K-Means and Feature Selection Mechanism to Improve Performance of Clustering User Stories in Agile Development

Rizqy Ahsana Putri, Umi Laili Yuhana, Taufiq Choirul Amri

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Agile development is an approach in software development that aims to improve the flexibility, responsiveness, and quality of software products. One part of the planning phase in agile development is writing user stories. User story is a text used to detail needs from the user's point of view. The semi-natural and simplicity of user stories sometimes results ambiguity, inconsistent sentences, and incomplete sentences. This ambiguous user story has the potential to cause problems or conflicts, especially when programmers start building the product. To solve that problem, this research. To solve this problem, a method that can identify ambiguous and problematic user stories is needed. One method that can be implemented to solve this problem is the clustering method. This research uses the K-means algorithm because K-means has proven to be very good at clustering data. To improve clustering performance, we have added Variance Threshold method to select features. The Silhouette Value evaluation results in this experiment also show a significant increase to reach 0.9634384171608066 in clustering experiments with feature selection. This means that the feature selection method does have a positive impact on the clustering evaluation results.

Original languageEnglish
Title of host publicationProceedings
Subtitle of host publicationICMERALDA 2023 - International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages39-43
Number of pages5
ISBN (Electronic)9798350369359
DOIs
Publication statusPublished - 2023
Event2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023 - Virtual, Online, Indonesia
Duration: 24 Nov 202324 Nov 2023

Publication series

NameProceedings: ICMERALDA 2023 - International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications

Conference

Conference2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023
Country/TerritoryIndonesia
CityVirtual, Online
Period24/11/2324/11/23

Keywords

  • Feature Selection
  • K-means
  • Silhouette Value
  • User Story
  • Variance Threshold

Fingerprint

Dive into the research topics of 'K-Means and Feature Selection Mechanism to Improve Performance of Clustering User Stories in Agile Development'. Together they form a unique fingerprint.

Cite this