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The advancements in machine learning techniques have encouraged researchers to apply these techniques to a myriad of software engineering tasks that use source code analysis, such as testing and vulnerability detection.
A complexity measure
Thomas J McCabe. 1976 · 1976
Earlier work this paper cites.
Elements of Software Science (Operating and Programming Systems Series)
Maurice H. Halstead. 1977 · 1977
Earlier work this paper cites.
A Metrics Suite for Object Oriented Design
S. R. Chidamber and C. F. Kemerer. 1994 · 1994
Earlier work this paper cites.
Design Patterns: Elements of Reusable Object-Oriented Software (1st ed.)
Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides. 1994 · 1994
Earlier work this paper cites.
Handbook of Software Reliability Engineering
Michael R. Lyu (Ed.). 1996 · 1996
Earlier work this paper cites.
What’s the Code? Automatic Classification of Source Code Archives. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Edmonton, Alberta, Canada) (KDD ’02) . 632–638
Secil Ugurel, Robert Krovetz, and C. Lee Giles. 2002 · 2002
Earlier work this paper cites.
Machine Learning and Software Engineering
Du Zhang and Jeffrey J. P. Tsai. 2003 · 2003
Earlier work this paper cites.
Does active learning work? A review of the research
Michael Prince. 2004 · 2004
Earlier work this paper cites.
The PROMISE Repository of Software Engineering Databases
J. Sayyad Shirabad and T.J. Menzies. 2005 · 2005
Earlier work this paper cites.
Theories, methods and tools in program comprehension: past, present and future. In 13th International Workshop on Program Comprehension (IWPC’05) . 181–191
M.-A. Storey. 2005 · 2005
Earlier work this paper cites.
Software Defect Identification Using Machine Learning Techniques. In 32nd EUROMICRO Conference on Software Engineering and Advanced Applications (EUROMICRO’06) . 240–247
E. Ceylan, F. O. Kutlubay, and A. B. Bener. 2006 · 2006
Earlier work this paper cites.
Predicting Defect Densities in Source Code Files with Decision Tree Learners. In Proceedings of the 2006 International Workshop on Mining Software Repositories (Shanghai, China) (MSR ’06) . 119–125
Patrick Knab, Martin Pinzger, and Abraham Bernstein. 2006 · 2006
Earlier work this paper cites.
On the Replicability and Reproducibility of Deep Learning in Software Engineering
Chao Liu, Cuiyun Gao, Xin Xia, David Lo, John Grundy, and Xiaohu Yang. 2020b · 2006
Earlier work this paper cites.
Software Assurance with SAMATE Reference Dataset, Tool Standards, and Studies
Paul E. Black. 2007 · 2007
Earlier work this paper cites.
Applying Machine Learning Techniques for Detection of Malicious Code in Network Traffic. In KI 2007: Advances in Artificial Intelligence , Joachim Hertzberg, Michael Beetz, and Roman Englert (Eds.). 44–50
Yuval Elovici, Asaf Shabtai, Robert Moskovitch, Gil Tahan, and Chanan Glezer. 2007 · 2007
Earlier work this paper cites.
Applying machine learning to software fault-proneness prediction
Iker Gondra. 2008 · 2007
Earlier work this paper cites.
Predicting Buggy Changes inside an Integrated Development Environment. In Proceedings of the 2007 OOPSLA Workshop on Eclipse Technology EXchange (Montreal, Quebec, Canada) (eclipse ’07) . 36–40
Janaki T. Madhavan and E. James Whitehead. 2007 · 2007
Earlier work this paper cites.
Thomas Zimmermann, Rahul Premraj, and Andreas Zeller. 2007 · 2007
Earlier work this paper cites.
EMPIRICAL ASSESSMENT OF MACHINE LEARNING BASED SOFTWARE DEFECT PREDICTION TECHNIQUES
VENKATA UDAYA B. CHALLAGULLA, FAROKH B. BASTANI, I-LING YEN, and RAYMOND A. PAUL. 2008 · 2008
Earlier work this paper cites.
An expert system for determining candidate software classes for refactoring
Yasemin Kosker, Burak Turhan, and Ayse Bener. 2009 · 2008
Earlier work this paper cites.
