• Corpus ID: 238582919

Learning to Describe Solutions for Bug Reports Based on Developer Discussions

  title={Learning to Describe Solutions for Bug Reports Based on Developer Discussions},
  author={Sheena Panthaplackel and Junyi Jessy Li and Milo{\vs} Gligori{\'c} and Raymond J. Mooney},
When a software bug is reported, developers engage in a discussion to collaboratively resolve it. While the solution is likely formulated within the discussion, it is often buried in a large amount of text, making it difficult to comprehend, which delays its implementation. To expedite bug resolution, we propose generating a concise natural language description of the solution by synthesizing relevant content within the discussion, which encompasses both natural language and source code… 


BugSum: Deep Context Understanding for Bug Report Summarization
This paper proposes a novel unsupervised approach based on deep learning network, called BugSum, which integrates an auto-encoder network for feature extraction with a novel metric (believability) to measure the degree to which a sentence is approved or disapproved within discussions.
Automatic Summarization of Bug Reports
It is found that summaries helped the study participants save time, that there was no evidence that accuracy degraded when summaries were used and that most participants preferred working with summaries to working with original bug reports.
Summarizing Source Code using a Neural Attention Model
This paper presents the first completely datadriven approach for generating high level summaries of source code, which uses Long Short Term Memory (LSTM) networks with attention to produce sentences that describe C# code snippets and SQL queries.
Learning to Update Natural Language Comments Based on Code Changes
This work proposes an approach that learns to correlate changes across two distinct language representations, to generate a sequence of edits that are applied to the existing comment to reflect the source code modifications.
Deep Code Comment Generation
DeepCom applies Natural Language Processing (NLP) techniques to learn from a large code corpus and generates comments from learned features for better comments generation of Java methods.
Unsupervised Deep Bug Report Summarization
The approach, called DeepSum, is a novel stepped auto-encoder network with evaluation enhancement and predefined fields enhancement modules, which successfully integrates the bug report characteristics into a deep neural network, which achieves the state-of-the-art performance.
Commit Message Generation for Source Code Changes
This paper first extracts both code structure and code semantics from the source code changes, and then jointly model these two sources of information so as to better learn the representations of the code changes.
Associating Natural Language Comment and Source Code Entities
A binary classifier and a sequence labeling model is developed by crafting a rich feature set which encompasses various aspects of code, comments, and the relationships between them and shows that these systems outperform several baselines learning from the proposed supervision.
Multi-layered approach for recovering links between bug reports and fixes
MLink is a multi-layered approach that takes into account not only textual features but also source code features of the changed code corresponding to the commit logs, and is capable of learning the association relations between the terms in bug reports and the names of entities/components in the changed source code of the commits from the established bug-to-fix links.
On Multi-Modal Learning of Editing Source Code
MODIT, a multi-modal NMT based code editing engine, is built that shows that developers’ hint as an input modality can narrow the search space for patches and outperform state-of-the-art models to generate correctly patched code in top-1 position.