Automatic recommendation of API methods from feature requests

Ferdian Thung 1 Shaowei Wang 1 David Lo 1 Julia Lawall 2
2 Regal - Large-Scale Distributed Systems and Applications
LIP6 - Laboratoire d'Informatique de Paris 6, Inria Paris-Rocquencourt
Abstract : Developers often receive many feature requests. To implement these features, developers can leverage various methods from third party libraries. In this work, we propose an automated approach that takes as input a textual description of a feature request. It then recommends methods in library APIs that developers can use to implement the feature. Our recommendation approach learns from records of other changes made to software systems, and compares the textual description of the requested feature with the textual descriptions of various API methods. We have evaluated our approach on more than 500 feature requests of Axis2/Java, CXF, Hadoop Common, HBase, and Struts 2. Our experiments show that our approach is able to recommend the right methods from 10 libraries with an average recall-rate@5 of 0.690 and recall-rate@10 of 0.779 respectively. We also show that the state-of-the-art approach by Chan et al., that recommends API methods based on precise text phrases, is unable to handle feature requests.
Type de document :
Communication dans un congrès
Ewen Denney; Tevfik Bultan; Andreas Zeller. ASE 2013 - 28th IEEE/ACM International Conference on Automated Software Engineering, Nov 2013, Palo Alto, California, United States. IEEE, pp.290-300, 2013, 〈10.1109/ASE.2013.6693088〉
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https://hal.inria.fr/hal-00918828
Contributeur : Julia Lawall <>
Soumis le : dimanche 15 décembre 2013 - 13:45:42
Dernière modification le : vendredi 25 mai 2018 - 12:02:03

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Ferdian Thung, Shaowei Wang, David Lo, Julia Lawall. Automatic recommendation of API methods from feature requests. Ewen Denney; Tevfik Bultan; Andreas Zeller. ASE 2013 - 28th IEEE/ACM International Conference on Automated Software Engineering, Nov 2013, Palo Alto, California, United States. IEEE, pp.290-300, 2013, 〈10.1109/ASE.2013.6693088〉. 〈hal-00918828〉

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