An Application of Latent Dirichlet Allocation to Analyzing Software Evolution. In 2008 Seventh International Conference on Machine Learning and Applications . 813–818
E. Linstead, C. Lopes, and P. Baldi. 2008 · 2008
Earlier work this paper cites.
Investigating Statistical Machine Learning as a Tool for Software Development. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Florence, Italy) (CHI ’08) . 667–676
Kayur Patel, James Fogarty, James A. Landay, and Beverly Harrison. 2008 · 2008
Earlier work this paper cites.
Detecting Defects with an Interactive Code Review Tool Based on Visualisation and Machine Learning. In SEKE
S. Axelsson, D. Baca, Robert Feldt, Darius Sidlauskas, and Denis Kacan. 2009 · 2009
Earlier work this paper cites.
Learning from Examples to Improve Code Completion Systems. In Proceedings of the 7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on The Foundations of Software Engineering (Amsterdam, The Netherlands) (ESEC/FSE ’09) . 213–222
Marcel Bruch, Martin Monperrus, and Mira Mezini. 2009 · 2009
Earlier work this paper cites.
Code Completion from Abbreviated Input. In 2009 IEEE/ACM International Conference on Automated Software Engineering . 332–343
S. Han, D. R. Wallace, and R. C. Miller. 2009 · 2009
Earlier work this paper cites.
Malicious web content detection by machine learning
Yung-Tsung Hou, Yimeng Chang, Tsuhan Chen, Chi-Sung Laih, and Chia-Mei Chen. 2010 · 2009
Earlier work this paper cites.
Risky Module Estimation in Safety-Critical Software. In 2009 Eighth IEEE/ACIS International Conference on Computer and Information Science . 967–970
Y. Kim, C. Jeong, A. Jeong, and H. S. Kim. 2009 · 2009
Earlier work this paper cites.
Malicious Code Detection Using Active Learning. In Privacy, Security, and Trust in KDD , Francesco Bonchi, Elena Ferrari, Wei Jiang, and Bradley Malin (Eds.). 74–91
Robert Moskovitch, Nir Nissim, and Yuval Elovici. 2009 · 2009
Earlier work this paper cites.
Active learning literature survey
Burr Settles. 2009 · 2009
Earlier work this paper cites.
Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey
Asaf Shabtai, Robert Moskovitch, Yuval Elovici, and Chanan Glezer. 2009 · 2009
Earlier work this paper cites.
Is newer always better? Re-evaluating the benefits of newer pharmaceuticals
Michael R. Law and Karen A. Grépin. 2010 · 2010
Earlier work this paper cites.
UCI Source Code Data Sets
C. Lopes, S. Bajracharya, J. Ossher, and P. Baldi. 2010 · 2010
Earlier work this paper cites.
A Machine Learning Based Tool for Source Code Plagiarism Detection
U. Bandara and G. Wijayarathna. 2011 · 2011
Earlier work this paper cites.
A Genetic Algorithm to Configure Support Vector Machines for Predicting Fault-Prone Components. In Product-Focused Software Process Improvement , Danilo Caivano, Markku Oivo, Maria Teresa Baldassarre, and Giuseppe Visaggio (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 247–261
Sergio Di Martino, Filomena Ferrucci, Carmine Gravino, and Federica Sarro. 2011 · 2011
Earlier work this paper cites.
Code Completion of Multiple Keywords from Abbreviated Input
Sangmok Han, David R. Wallace, and Robert C. Miller. 2011 · 2011
Earlier work this paper cites.
Sample-based software defect prediction with active and semi-supervised learning
M. Li, H. Zhang, Rongxin Wu, and Z. Zhou. 2011 · 2011
Earlier work this paper cites.
Transfer learning for cross-company software defect prediction
Ying Ma, Guangchun Luo, Xue Zeng, and Aiguo Chen. 2012 · 2011
Earlier work this paper cites.
On the applicability of machine learning techniques for object-oriented software fault prediction
Ruchika Malhotra and Yogesh Singh. 2011 · 2011
Earlier work this paper cites.
The Juliet 1.1 C/C++ and Java Test Suite
Frederick Boland and Paul Black. 2012 · 2012
Earlier work this paper cites.
Evaluating Defect Prediction Approaches: A Benchmark and an Extensive Comparison
Marco D’Ambros, Michele Lanza, and Romain Robbes. 2012 · 2012
Earlier work this paper cites.
