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ICPC 2020
Mon 13 - Wed 15 July 2020
co-located with ICSE 2020
Tue 14 Jul 2020 07:15 - 07:30 at ICPC - Session 5: For Researchers Chair(s): Bin Lin

Searching and reusing existing code from a large-scale codebase, e.g, GitHub, can help developers complete a programming task efficiently. Recently, Gu et al. proposed a deep learning-based model (i.e., DeepCS), which significantly outperformed prior models. The DeepCS embedded codebase and natural language queries into vectors by two LSTM (long and short-term memory) models separately, and returned developers the code with higher similarity to a code search query. However, such embedding method learned two isolated representations for code and query but ignored their internal semantic correlations. As a result, the learned isolated representations of code and query may limit the effectiveness of code search. To address the aforementioned issue, we propose a co-attentive representation learning model, i.e., Co-Attentive Representation Learning Code Search-CNN (CARLCS-CNN). CARLCS-CNN learns interdependent representations for the embedded code and query with a co-attention mechanism. Generally, such mechanism learns a correlation matrix between embedded code and query, and coattends their semantic relationship via row/column-wise max-pooling. In this way, the semantic correlation between code and query can directly affect their individual representations. We evaluate the effectiveness of CARLCS-CNN on Gu et al.’s dataset with 10k queries. Experimental results show that the proposed CARLCS-CNN model significantly outperforms DeepCS by 26.72% in terms of MRR (mean reciprocal rank). Additionally, CARLCS-CNN is five times faster than DeepCS in model training and four times in testing.

Tue 14 Jul
Times are displayed in time zone: (UTC) Coordinated Universal Time change

07:00 - 08:00: Session 5: For ResearchersResearch / ERA / Tool Demonstration at ICPC
Chair(s): Bin LinUniversità della Svizzera italiana (USI)
07:00 - 07:15
A Literature Review of Automatic Traceability Links Recovery for Software Change Impact Analysis
Thazin Win Win AungUniversity of Technology Sydney, Yulei SuiUniversity of Technology Sydney, Australia, Huan HuoUniversity of Technology Sydney
Media Attached
07:15 - 07:30
Improving Code Search with Co-Attentive Representation Learning
Jianhang ShuaiSchool of Big Data & Software Engineering, Chongqing University, Ling XuSchool of Big Data & Software Engineering, Chongqing University, Chao LiuZhejiang University, Meng YanSchool of Big Data & Software Engineering, Chongqing University, Xin XiaMonash University, Yan LeiSchool of Big Data & Software Engineering, Chongqing University
Media Attached
07:30 - 07:45
OpenSZZ: A Free, Open-Source, Web-Accessible Implementation of the SZZ Algorithm
Tool Demonstration
Valentina LenarduzziLUT University, Fabio PalombaUniversity of Salerno, Davide TaibiTampere University, Damian Andrew TamburriJheronimus Academy of Data Science
Media Attached
07:45 - 08:00
Staged Tree Matching for Detecting Code Move across Files
Akira Fujimoto Osaka University, Yoshiki HigoOsaka University, Junnosuke Matsumoto, Shinji KusumotoOsaka University
Media Attached