Application programming interface recommendation according to the knowledge indexed by app feature mined from app stores

Application programming interfaces (APIs) play an important role in the increasingly competitive mobile application development industry, as they can greatly improve the efficiency of app development. However, finding proper APIs is often time‐consuming for the gap between the knowledge of APIs and...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Journal of software : evolution and process Ročník 33; číslo 11
Hlavní autori: Liu, Lei, Li, Xun, Liu, Yuzhou, Liu, Huaxiao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Chichester Wiley Subscription Services, Inc 01.11.2021
Predmet:
API recommendation
app feature
Application programming interface
Applications programs
description mining
Feature extraction
Industrial development
Knowledge
Mobile computing
Queries
reverse engineering
ISSN:2047-7473, 2047-7481
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Application programming interfaces (APIs) play an important role in the increasingly competitive mobile application development industry, as they can greatly improve the efficiency of app development. However, finding proper APIs is often time‐consuming for the gap between the knowledge of APIs and app features. To solve this problem, we give an approach to summarize the wisdom of developers contained in the products in app stores and establish the system of API knowledge indexed by app features for the API recommendation. First, we extract features from the app descriptions and define the feature framework. Second, we parse the APK files of apps to gain the methods in code and APIs called by them and further introduce such API knowledge into the feature framework by utilizing method names as bridges. Finally, according to features in developers' queries, we locate corresponding feature nodes in the API knowledge system and recommend related API knowledge to developers. We conduct experiments based on 38,952 apps from five categories on Google Play, and the experimental results show that our approach has a good recommendation effect for the queries on app features. The approach summarizes the wisdom of developers in app stores for the application programming interface (API) recommendation: 1. It mines app features from products descriptions to construct the framework as the skeleton for API knowledge organization. 2. The API knowledge is extracted from the APK files and integrated into the feature framework to construct the knowledge system indexed by app features. 3. We use the developer's query to retrieve the knowledge system and design a recommendation framework to show the results.
Bibliografia:Funding information
National Key Research and Development Program of China, Grant/Award Number: 2017YFB1003103; Natural Science Research Foundation of Jilin Province of China, Grant/Award Number: 20190201193JC; Natural Science Foundation of China (NSFC), Grant/Award Number: 6210070366
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2047-7473
2047-7481
DOI:10.1002/smr.2380