Toward comprehensible software fault prediction models using bayesian network classifiers
Karel Dejaeger, Thomas Verbraken, and Bart Baesens. 2012 · 2012
Earlier work this paper cites.
The State of Machine Learning Methodology in Software Fault Prediction. In 2012 11th International Conference on Machine Learning and Applications , Vol. 2. 308–313
T. Hall and D. Bowes. 2012 · 2012
Earlier work this paper cites.
ImageNet classification with deep convolutional neural networks. In Advances in neural information processing systems . 1097–1105
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012 · 2012
Earlier work this paper cites.
Assessment of Software Testing Time Using Soft Computing Techniques
Pradeep Kumar and Yogesh Singh. 2012 · 2012
Earlier work this paper cites.
Fault prediction using statistical and machine learning methods for improving software quality
Ruchika Malhotra and Ankita Jain. 2012 · 2012
Earlier work this paper cites.
Software maintainability prediction using machine learning algorithms
Ruchika Malhotra 1 and Anuradha Chug. 2012 · 2012
Earlier work this paper cites.
Mining source code descriptions from developer communications. In 2012 20th IEEE International Conference on Program Comprehension (ICPC) . 63–72
Sebastiano Panichella, Jairo Aponte, Massimiliano Di Penta, Andrian Marcus, and Gerardo Canfora. 2012 · 2012
Earlier work this paper cites.
A Further Analysis on the Use of Genetic Algorithm to Configure Support Vector Machines for Inter-Release Fault Prediction. In Proceedings of the 27th Annual ACM Symposium on Applied Computing (Trento, Italy) (SAC ’12) . Association for Computing Machinery, New York, NY, USA, 1215–1220
F. Sarro, S. Di Martino, F. Ferrucci, and C. Gravino. 2012 · 2012
Earlier work this paper cites.
Using coding-based ensemble learning to improve software defect prediction
Zhongbin Sun, Qinbao Song, and Xiaoyan Zhu. 2012 · 2012
Earlier work this paper cites.
Dissecting Android Malware: Characterization and Evolution. In Proceedings of the 2012 IEEE Symposium on Security and Privacy (SP ’12) . 95–109
Yajin Zhou and Xuxian Jiang. 2012 · 2012
Earlier work this paper cites.
Machine Learning-Based Software Quality Prediction Models: State of the Art. In 2013 International Conference on Information Science and Applications (ICISA) . 1–4
H. A. Al-Jamimi and M. Ahmed. 2013 · 2013
Earlier work this paper cites.
Towards machine learning based design pattern recognition. In 2013 13th UK Workshop on Computational Intelligence (UKCI) . IEEE, 244–251
Sultan Alhusain, Simon Coupland, Robert John, and Maria Kavanagh. 2013 · 2013
Earlier work this paper cites.
Mining source code repositories at massive scale using language modeling. In 2013 10th Working Conference on Mining Software Repositories (MSR) . 207–216
M. Allamanis and C. Sutton. 2013 · 2013
Earlier work this paper cites.
Mining source code repositories at massive scale using language modeling. In 10th Working Conference on Mining Software Repositories (MSR) . 207–216
Miltiadis Allamanis and Charles Sutton. 2013 · 2013
Earlier work this paper cites.
Clonewise – Detecting Package-Level Clones Using Machine Learning. In Security and Privacy in Communication Networks , Tanveer Zia, Albert Zomaya, Vijay Varadharajan, and Morley Mao (Eds.). 197–215
Silvio Cesare, Yang Xiang, and Jun Zhang. 2013 · 2013
Earlier work this paper cites.
Software defect prediction using supervised learning algorithm and unsupervised learning algorithm. In Confluence 2013: The Next Generation Information Technology Summit (4th International Conference) . 173–179
A. Chug and S. Dhall. 2013 · 2013
Earlier work this paper cites.
Code Smell Detection: Towards a Machine Learning-Based Approach. In 2013 IEEE International Conference on Software Maintenance . 396–399
F. A. Fontana, M. Zanoni, A. Marino, and M. V. Mäntylä. 2013 · 2013
Earlier work this paper cites.
