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2013/11/06, MC103, *1:30 - 2*

Discovering Information Relevant to API Elements Using Text Classiļ¬cation
Gayane Petrosyan , McGill, SOCS

Area: Software Engineering/Natural Language Processing

Abstract:

The number and the size of Application Programming Interfaces (APIs) are continuously growing. Applications become increasingly dependent on APIs. For example, Java SE 6, which is the core of all Java applications, contains 3774 classes and 203 packages. Programmers, both novice and experienced, are faced to the problem of learning about the vast number of APIs. Given the time at their disposal, it is not possible for programmers to learn all the APIs they need in depth. During this seminar I will describe a technique for discovering relevant sections of an API tutorial to help programmers to find additional related information about API elements they are interested in. The suggested technique automatically analyses API tutorials to assess the usefulness of the information contained therein using Natural Language Processing and Text Classification methods. Afterwards, I will present the results of experiments and the lessons learnt from this work.