Parameter-free probabilistic API mining across GitHub

By Jaroslav M. Fowkes, Charles Sutton. 2015

99 citations recorded by the source corpus.

Existing API mining algorithms can be difficult to use as they require expensive parameter tuning and the returned set of API calls can be large, highly redundant and difficult to understand. To address this, we present PAM (Probabilistic API Miner), a near parameter-free probabilistic algorithm for mining the most interesting API call patterns. We show that PAM significantly outperforms both MAPO and UPMiner, achieving 69% test-set precision, at retrieving relevant API call sequences from GitHub. Moreover, we focus on libraries for which the developers have explicitly provided code examples, yielding over 300,000 LOC of hand-written API example code from the 967 client projects in the data set. This evaluation suggests that the hand-written examples actually have limited coverage of real API usages.

Subfields: Computer Networks and Communications, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Adam A. Porter
2. Jeffrey S. Foster
3. Ugur Koc
4. Parsa Saadatpanah

In Information Systems

1. Adam A. Porter
2. Jeffrey S. Foster
3. Ugur Koc
4. Parsa Saadatpanah

Citations in the corpus

• Learning a classifier for false positive error reports emitted by static code analysis tools (2017, methodology): “…there have been many studies using statistical language models for handling important software development and maintenance tasks (Nguyen et al. 2013; Tu et al. 2014; Raychev et al. 2014, 2015; Allamanis et al. 2015, 2016; Fowkes and Sutton 2016; Gu et al. 2016; White et al. 2016; Dam et al. 2016).”