The GHTorrent dataset and tool suite. In Proceedings of the 10th Working Conference on Mining Software Repositories (MSR ’13) . IEEE Press, Piscataway, NJ, USA, 233–236
Georgios Gousios. 2013 · 2013
Earlier work this paper cites.
Hybrid speech recognition with deep bidirectional LSTM. In Automatic Speech Recognition and Understanding (ASRU), 2013 IEEE Workshop on . IEEE, 273–278
Alex Graves, Navdeep Jaitly, and Abdel-rahman Mohamed. 2013 · 2013
Earlier work this paper cites.
R2Fix: Automatically generating bug fixes from bug reports. In 2013 IEEE Sixth international conference on software testing, verification and validation . IEEE, 282–291
Chen Liu, Jinqiu Yang, Lin Tan, and Munawar Hafiz. 2013 · 2013
Earlier work this paper cites.
Investigation of relationship between object-oriented metrics and change proneness
Ruchika Malhotra and Megha Khanna. 2013 · 2013
Earlier work this paper cites.
Securing energy metering software with automatic source code correction. In 2013 11th IEEE International Conference on Industrial Informatics (INDIN)
Iberia Medeiros, Nuno F. Neves, and Miguel Correia. 2013 · 2013
Earlier work this paper cites.
Natural Language Models for Predicting Programming Comments
Dana Movshovitz-Attias and William Cohen. 2013 · 2013
Earlier work this paper cites.
A Statistical Semantic Language Model for Source Code. In Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering (Saint Petersburg, Russia) (ESEC/FSE 2013) . Association for Computing Machinery, New York, NY, USA, 532–542
Tung Thanh Nguyen, Anh Tuan Nguyen, Hoan Anh Nguyen, and Tien N. Nguyen. 2013 · 2013
Earlier work this paper cites.
OPEM: A Static-Dynamic Approach for Machine-Learning-Based Malware Detection. In International Joint Conference CISIS’12-ICEUTE´12-SOCO´12 Special Sessions , Álvaro Herrero, Václav Snášel, Ajith Abraham, Ivan Zelinka, Bruno Baruque, Héctor Quintián, José Luis Calvo, Javier Sedano, and Emilio Corchado (Eds.). 271–280
Igor Santos, Jaime Devesa, Félix Brezo, Javier Nieves, and Pablo Garcia Bringas. 2013 · 2013
Earlier work this paper cites.
Using Class Imbalance Learning for Software Defect Prediction
S. Wang and X. Yao. 2013 · 2013
Earlier work this paper cites.
On the Use of Machine Learning and Search-Based Software Engineering for Ill-Defined Fitness Function: A Case Study on Software Refactoring. In Search-Based Software Engineering , Claire Le Goues and Shin Yoo (Eds.). 31–45
Boukhdhir Amal, Marouane Kessentini, Slim Bechikh, Josselin Dea, and Lamjed Ben Said. 2014 · 2014
Earlier work this paper cites.
On Automatically Generating Commit Messages via Summarization of Source Code Changes
Luis Fernando Cortes-Coy, M. Vásquez, Jairo Aponte, and D. Poshyvanyk. 2014 · 2014
Earlier work this paper cites.
Fine-Grained and Accurate Source Code Differencing. In Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering (Vasteras, Sweden) (ASE ’14) . 313–324
Jean-Rémy Falleri, Floréal Morandat, Xavier Blanc, Matias Martinez, and Martin Monperrus. 2014 · 2014
Earlier work this paper cites.
Data-Guided Repair of Selection Statements. In Proceedings of the 36th International Conference on Software Engineering (Hyderabad, India) (ICSE 2014) . 243–253
Divya Gopinath, Sarfraz Khurshid, Diptikalyan Saha, and Satish Chandra. 2014 · 2014
Earlier work this paper cites.
Less is More: Temporal Fault Predictive Performance over Multiple Hadoop Releases. In Search-Based Software Engineering , Claire Le Goues and Shin Yoo (Eds.). Springer International Publishing, Cham, 240–246
Mark Harman, Syed Islam, Yue Jia, Leandro L. Minku, Federica Sarro, and Komsan Srivisut. 2014 · 2014
Earlier work this paper cites.
