 1, 2, and 3 conﬁrms that project
inferences are contingent upon two factors. The ﬁrst is theextent to which the corpus, corpusP j , accurately reﬂects
the information possessed by the system. In the analysisof RQ1, we provided an example where corpusP j needed
to be improved in information on the project’s functionality. As corpusP j is constructed from class names andcomponent words, it is imperative that the original classnames accurately represent the system’s characteristics.
The second factor is the participant’s familiarity with thewords present in the corpus. In the analysis of RQ2, we
identiﬁed instances where the corpus contained words
related to the technologies utilized. Participants’ knowledge is imperative in correctly interpreting these technologies, resulting in the amount of information obtained
from the words varying among individuals [8]. RQ3 also
conﬁrms that understanding the system’s functions andtechnologies employed only sometimes results in a correct
overview of the project as a whole. Though the systemcomprises various functions, it is necessary to infer its
overall purpose by synthesizing them. To make accurateinferences from the given information, knowledge andexperience in software engineering are essential.
F. Further Challenges
One of the challenges associated with the proposed
method is structuring the corpus to include the information contained within the classes. Developers differ on
how many functions and roles they can include in a single
class. If the proposed method is for a class with multiple
methods, it is possible to ignore the function and purposeof the class. Therefore, it is expected that corpusP j canmore accurately reﬂect the roles of the class by obtaining
the names of the methods and member variables within
the ﬁle. Furthermore, the current study did not consider
variations in the individual characteristics of participants.

V. C ONCLUSION
In this study, we evaluated the effectiveness of the
proposed method from previous research. Speciﬁcally,
we reevaluated the survey items and examined the information obtained through the corpus from the perspectives of project function, technology employed, andoverall system overview. Additionally, we recruited 12
participants and analyzed the results accordingly. The
experiment yielded responses from 12 participants. It wasdetermined that the participants comprehension of the
project overview relied on two factors: the accuracy ofcorpusP j in summarizing the system and the level ofknowledge possessed by the participants. Future tasks
include incorporating additional information into the corpus by extracting method names and member variables
within the classes and structuring the corpus. The study
also revealed that the accuracy of a question is contingent
upon the participant. Therefore, we intend to investigate
the impact of participants’ foundational knowledge on the
results by documenting the information and techniques
they utilized for inference.
ACKNOWLEDGMENT
This research was partially supported by JSPS
KAKENHI Grant Numbers JP19H01138, JP20H05706,
JP20H04014, JP20K11059, JP22H03699, JP19K02973,
Grant-in-Aid for JSPS Research Fellow (No.22J13217),
and Tateishi Science and Technology Foundation (C)(No.2207004).
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