Zobrazit v EDS

API usage pattern recommendation for software development.

Uloženo v:
Podrobná bibliografie
Název: API usage pattern recommendation for software development.
Autoři: Niu, Haoran1 hit.haoran@gmail.com, Keivanloo, Iman1 iman.keivanloo@queensu.ca, Zou, Ying1 ying.zou@queensu.ca
Zdroj: Journal of Systems & Software. Jul2017, Vol. 129, p127-139. 13p.
Témata: *APPLICATION program interfaces, *COMPUTER software development, *DATA mining, *CLUSTER analysis (Statistics), PATTERN recognition systems
Reviews & Products: ANDROID (Operating system)
Abstrakt: Application Programming Interfaces (APIs) facilitate pragmatic reuse and improve the productivity of software development. An API usage pattern documents a set of method calls from multiple API classes to achieve a reusable functionality. Existing approaches often use frequent-sequence mining to extract API usage patterns. However, as reported by earlier studies, frequent-sequence mining may not produce a complete set of usage patterns. In this paper, we explore the possibility of mining API usage patterns without relying on frequent-pattern mining. Our approach represents the source code as a network of object usages where an object usage is a set of method calls invoked on a single API class. We automatically extract usage patterns by clustering the data based on the co-existence relations between object usages. We conduct an empirical study using a corpus of 11,510 Android applications. The results demonstrate that our approach can effectively mine API usage patterns with high completeness and low redundancy. We observe 18% and 38% improvement on F-measure and response time respectively comparing to usage pattern extraction using frequent-sequence mining. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Systems & Software is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáze: Business Source Index
Popis
Abstrakt:Application Programming Interfaces (APIs) facilitate pragmatic reuse and improve the productivity of software development. An API usage pattern documents a set of method calls from multiple API classes to achieve a reusable functionality. Existing approaches often use frequent-sequence mining to extract API usage patterns. However, as reported by earlier studies, frequent-sequence mining may not produce a complete set of usage patterns. In this paper, we explore the possibility of mining API usage patterns without relying on frequent-pattern mining. Our approach represents the source code as a network of object usages where an object usage is a set of method calls invoked on a single API class. We automatically extract usage patterns by clustering the data based on the co-existence relations between object usages. We conduct an empirical study using a corpus of 11,510 Android applications. The results demonstrate that our approach can effectively mine API usage patterns with high completeness and low redundancy. We observe 18% and 38% improvement on F-measure and response time respectively comparing to usage pattern extraction using frequent-sequence mining. [ABSTRACT FROM AUTHOR]
ISSN:01641212
DOI:10.1016/j.jss.2016.07.026