Dictionary learning based software defect prediction. In Proceedings of the 36th international conference on software engineering . 414–423
Xiao-Yuan Jing, Shi Ying, Zhi-Wu Zhang, Shan-Shan Wu, and Jin Liu. 2014 · 2014
Earlier work this paper cites.
Defects4J: A Database of Existing Faults to Enable Controlled Testing Studies for Java Programs. In Proceedings of the 2014 International Symposium on Software Testing and Analysis (San Jose, CA, USA) (ISSTA 2014) . Association for Computing Machinery, New York, NY, USA, 437–440
René Just, Darioush Jalali, and Michael D. Ernst. 2014 · 2014
Earlier work this paper cites.
Comparative analysis of statistical and machine learning methods for predicting faulty modules
Ruchika Malhotra. 2014 · 2014
Earlier work this paper cites.
Automatic Detection and Correction of Web Application Vulnerabilities Using Data Mining to Predict False Positives. In Proceedings of the 23rd International Conference on World Wide Web (Seoul, Korea) (WWW ’14) . 63–74
Ibéria Medeiros, Nuno F. Neves, and Miguel Correia. 2014 · 2014
Earlier work this paper cites.
Software defect prediction using Bayesian networks
Ahmet Okutan and Olcay Taner Yıldız. 2014 · 2014
Earlier work this paper cites.
On software defect prediction using machine learning
Jinsheng Ren, Ke Qin, Ying Ma, and Guangchun Luo. 2014 · 2014
Earlier work this paper cites.
Web Application Vulnerability Prediction Using Hybrid Program Analysis and Machine Learning
L. K. Shar, L. C. Briand, and H. B. K. Tan. 2015 · 2014
Earlier work this paper cites.
Mobile-Sandbox: combining static and dynamic analysis with machine-learning techniques
Michael Spreitzenbarth, Thomas Schreck, F. Echtler, D. Arp, and Johannes Hoffmann. 2014 · 2014
Earlier work this paper cites.
Statistical and machine learning methods for software fault prediction using CK metric suite: a comparative analysis
Yeresime Suresh, Lov Kumar, and Santanu Ku Rath. 2014 · 2014
Earlier work this paper cites.
Refactoring for Software Design Smells: Managing Technical Debt (1 ed.)
Girish Suryanarayana, Ganesh Samarthyam, and Tushar Sharma. 2014 · 2014
Earlier work this paper cites.
Towards a Big Data Curated Benchmark of Inter-project Code Clones. In 2014 IEEE International Conference on Software Maintenance and Evolution . 476–480
Jeffrey Svajlenko, Judith F. Islam, Iman Keivanloo, Chanchal K. Roy, and Mohammad Mamun Mia. 2014 · 2014
Earlier work this paper cites.
Detecting Design Patterns in Object-Oriented Program Source Code by Using Metrics and Machine Learning
S. Uchiyama, A. Kubo, H. Washizaki, and Y. Fukazawa. 2014 · 2014
Earlier work this paper cites.
Prediction of Cross-Site Scripting Attack Using Machine Learning Algorithms. In Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing (Amritapuri, India) (ICONIAAC ’14) . Association for Computing Machinery, New York, NY, USA, Article 55, 5 pages
B. A. Vishnu and K. P. Jevitha. 2014 · 2014
Earlier work this paper cites.
Recommending Clones for Refactoring Using Design, Context, and History. In 2014 IEEE International Conference on Software Maintenance and Evolution . 331–340
Wei Wang and Michael W. Godfrey. 2014 · 2014
Earlier work this paper cites.
Classification model for code clones based on machine learning
Jiachen Yang, K. Hotta, Yoshiki Higo, H. Igaki, and S. Kusumoto. 2014 · 2014
Earlier work this paper cites.
Using Software Structure to Predict Vulnerability Exploitation Potential. In 2014 IEEE Eighth International Conference on Software Security and Reliability-Companion . 13–18
Awad A. Younis and Yashwant K. Malaiya. 2014 · 2014
Earlier work this paper cites.
Towards Cross Project Vulnerability Prediction in Open Source Web Applications. In Proceedings of the The International Conference on Engineering & MIS 2015 (Istanbul, Turkey) (ICEMIS ’15) . Association for Computing Machinery, New York, NY, USA, Article 42, 5 pages
Ibrahim Abunadi and Mamdouh Alenezi. 2015 · 2015
Earlier work this paper cites.
