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ICSE 2020
Wed 24 June - Thu 16 July 2020
Tue 7 Jul 2020 08:43 - 08:51 at Baekje - I4-Clones and Changes Chair(s): Chanchal K. Roy

Context Code-free software similarity detection techniques have been used to support different software engineering tasks, including clustering mobile applications (apps). The way of measuring similarity may affect both the efficiency and quality of clustering solutions. However, there has been no previous comparative study of feature extraction methods used to guide mobile app clustering.

Objective In this paper, we investigate different techniques to compute the similarity of apps based on their textual descriptions and evaluate their effectiveness using hierarchical agglomerative clustering.

Method To this end we carry out an empirical study comparing five different techniques, based on topic modelling and keyword feature extraction, to cluster 12,664 apps randomly sampled from the Google Play App Store. The comparison is based on three main criteria: silhouette width measure, human judgement and execution time.

Results The results of our study show that using topic modelling, in addition to collocation-based and dependency-based feature extractors perform similarly in detecting app-feature similarity. However, dependency-based feature extraction performs better than any other in finding application domain similarity (ρ = 0.7, p − value < 0.01).

Conclusions Current categorisation in the app store studied does not exhibit a good classification quality in terms of the claimed feature space. However, a better quality can be achieved using a good feature extraction technique and a traditional clustering method.

Tue 7 Jul
Times are displayed in time zone: (UTC) Coordinated Universal Time change

08:05 - 09:05
HARP: Holistic Analysis for Refactoring Python-Based Analytics ProgramsTechnical
Technical Papers
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CC2Vec: Distributed Representations of Code ChangesTechnical
Technical Papers
Thong HoangSingapore Management University, Singapore, Hong Jin KangSchool of Information Systems, Singapore Management University, Julia LawallInria, David LoSingapore Management University
Code Duplication on Stack OverflowNIER
New Ideas and Emerging Results
Sebastian BaltesQAware GmbH and The University of Adelaide, Christoph TreudeThe University of Adelaide
Pre-print Media Attached
Siamese: Scalable and Incremental Code Clone Search via Multiple Code RepresentationsJ1
Journal First
Chaiyong RagkhitwetsagulMahidol University, Thailand, Jens KrinkeUniversity College London
Link to publication DOI Pre-print Media Attached
Empirical comparison of text-based mobile apps similarity measurement techniquesJ1
Journal First
Afnan Al-SubaihinKing Saud University, Federica SarroUniversity College London, UK, Sue BlackDurham University, Licia CapraUniversity College London
Link to publication DOI Media Attached File Attached
SimilarAPI: Mining Analogical APIs for Library MigrationDemo
Chunyang ChenMonash University