ARist: An effective API argument recommendation approach

An Information-theoretic Approach for Argument Interpretation

We describe an information-theoretic argument-interpretation mechanism embedded in an interactive system. Our mechanism receives as input an argument entered through a web interface. It generates candidate interpretations in terms of its underlying knowledge representation – a Bayesian network, and applies the Minimum Message Length principle to select the best candidate. The results of our pre...

An Approach for Solving Fuzzy Fredholm Integro-Difference Equations with Mixed Argument

Establishing an Argument-Based Validity Approach for a Low-Stake Test of Collocational Behavior

Most of the validation studies conducted across varying test application contexts are usually framed within the traditional conceptualization of validity and therefore lack a comprehensive framework to focus on test score interpretations and test score use. This study aimed at developing and validating a collocational behavior test (CBT), drawing on Kane's argument-based approach to validity. F...

Using Change Context with Statistical Learning for API Code Recommendation

Learning and remembering how to use APIs is hard. While codecompletion tools list all the API methods available on a given object, reading through a long list of API method names and their associated documentation is tedious, and users can be easily overloaded with too many suggestions. While several researchers proposed techniques for recommending APIs, their accuracy is low. We present a nove...

Personal Web API Recommendation Using Network-based Inference

In this paper, we evaluate a generic network-based inference algorithm for Web API recommendation. Based on experimental data collected from the Programmable Web repository, we construct two tripartite networks: one where the nodes are Web APIs, users and mashups, and another where the nodes are Web APIs, users and tags. Experimental results show that the network-based inference algorithm yield...