Comparative performance analysis of machine learning techniques for software bug detection. In Proceedings of the 4th International Conference on Software Engineering and Applications . AIRCC Press Chennai, Tamil Nadu, India, 71–79
Saiqa Aleem, Luiz Fernando Capretz, Faheem Ahmed, et al · 2015
Earlier work this paper cites.
Suggesting Accurate Method and Class Names. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering (Bergamo, Italy) (ESEC/FSE 2015) . 38–49
Miltiadis Allamanis, Earl T. Barr, Christian Bird, and Charles Sutton. 2015a · 2015
Earlier work this paper cites.
Automated Support for Diagnosis and Repair
Dalal Alrajeh, Jeff Kramer, Alessandra Russo, and Sebastian Uchitel. 2015 · 2015
Earlier work this paper cites.
Experience report: Evaluating the effectiveness of decision trees for detecting code smells. In 2015 IEEE 26th International Symposium on Software Reliability Engineering (ISSRE) . 261–269
L. Amorim, E. Costa, N. Antunes, B. Fonseca, and M. Ribeiro. 2015 · 2015
Earlier work this paper cites.
Software defect prediction using cost-sensitive neural network
"̈Omer Faruk Arar and K"̈urşat Ayan. 2015 · 2015
Earlier work this paper cites.
A Static Android Malicious Code Detection Method Based on Multi-Source Fusion
Yao Du, Xiaoqing Wang, and Junfeng Wang. 2015 · 2015
Earlier work this paper cites.
A comparison of some soft computing methods for software fault prediction
Ezgi Erturk and Ebru Akcapinar Sezer. 2015 · 2015
Earlier work this paper cites.
Comparing and experimenting machine learning techniques for code smell detection
F. Fontana, M. Mäntylä, Marco Zanoni, and Alessandro Marino. 2015 · 2015
Earlier work this paper cites.
An empirical study of robustness and stability of machine learning classifiers in software defect prediction
Arvinder Kaur and Kamaldeep Kaur. 2015 · 2015
Earlier work this paper cites.
Software defect prediction using ensemble learning on selected features
Issam H Laradji, Mohammad Alshayeb, and Lahouari Ghouti. 2015 · 2015
Earlier work this paper cites.
Should fixing these failures be delegated to automated program repair?. In 2015 IEEE 26th International Symposium on Software Reliability Engineering (ISSRE) . 427–437
X. D. Le, T. B. Le, and D. Lo. 2015 · 2015
Earlier work this paper cites.
The ManyBugs and IntroClass Benchmarks for Automated Repair of C Programs
Claire Le Goues, Neal Holtschulte, Edward K. Smith, Yuriy Brun, Premkumar Devanbu, Stephanie Forrest, and Westley Weimer. 2015 · 2015
Earlier work this paper cites.
A Hybrid Malicious Code Detection Method based on Deep Learning
Yuancheng Li, Rong Ma, and Runhai Jiao. 2015 · 2015
Earlier work this paper cites.
Detecting and Removing Web Application Vulnerabilities with Static Analysis and Data Mining
Ibéria Medeiros, Nuno Neves, and Miguel Correia. 2016 · 2015
Earlier work this paper cites.
Source code fragment summarization with small-scale crowdsourcing based features
N. Nazar, He Jiang, Guojun Gao, Tao Zhang, Xiaochen Li, and Zhilei Ren. 2015 · 2015
Earlier work this paper cites.
Learning to Generate Pseudo-Code from Source Code Using Statistical Machine Translation. In 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE) . 574–584
Y. Oda, H. Fudaba, G. Neubig, H. Hata, S. Sakti, T. Toda, and S. Nakamura. 2015 · 2015
Earlier work this paper cites.
Buffer Overflow Vulnerability Prediction from x86 Executables Using Static Analysis and Machine Learning. In 2015 IEEE 39th Annual Computer Software and Applications Conference , Vol. 2. 450–459
Bindu Madhavi Padmanabhuni and Hee Beng Kuan Tan. 2015 · 2015
Earlier work this paper cites.
