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Deep Just-In-Time Inconsistency Detection Between Comments and Source Code [article]

Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric, Raymond J. Mooney
2020 arXiv   pre-print

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2020 pre-print arXiv:2010.01625v1 fatcat:sm5csirar5eytmnxpn7rqzzmaq
In this paper, we aim to detect whether a comment becomes inconsistent as a result of changes to the corresponding body of code, in order to catch potential inconsistencies just-in-time, i.e., before they  ...  Natural language comments convey key aspects of source code such as implementation, usage, and pre- and post-conditions.  ...  Conclusion We developed a deep learning approach for just-in-time inconsistency detection between code and comments by learning to relate comments and code changes.  ... 
arXiv:2010.01625v2 fatcat:u6eagow32vcvflbjfga4ayhniq
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Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric, Raymond J. Mooney. "Deep Just-In-Time Inconsistency Detection Between Comments and Source Code." arXiv (2020)

Code Comment Inconsistency Detection with BERT and Longformer [article]

Theo Steiner, Rui Zhang
2022 arXiv   pre-print

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However, when the code is modified without an accompanying correction to the comment, an inconsistency between the comment and code can arise, which opens up the possibility for developer confusion and  ...  We further discuss ideas for future research in using pretrained language models for both inconsistency detection and automatic comment updating.  ...  Acknowledgements We thank the 2022 Machine Learning in Cybersecurity Research Experience for Undergraduates (REU) program at Penn State University, funded by National Science Foundation Grant 1950491,  ... 
arXiv:2207.14444v1 fatcat:viy4l22l7nbrpg2e26jk7zjisa
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Theo Steiner, Rui Zhang. "Code Comment Inconsistency Detection with BERT and Longformer." arXiv (2022)

An Exploratory Study of Copyright Inconsistency in the Linux Kernel

Shi QIU, Daniel M. GERMAN, Katsuro INOUE
2021 IEICE transactions on information and systems  

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For each inconsistency, we manually check the commit logs and the comments in the source code of the source files to find out the reasons.  ...  To achieve this goal, for the holder-notcommitter inconsistency, we manually check the commit logs and the comments in the source code of all 134 source files detected as having the holder-not-committer  ...  His research interests include software engineering, especially software maintenance, software reuse, empirical approach, program analysis, code clone detection, and software license/copyright analysis  ... 
doi:10.1587/transinf.2020edp7107 fatcat:lnwjrypxw5hxtf5cqh3t2n5zdu
Citation

Shi QIU, Daniel M. GERMAN, Katsuro INOUE. "An Exploratory Study of Copyright Inconsistency in the Linux Kernel." IEICE transactions on information and systems (2021) 254-263

A Survey of Automatic Generation of Source Code Comments: Algorithms and Techniques

Xiaotao Song, Hailong Sun, Xu Wang, Jiafei Yan
2019 IEEE Access  

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2019 pre-print
arXiv:1907.10863v1 fatcat:xya3gxmqwvefxezchbqq5py554
As an integral part of source code files, code comments help improve program readability and comprehension.  ...  As a result, code comments can be inadequate, absent or even mismatched with source code, which affects the understanding, reusing and the maintenance of software.  ...  Basing on the related studies of eye-tracking in program comprehension, they detect eye movements and the amount of gaze time which programmers spend in scanning source code.  ... 
doi:10.1109/access.2019.2931579 fatcat:gzwjs6wnerec3nlciqmrvpbsz4
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Xiaotao Song, Hailong Sun, Xu Wang, Jiafei Yan. "A Survey of Automatic Generation of Source Code Comments: Algorithms and Techniques." IEEE Access (2019) 1-1

Characterizing logging practices in open-source software

Ding Yuan, Soyeon Park, Yuanyuan Zhou
2012 2012 34th International Conference on Software Engineering (ICSE)  

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To demonstrate the benefit of our study, we built a simple checker based on one of our findings and effectively detected 138 pieces of new problematic logging code from studied software (24 of them are  ...  By examining developers' own modifications to the logging code in the revision history, we find that they often do not make the log messages right in their first attempts, and thus need to spend a significant  ...  This research is supported by NSF CNS-0720743 grant, NSF CCF-0325603 grant, NSF CNS-0615372 grant, NSF CNS-0347854 (career award), NSF CSR Small 1017784 grant and NetApp Gift grant.  ... 
doi:10.1109/icse.2012.6227202 dblp:conf/icse/YuanPZ12 fatcat:7yqw3sqs3zdk3fwm5erhaii7yu
Citation

