User Story Clustering using K-Means Algorithm in Agile Requirement Engineering

Kumar, Bhawnesh; Tiwari, Umesh; Dobhal, Dinesh C.; Negi, H. S.

doi:10.1109/cises54857.2022.9844390

Cited by 11 publications

(7 citation statements)

References 15 publications

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“…In this section, we compare our approach with existing state-of-the-art techniques [14], [16] that also use user stories as input. Although these approaches focus on clustering and duplicate detection, they do not include diagram generation.…”

Section: Discussionmentioning

confidence: 99%

“…After that, they defined simple NLP rules for component extraction to generate a use case diagram. In [14] authors used a K-means clustering algorithm applied to user stories. The authors in study [25] have provided a tool to extract the time spent in historical and similar user stories.…”

Section: Related Workmentioning

confidence: 99%

“…Several studies were conducted to automate the generation of models from natural language software requirements [4]- [12]. In addition, some authors studied natural language requirements clustering to decompose the target system at an initial level [13], [14]. The current requirements clustering approach lacks precision and does not achieve a high level of automation.…”

Section: Introductionmentioning

confidence: 99%

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A Smart AI Framework for Backlog Refinement and UML Diagram Generation

NASIRI,

LAHMER

2024

IJACSA

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In Agile development, it is crucial to refine the backlog to prioritize tasks, resolve problems quickly, and align development efforts with project goals. Automated tools can help in this process by generating Unified Modeling Language (UML) diagrams, allowing teams to work more efficiently with a clear understanding and communicate product requirements. This paper presents an automated approach to Agile methodology which refines backlogs by detecting duplicate user stories and clustering them. Following the refinement process, our approach generates UML diagrams automatically for each cluster, including both class and use case diagrams. Our method is based on machine learning and natural language processing techniques. To implement our approach, we developed a tool that selects the user stories file, groups them by actor, and employs the unsupervised k-means algorithm to form clusters. After that, we used Sentence Bidirectional Encoder Representations from Transformers (SBERT) to measure the similarity between user stories in a cluster. The tool highlights the most similar user stories and facilitates the decision to delete or keep them. Additionally, our approach detects similar or duplicate use cases in the UML use case diagram, making it more convenient for computer system designers. We evaluated our approach on a set of case studies using different performance measures. The results demonstrated its effectiveness in detecting duplicate user stories in the backlog and duplicate use cases. Our automated approach not only saves time and reduces errors, but it also improves collaboration between team members. With an automatic generation of UML diagrams from user stories, all team members can understand product requirements clearly and consistently, regardless of their technical expertise.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Smart AI Framework for Backlog Refinement and UML Diagram Generation

NASIRI,

LAHMER

2024

IJACSA

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“…Similarity Measure Algorithm. This approach is obtained from two literatures [16] [18]. The proposed approach effectively utilizes text clustering techniques to identify homogeneous groups of requirements and classify them based on similarity measures.…”

Section: A) Approaches In Determining User Story Qualitymentioning

confidence: 99%

“…The proposed approach effectively utilizes text clustering techniques to identify homogeneous groups of requirements and classify them based on similarity measures. This approach successfully clusters similar user stories [16] [18]. The text is enhanced for analysis through the application of multiple preprocessing steps, including stop word removal, tokenization, lemmatization and stemming.…”

Section: A) Approaches In Determining User Story Qualitymentioning

confidence: 99%

Approaches in Determining User Story Quality through Requirement Elicitation : A Systematic Literature Review

Angga Hendriana,

Raharjo,

Nurfitriani

2024

ijcs

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A survey of 108 agile practitioners revealed that user stories are the most widely used method for capturing requirements. However, user stories can be interpreted differently by different stakeholders, leading to potential misunderstandings within the development team. Additionally, the interconnectedness of user stories poses challenges during the requirement elicitation process. A Systematic Literature Review (SLR) of 27 articles about user story elicitation process were selected and these are examined to determine user story quality. This research will provide a comprehensive summary of user story elicitation approaches and their application in addressing user story quality issues. The study will also offer insights into selecting appropriate approaches for resolving challenges in user story requirement elicitation. Finally, most user story elicitation approach primarily focus on addressing the issue of ambiguity.

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