CC2Vec: Distributed Representations of Code Changes
Abstrak
Existing work on software patches often use features specific to a single task. These works often rely on manually identified features, and human effort is required to identify these features for each task. In this work, we propose CC2Vec, a neural network model that learns a representation of code changes guided by their accompanying log messages, which represent the semantic intent of the code changes. CC2Vec models the hierarchical structure of a code change with the help of the attention mechanism and uses multiple comparison functions to identify the differences between the removed and added code. To evaluate if CC2Vec can produce a distributed representation of code changes that is general and useful for multiple tasks on software patches, we use the vectors produced by CC2Vec for three tasks: log message generation, bug fixing patch identification, and just-in-time defect prediction. In all tasks, the models using CC2Vec outperform the state-of-the-art techniques.
Topik & Kata Kunci
Penulis (4)
Thong Hoang
Hong Jin Kang
J. Lawall
David Lo
Akses Cepat
- Tahun Terbit
- 2020
- Bahasa
- en
- Total Sitasi
- 228×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1145/3377811.3380361
- Akses
- Open Access ✓