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Enhancing Software Development Efficiency
Software development projects frequently encounter cost and time efficiency challenges, rendering them susceptible
to failure. An imprecise and incomplete process of eliciting user software requirements can further escalate software
development’s time, cost, and effort. Consequently, the reuse of user stories based on application descriptions for software
requirement elicitation is proposed to mitigate these issues and enhance cost efficiency. This research aims to evaluate the reuse
of user stories within the software elicitation process by extracting software features from initial application descriptions. The
goal is to determine whether this approach can effectively reduce the time and cost associated with software development. This
study employs Natural Language Processing (NLP) techniques to identify Part-Of-Speech (POS) tag patterns to extract software
features from application descriptions. A similarity measurement is conducted between user stories and the extracted software
features. User stories are then filtered based on a similarity threshold to ensure high relevance to the extracted software features.
The proposed method was tested using three distinct application descriptions alongside 22 user story datasets provided to nine
respondents. The analysis indicates that the reuse of user stories based on feature extraction from application descriptions,
determined through Cosine similarity with an 80% threshold, significantly enhances software development efficiency in terms of
time and cost. The method demonstrated an average precision of 84%, recall of 93%, and F1-score of 86% across the three test
projects, confirming its alignment with system design requirements. The findings of this research validate the effectiveness of the
Cosine similarity method in identifying reusable user stories from application descriptions. This approach is a viable solution
for reusing user stories in the software requirement elicitation process, thereby improving efficiency in software development
projects.
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Indra Kharisma Raharjana is an
Associate Professor at Universitas
Airlangga. He holds degrees from
Institut Teknologi Sepuluh Nopember
and Institut Teknologi Bandung. His
research focuses on software
engineering and natural language
processing. He is Editor-in-Chief of the Journal of
Information Systems Engineering and Business
Intelligence and an active member of CASE, IEEE,
ACM, INACL, and AISINDO.
Avril Hermawan received a
bachelor’s degree in information
systems from the Universitas
Airlangga, Indonesia, in 2023. He
currently works as a digital product
campaign specialist at PT. Bank
Tabungan Negara (BTN). His
professional interests include Digital Marketing, Digital
Product Management, and User Experience.
Badrus Zaman is a lecturer in the
information systems study program at
Universitas Airlangga. He received a
bachelor’s degree in informatics
engineering from the Institut
Teknologi Sepuluh Nopember,
Indonesia, in 2005, and a master’s
degree in computer science from the Universitas Gadjah
Mada, Indonesia, in 2011. His research interests Cover
Information Systems and Natural Language Processing.
Shukor Sanim Mohd Fauzi is a
Senior Lecturer at the Faculty of
Computer and Mathematical
Sciences, Universiti Teknologi
MARA. He holds degrees from
UiTM, Universiti Teknologi
Malaysia, and a Ph.D. in Software
Engineering from UNSW. His research interests include
Software Engineering, Empirical Studies, Software
Repository Mining, Social Network Analysis, Socio-
Technical Congruence, and Collaborative Software
Processes.