Saul Schleimer, Daniel S Wilkerson, and Alex Aiken.
Paragraph vector code code#
Keep Code Review from Wasting Everyone’s Time: Code Climate.
Paragraph vector code software#
In Proceedings of the 11th Working Conference on Mining Software Repositories. Mining StackOverflow to turn the IDE into a self-confident programming prompter. Luca Ponzanelli, Gabriele Bavota, Massimiliano Di Penta, Rocco Oliveto, and Michele Lanza.In Proceedings of the 2013 International Conference on Software Engineering. Luca Ponzanelli, Alberto Bacchelli, and Michele Lanza.Scikit-learn: Machine Learning in Python. The promise repository of empirical software engineering data. Tim Menzies, Bora Caglayan, Ekrem Kocaguneli, Joe Krall, Fayola Peters, and Burak Turhan.IEEE Transactions on software Engineering4 (1976), 308–320. In Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations. The Stanford CoreNLP natural language processing toolkit. Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, and David McClosky.In International conference on machine learning. Distributed representations of sentences and documents. ACM Transactions on Software Engineering and Methodology (TOSEM) 29, 2(2020), 1–35. A Defect Estimator for Source Code: Linking Defect Reports with Programming Constructs Usage Metrics. In Eighth international AAAI conference on weblogs and social media. Vader: A parsimonious rule-based model for sentiment analysis of social media text. IEEE Transactions on software Engineering 22, 4 (1996), 267–271. Chidamber and Kemerer’s metrics suite: a measurement theory perspective. Document embedding with paragraph vectors. In 2009 IEEE 17th International Conference on Program Comprehension. Syntax tree fingerprinting for source code similarity detection. Michel Chilowicz, Etienne Duris, and Gilles Roussel.The masking and swamping effects using the planted mean-shift outliers models. Code2Vec: Learning Distributed Representations of Code. Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav.In 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Vol. 1.
Paragraph vector code for android#
Droidcc: A scalable clone detection approach for android applications to detect similarity at source code level. Junaid Akram, Zhendong Shi, Majid Mumtaz, and Ping Luo.CRUSO-P achieves the highest mean accuracy score of 99.6% when tested with the C programming language, thus achieving an improvement of 5.6% over the existing method. ĬRUSO-P outperforms CRUSO with an improvement of 97.82% in response time and a storage reduction of 99.15%. For a given input source code, CRUSO-P labels it as.
![paragraph vector code paragraph vector code](https://i.pinimg.com/originals/ab/bf/89/abbf89b96ea1e61cfcf450eb202e33b5.png)
The significant contributions of our paper are i) SOpostsDB: a dataset containing the PVA vectors and the SO posts information, ii) CRUSO-P: a code review assisting system based on PVA models trained on SOpostsDB. The central idea of the approach is to estimate the defectiveness for an input source code by using the defectiveness score of similar code fragments present in various StackOverflow (SO) posts.
![paragraph vector code paragraph vector code](https://scoreintl.org/wp-content/uploads/2019/12/DSC_3447-1-1024x683.jpg)
![paragraph vector code paragraph vector code](https://scoreintl.org/wp-content/uploads/2020/06/unnamed-2.jpg)
In this paper, we improve the performance (in terms of speed and memory usage) of our existing code review assisting tool–CRUSO. However, the existing methods are dependent on experts or inefficient. Code reviews are one of the effective methods to estimate defectiveness in source code.