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

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 13 No. 5 (2023)

DOI
10.11591/ijece.v13i5.pp5501-5516
ISSN
2722-2578, 2088-8708
Published
2023-10-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

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.

Collaboration network of the authors

40 authors · 70 co-authorship links · 15 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Institut Teknologi Sepuluh Nopember
  • University of Brawijaya
  • Universitas Airlangga
  • Hasanuddin University
  • University of Indonesia
  • North Sumatra Islamic University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Nanik SuciatiInstitut Teknologi Sepuluh Nopember9
Diana PurwitasariInstitut Teknologi Sepuluh Nopember8
Handayani TjandrasaInstitut Teknologi Sepuluh Nopember7
Anny YuniartiInstitut Teknologi Sepuluh Nopember3
Riyanarto SarnoInstitut Teknologi Sepuluh Nopember3
Achmad ArwanUniversity of Brawijaya2
Suhadi LiliInstitut Teknologi Sepuluh Nopember2
Hari GinardiInstitut Teknologi Sepuluh Nopember2
Ahmad SaikhuInstitut Teknologi Sepuluh Nopember2
Hilya TsaniyaInstitut Teknologi Sepuluh Nopember2
Hendra MesraHasanuddin University2
Eha Renwi AstutiUniversitas Airlangga2
Ramadhan Hardani PutraUniversitas Airlangga2
Bilqis AmaliahInstitut Teknologi Sepuluh Nopember1
M. Rahmat WidyantoUniversity of Indonesia1
Dyah Sulistyowati RahayuInstitut Teknologi Sepuluh Nopember1
Jayanti Yusmah SariInstitut Teknologi Sepuluh Nopember1
Mutmainnah MuchtarInstitut Teknologi Sepuluh Nopember1
Evan TanuwijayaInstitut Teknologi Sepuluh Nopember1
Bayu PriyambadhaUniversity of Brawijaya1
Aldy Sefan RezanaldyAffiliation unknown1
Yuita Arum SariUniversity of Brawijaya1
Ratih Kartika DewiUniversity of Brawijaya1
Abdul MunifInstitut Teknologi Sepuluh Nopember1
Vico Ade CandraAffiliation unknown1
Yudhi PurwanantoInstitut Teknologi Sepuluh Nopember1
Junaidi JunaidiNorth Sumatra Islamic University1
Faida RoyaniAffiliation unknown1
Imam KuswardayanInstitut Teknologi Sepuluh Nopember1
Rahmi Ika NovianaAffiliation unknown1
Faizal JohanAffiliation unknown1
Widyanti KartikaAffiliation unknown1
Darlis Heru MurtiInstitut Teknologi Sepuluh Nopember1
Choiru ZainAffiliation unknown1
Kusbandono Ari BowoAffiliation unknown1
Dewi RosidaAffiliation unknown1
Lutfi Rizal GozaliAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Query expansion using novel use case scenario relationship for finding feature location</dc:title><dc:creator>Arwan, Achmad</dc:creator><dc:creator>Rochimah, Siti</dc:creator><dc:creator>Fatichah, Chastine</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;span lang="EN-US"&amp;gt;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.&amp;lt;/span&amp;gt;&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2023-10-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v13i5.pp5501-5516</dc:identifier><dc:identifier>10.11591/ijece.v13i5.pp5501-5516</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 13 No 5 (2023); 5501</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>https://ijece.iaescore.com/index.php/IJECE/article/viewFile/31680/16848</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>