Ding Yuan, Soyeon Park, Yuanyuan Zhou. "Characterizing logging practices in open-source software." 2012 34th International Conference on Software Engineering (ICSE) (2012) 102-112

A New Family of Software Anti-patterns: Linguistic Anti-patterns

V. Arnaoudova, M. Di Penta, G. Antoniol, Y.-G Gueheneuc
2013 2013 17th European Conference on Software Maintenance and Reengineering  

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and (ii) between attribute names, types, and comments.  ...  Recent and past studies have shown that poor source code lexicon negatively affects software understandability, maintainability, and, overall, quality.  ...  Consequences Without a deep analysis of the source code, the developer might not clearly understand the role of the attribute, and the comment is just misleading. III.  ... 
doi:10.1109/csmr.2013.28 dblp:conf/csmr/ArnaoudovaPAG13 fatcat:5vmrh4eogfauxbh77kk3ss7qse
Citation

V. Arnaoudova, M. Di Penta, G. Antoniol, Y.-G Gueheneuc. "A New Family of Software Anti-patterns: Linguistic Anti-patterns." 2013 17th European Conference on Software Maintenance and Reengineering (2013) 187-196

Where should I comment my code?

Annie Louis, Santanu Kumar Dash, Earl T. Barr, Michael D. Ernst, Charles Sutton
2020 Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: New Ideas and Emerging Results  

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This first success opens the door to future work, both in the new where-to-comment problem and in guiding comment generation. Our code and data is available at  ...  Our models achieved precision of 74% and recall of 13% in identifying comment-worthy locations.  ...  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] [13] [14] .  ... 
doi:10.1145/3377816.3381736 dblp:conf/icse/LouisDBES20 fatcat:los7ywzibjh3becee2xizkirdq
Citation

Annie Louis, Santanu Kumar Dash, Earl T. Barr, Michael D. Ernst, Charles Sutton. "Where should I comment my code?." Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: New Ideas and Emerging Results (2020) 21-24

Suboptimal Comments in Java Projects: From Independent Comment Changes to Commenting Practices

Chao Wang, Hao He, Uma Pal, Darko Marinov, Minghui Zhou
2022 ACM Transactions on Software Engineering and Methodology  

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We name such source code comments as suboptimal comments. Such suboptimal comments create challenges in code comprehension and maintenance.  ...  High-quality source code comments are valuable for software development and maintenance, however, code often contains low-quality comments or lacks them altogether.  ...  ACKNOWLEDGMENTS We thank Milos Gligoric, Haiqiao Gu, Pengcheng Li, Zanpeng Ma, Pengyu Nie, Xin Tan, and Yuxia Zhang for piloting our survey/questionnaire, and Julia Rubin for comments on a paper draft.  ... 
doi:10.1145/3546949 fatcat:xfdllo2i45fzfhku4iedcnpyz4
Citation

Chao Wang, Hao He, Uma Pal, Darko Marinov, Minghui Zhou. "Suboptimal Comments in Java Projects: From Independent Comment Changes to Commenting Practices." ACM Transactions on Software Engineering and Methodology (2022)

Exploiting Token and Path-based Representations of Code for Identifying Security-Relevant Commits [article]

Achyudh Ram, Ji Xin, Meiyappan Nagappan, Yaoliang Yu, Rocío Cabrera Lozoya, Antonino Sabetta, Jimmy Lin
2019 arXiv   pre-print

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Public vulnerability databases such as CVE and NVD account for only 60% of security vulnerabilities present in open-source projects, and are known to suffer from inconsistent quality.  ...  We propose novel hierarchical deep learning models for the identification of security-relevant commits from either the commit diff or the source code for the Java classes.  ...  Acknowledgments We would like to thank SAP and NSERC for their support towards this project.  ... 
arXiv:1911.07620v1 fatcat:r5aebrjw7vfhble4k3osfsegui
Citation

Achyudh Ram, Ji Xin, Meiyappan Nagappan, Yaoliang Yu, Rocío Cabrera Lozoya, Antonino Sabetta, Jimmy Lin. "Exploiting Token and Path-based Representations of Code for Identifying Security-Relevant Commits." arXiv (2019)

Clones in Deep Learning Code: What, Where, and Why? [article]

Hadhemi Jebnoun, Md Saidur Rahman, Foutse Khomh, Biruk Asmare Muse
2021 arXiv   pre-print

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Our results show that code cloning is a frequent practice in deep learning systems and that deep learning developers often clone code from files in distant repositories in the system.  ...  We also study the correlation between bugs and code clones to assess the impacts of clones on the quality of the studied systems.  ...  Amin Nikanjam for his valuable comments on the manuscript.  ... 
arXiv:2107.13614v1 fatcat:ndks7o25orfnzlif5dmmew7pmu
Citation

