Add two papers and remove duplicate

Author: mallamanis

_publications/chakraborty2018tree2tree.markdown

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+---
+layout: publication
+title: "Tree2Tree Neural Translation Model for Learning Source Code Changes"
+authors: S. Chakraborty, M. Allamanis, B. Ray
+conference: 
+year: 2018
+bibkey: chakraborty2018tree2tree
+---
+The way developers edit day-to-day code tend to be repetitive and often use existing code elements. Many researchers tried to automate this tedious task of code changes by learning from specific change templates and applied to limited scope. The advancement of Neural Machine Translation (NMT) and the availability of the vast open source software evolutionary data open up a new possibility of automatically learning those templates from the wild. However, unlike natural languages, for which NMT techniques were originally designed, source code and the changes have certain properties. For instance, compared to natural language source code vocabulary can be virtually infinite. Further, any good change in code should not break its syntactic structure. Thus, deploying state-of-the-art NMT models without domain adaptation may poorly serve the purpose. To this end, in this work, we propose a novel Tree2Tree Neural Machine Translation system to model source code changes and learn code change patterns from the wild. We realize our model with a change suggestion engine: CODIT. We train the model with more than 30k real-world changes and evaluate it with 6k patches. Our evaluation shows the effectiveness of CODIT in learning and suggesting abstract change templates. CODIT also shows promise in suggesting concrete patches and generating bug fixes. 

_publications/gu2017deepam.markdown

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+---
+layout: publication
+title: "DeepAM: Migrate APIs with Multi-modal Sequence to Sequence Learning"
+authors: X. Gu, H. Zhang, D. Zhang, S. Kim
+conference: IJCAI
+year: 2017
+bibkey: gu2017deepam
+---
+Computer programs written in one language are often required to be ported to other languages to support multiple devices and environments. When programs use language specific APIs (Application Programming Interfaces), it is very challenging to migrate these APIs to the corresponding APIs written in other languages. Existing approaches mine API mappings from projects that have corresponding versions in two languages. They rely on the sparse availability of bilingual projects, thus producing a limited number of API mappings. In this paper, we propose an intelligent system called DeepAM for automatically mining API mappings from a large-scale code corpus without bilingual projects. The key component of DeepAM is based on the multimodal sequence to sequence learning architecture that aims to learn joint semantic representations of bilingual API sequences from big source code data. Experimental results indicate that DeepAM significantly increases the accuracy of API mappings as well as the number of API mappings, when compared with the state-of-the-art approaches.