Title :
Mining API protocols based on a balanced probabilistic model
Author :
Deng Chen; Yanduo Zhang; Rongcun Wang;Wei Wei; Huabing Zhou; Xun Li; Binbin Qu
Author_Institution :
Hubei Provincial Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, 430205, China
Abstract :
API protocols are important for modern software development. They can be used in program testing, documentation, understanding and validation. Mining API protocols based on probabilistic models is proved to be an effective method to achieve protocols automatically. In this paper, we discuss the unbalanced probability problem caused by loops and recursive functions in application programs and a method based on the suffix tree is proposed to address it. In order to investigate the feasibility and effectiveness of our approach, we implemented it in our previous prototype tool ISpecMiner and performed a comparison test based on several real-world applications. Experimental results show that our approach can achieve protocols with more balanced probabilities than existing approaches, which provides a strong assurance for achieving valid and precise API protocols.
Keywords :
"Protocols","Probabilistic logic","Markov processes","Data mining","Arrays","Documentation","Automata"
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2015 12th International Conference on
DOI :
10.1109/FSKD.2015.7382307