Hadhemi Jebnoun, Md Saidur Rahman, Foutse Khomh, Biruk Asmare Muse. "Clones in Deep Learning Code: What, Where, and Why?." arXiv (2021)

Fuzzing Deep-Learning Libraries via Automated Relational API Inference [article]

Yinlin Deng, Chenyuan Yang, Anjiang Wei, Lingming Zhang
2022 arXiv   pre-print

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Also, besides the 162 code bugs, we have also detected 14 documentation bugs (all confirmed).  ...  To date, DeepREL has detected 162 bugs in total, with 106 already confirmed by the developers as previously unknown bugs.  ...  Also, besides the 162 code bugs, we were also able to detect 14 documentation bugs (all confirmed).  ... 
arXiv:2207.05531v1 fatcat:papv43ussjaq7j32i5m3y37nm4
Open Access
Citation

Yinlin Deng, Chenyuan Yang, Anjiang Wei, Lingming Zhang. "Fuzzing Deep-Learning Libraries via Automated Relational API Inference." arXiv (2022)

Code Word Detection in Fraud Investigations using a Deep-Learning Approach [article]

Youri van der Zee, Jan C. Scholtes, Marcel Westerhoud, Julien Rossi
2021 arXiv   pre-print

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With this result, we demonstrate that deep neural language models can reliably (F1 score of 0.9) be applied in fraud investigations for the detection of code words.  ...  In addition, fraudsters may use deception to hide their behaviour and intentions by using code words.  ...  Acknowledgements The authors are grateful for the extensive support obtained for this research from ZyLAB Technologies BV and Ebben Partners BV, both based in the Netherlands.  ... 
arXiv:2103.09606v1 fatcat:dxswtjtlhfhfdhg3ekfkf3m6ci
Open Access
Citation

Youri van der Zee, Jan C. Scholtes, Marcel Westerhoud, Julien Rossi. "Code Word Detection in Fraud Investigations using a Deep-Learning Approach." arXiv (2021)

Architecture consistency: State of the practice, challenges and requirements

Nour Ali, Sean Baker, Ross O'Crowley, Sebastian Herold, Jim Buckley
2017 Empirical Software Engineering  

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These barriers are: 1) Difficulty in quantifying architectural inconsistency effects, and thus justifying the allocation of resources to fix them to senior management, 2) The near invisibility of architectural  ...  inconsistency to customers, 3) Practitioners' reluctance towards fixing architectural inconsistencies, and 4) Practitioners perception that huge effort is required to map the system to the architecture  ...  reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made  ... 
doi:10.1007/s10664-017-9515-3 fatcat:7t4xxleajbgbzlzfqirvx6ul6m
Citation

Nour Ali, Sean Baker, Ross O'Crowley, Sebastian Herold, Jim Buckley. "Architecture consistency: State of the practice, challenges and requirements." Empirical Software Engineering 23.1 (2017) 224-258

A Survey on Deep Learning for Software Engineering [article]

Yanming Yang, Xin Xia, David Lo, John Grundy
2020 arXiv   pre-print

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We first provide an example to illustrate how deep learning techniques are used in SE. We then summarize and classify different deep learning techniques used in SE.  ...  We analyzed key optimization technologies used in these deep learning models, and finally describe a range of key research topics using DNNs in SE.  ...  [18] used a tree-based LSTM network, which can directly match with the AST of programs for capturing multiple levels of the semantics of source code. Just-In-Time (JIT) defect prediction.  ... 
arXiv:2011.14597v1 fatcat:pcyg6zbnm5bc3g4yhjomcnye3y
Open Access
Citation

Yanming Yang, Xin Xia, David Lo, John Grundy. "A Survey on Deep Learning for Software Engineering." arXiv (2020)

IDEAL: An Open-Source Identifier Name Appraisal Tool [article]

Anthony Peruma, Venera Arnaoudova, Christian D. Newman
2021 arXiv   pre-print

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Developers must comprehend the code they will maintain, meaning that the code must be legible and reasonably self-descriptive.  ...  IDEAL is open-source and publicly available, with a demo video available at: https://youtu.be/fVoOYGe50zg  ...  While IDEAL correctly detects these terms in the source code, how the developer utilizes the term in a name or comment is currently a challenge.  ... 
arXiv:2107.08344v1 fatcat:7cpxjba6knaunodgjydfmwpgsu
Open Access
Citation

Anthony Peruma, Venera Arnaoudova, Christian D. Newman. "IDEAL: An Open-Source Identifier Name Appraisal Tool." arXiv (2021)

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