/*icomment: bugs or bad comments?*/

By Lin Tan, Ding Yuan, Gopal Krishna, Yuanyuan Zhou. Symposium on Operating Systems Principles, 2007

328 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/d8138bcf285547b1b0939c9ad86ecd97a1b82621

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Lin Tan
2. Emad Shihab
3. Aniket Potdar
4. Wei Yang
5. Andreas Zeller
6. Tao Xie
7. Foutse Khomh
8. William Enck

In Information Systems

1. Michael Pradel
2. Lin Tan
3. Jibesh Patra
4. Rabee Sohail Malik
5. Martin C. Rinard
6. Fan Long
7. Andreas Zeller
8. Song Wang

In Software

1. Michael Pradel
2. Jibesh Patra
3. Martin C. Rinard
4. Fan Long
5. Lin Tan
6. Rabee Sohail Malik
7. Song Wang
8. Tao Xie

Citations in the corpus

• An Empirical Study of C++ Vulnerabilities in Crowd-Sourced Code Examples (2019, methodology): “We chose rules because unlike keyword-based searching, rules are less susceptible to false positive [72], [73].”
• Checking app behavior against app descriptions (2014, background): “Lin Tan et al. [24] extract implicit program rules from program corpora and use these rules to automatically detect inconsistencies between comments and source code, indicating either bugs or bad comments.”
• NL2Type: Inferring JavaScript Function Types from Natural Language Information (2019, background): “…outperforms both of them. c) Analysis of Comments: Prior work on analyzing comments focuses on finding inconsistencies between comments and code [36]–[38], on inferring executable specifications for a method [39], on identifying comments that have textual references to identifier names [40], on…”
• WHYPER: Towards Automating Risk Assessment of Mobile Applications (2013, methodology): “There are existing approaches [33, 45–47] that apply either NLP or a mix of NLP and machine learning algorithms to infer specifications from the natural-language elements such as code comments, API descriptions, and formal requirements documents.”
• Where should I comment my code? A dataset and model for predicting locations that need comments (2020, background): “There is also work on translating comments into assertions (comments to code) [1, 3, 5, 10] and/or detecting inconsistency between comments and code [12–14].”
• Detecting large-scale system problems by mining console logs (2009, unknown): “proposed a novel approach to detect inconsistencies between textual comments and the program logic [31].”
• Bugram: Bug detection with n-gram language models (2016, background): “Rule-based bug detection approaches infer likely programming rules from source code [1, 4, 6, 8, 23, 24, 42, 45, 46, 54], version histories [4, 19, 53], and source code comments [43,44].”
• Deep Just-In-Time Inconsistency Detection Between Comments and Source Code (2020, methodology): “Furthermore, in contrast to previous studies tailored to a specific type of tag (Zhou et al. 2017; Tan et al. 2012) or specific types of keywords and templates (Tan et al. 2007; Tan, Zhou, and Padioleau 2011), we simultaneously consider multiple comment types with diverse characteristics.”
• From Natural Language Specifications to Program Input Parsers (2013, background): “Examples include analyzing API documents to infer API li- brary specifications (Zhong et al., 2009; Pandita et al., 2012) and analyzing code comments to detect concurrency bugs (Tan et al., 2007; Tan et al., 2011).”
• An Exploratory Study on Self-Admitted Technical Debt (2014, methodology): “[6] developed natural language processing tools to find commentbug inconsistencies.”