A Data-driven Approach for Mining Software Features based on Similar App Descriptions and User Reviews Analysis
Paper i proceeding, 2024

Mobile app development necessitates extracting domain-specific, essential, and innovative features that align with user needs and market trends. Determining which features provide a competitive advantage is a complex task, often managed manually by product managers. This study addresses the challenge of automating feature mining and recommendation by identifying similar apps based on user-provided descriptions. The proposed approach integrates Named Entity Recognition (NER) for feature extraction from mined Google Play app data with BERT (Bidirectional Encoder Representations from Transformers) and Topic Modeling to find comparable apps. Our top-performing model, which uses Non-negative Matrix Factorization (NMF) for Topic Modeling with Sentence-BERT (SBERT) embeddings, achieves an F1 score of 87.38%.

Författare

Khubaib Amjad Alam

Al Ain University

Ramsha Ali

National University of Computer and Emerging Sciences Islamabad

Zyena Kamran

National University of Computer and Emerging Sciences Islamabad

Sabeen Fatima

National University of Computer and Emerging Sciences Islamabad

Irum Inayat

Software Engineering 1

Göteborgs universitet

Proceedings - 2024 39th ACM/IEEE International Conference on Automated Software Engineering, ASE 2024

2488-2489
9798400712487 (ISBN)

39th ACM/IEEE International Conference on Automated Software Engineering, ASE 2024
Sacramento, USA,

Ämneskategorier (SSIF 2011)

Språkteknologi (språkvetenskaplig databehandling)

Datavetenskap (datalogi)

DOI

10.1145/3691620.3695342

Mer information

Senast uppdaterat

2025-01-09