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A Survey of Automatic Generation of Source Code Comments: Algorithms and Techniques
As an integral part of source code files, code comments help improve pro...
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9.6 Million Links in Source Code Comments: Purpose, Evolution, and Decay
Links are an essential feature of the World Wide Web, and source code re...
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Source Code Comments: Overlooked in the Realm of Code Clone Detection
Reusing code can produce duplicate or near-duplicate code clones in code...
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Norm violation in online communities – A study of Stack Overflow comments
Norms are behavioral expectations in communities. Online communities are...
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Code Attention: Translating Code to Comments by Exploiting Domain Features
Appropriate comments of code snippets provide insight for code functiona...
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Predicting Usefulness of Code Review Comments using Textual Features and Developer Experience
Although peer code review is widely adopted in both commercial and open ...
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Automatic Generation of Text Descriptive Comments for Code Blocks
We propose a framework to automatically generate descriptive comments fo...
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Recommending Insightful Comments for Source Code using Crowdsourced Knowledge
Recently, automatic code comment generation is proposed to facilitate program comprehension. Existing code comment generation techniques focus on describing the functionality of the source code. However, there are other aspects such as insights about quality or issues of the code, which are overlooked by earlier approaches. In this paper, we describe a mining approach that recommends insightful comments about the quality, deficiencies or scopes for further improvement of the source code. First, we conduct an exploratory study that motivates crowdsourced knowledge from Stack Overflow discussions as a potential resource for source code comment recommendation. Second, based on the findings from the exploratory study, we propose a heuristic-based technique for mining insightful comments from Stack Overflow Q & A site for source code comment recommendation. Experiments with 292 Stack Overflow code segments and 5,039 discussion comments show that our approach has a promising recall of 85.42 also conducted a complementary user study which confirms the accuracy and usefulness of the recommended comments.
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