VCCFinder: Finding Potential Vulnerabilities in Open-Source Projects to Assist Code Audits. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (Denver, Colorado, USA) (CCS ’15) . 426–437
Henning Perl, Sergej Dechand, Matthew Smith, Daniel Arp, Fabian Yamaguchi, Konrad Rieck, Sascha Fahl, and Yasemin Acar. 2015 · 2015
Earlier work this paper cites.
Intelligent Code Completion with Bayesian Networks
Sebastian Proksch, Johannes Lerch, and Mira Mezini. 2015 · 2015
Earlier work this paper cites.
Recommending Insightful Comments for Source Code using Crowdsourced Knowledge. In Proc. SCAM . 81–90
M. M. Rahman, C. K. Roy, and I. Keivanloo. 2015 · 2015
Earlier work this paper cites.
Deep convolutional neural networks for large-scale speech tasks
Tara N Sainath, Brian Kingsbury, George Saon, Hagen Soltau, Abdel-rahman Mohamed, George Dahl, and Bhuvana Ramabhadran. 2015 · 2015
Earlier work this paper cites.
Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition . 1–9
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015 · 2015
Earlier work this paper cites.
On applying machine learning techniques for design pattern detection
Marco Zanoni, Francesca Arcelli Fontana, and Fabio Stella. 2015 · 2015
Earlier work this paper cites.
Android Malware Detection Using Category-Based Machine Learning Classifiers. In Proceedings of the 17th Annual Conference on Information Technology Education (Boston, Massachusetts, USA) (SIGITE ’16) . 54–59
Huda Ali Alatwi, Tae Oh, Ernest Fokoue, and Bill Stackpole. 2016 · 2016
Earlier work this paper cites.
A Convolutional Attention Network for Extreme Summarization of Source Code
Miltiadis Allamanis, Hao Peng, and Charles Sutton. 2016 · 2016
Earlier work this paper cites.
AndroZoo: Collecting Millions of Android Apps for the Research Community. In Proceedings of the 13th International Conference on Mining Software Repositories (Austin, Texas) (MSR ’16) . 468–471
Kevin Allix, Tegawendé F. Bissyandé, Jacques Klein, and Yves Le Traon. 2016 · 2016
Earlier work this paper cites.
Experimenting Machine Learning Techniques to Predict Vulnerabilities. In 2016 Seventh Latin-American Symposium on Dependable Computing (LADC) . 151–156
H. Alves, B. Fonseca, and N. Antunes. 2016 · 2016
Earlier work this paper cites.
Mutation-Aware Fault Prediction. In Proceedings of the 25th International Symposium on Software Testing and Analysis (Saarbrücken, Germany) (ISSTA 2016) . Association for Computing Machinery, New York, NY, USA, 330–341
David Bowes, Tracy Hall, Mark Harman, Yue Jia, Federica Sarro, and Fan Wu. 2016 · 2016
Earlier work this paper cites.
Latent Attention for If-Then Program Synthesis. In Proceedings of the 30th International Conference on Neural Information Processing Systems (Barcelona, Spain) (NIPS’16) . 4581–4589
Xinyun Chen, Chang Liu, Richard Shin, Dawn Song, and Mingcheng Chen. 2016 · 2016
Earlier work this paper cites.
Software design pattern recognition using machine learning techniques. In 2016 ieee region 10 conference (tencon) . IEEE, 222–227
Ashish Kumar Dwivedi, Anand Tirkey, Ransingh Biswajit Ray, and Santanu Kumar Rath. 2016 · 2016
Earlier work this paper cites.
Repairing Intricate Faults in Code Using Machine Learning and Path Exploration. In 2016 IEEE International Conference on Software Maintenance and Evolution (ICSME) . 453–457
D. Gopinath, K. Wang, J. Hua, and S. Khurshid. 2016 · 2016
Earlier work this paper cites.
Survey on Software Vulnerability Analysis Method Based on Machine Learning. In 2016 IEEE First International Conference on Data Science in Cyberspace (DSC) . 642–647
Gong Jie, Kuang Xiao-Hui, and Liu Qiang. 2016 · 2016
Earlier work this paper cites.
Automatic Patch Generation by Learning Correct Code. In Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (St. Petersburg, FL, USA) (POPL ’16) . 298–312
Fan Long and Martin Rinard. 2016 · 2016
Earlier work this paper cites.
Convolutional Neural Networks over Tree Structures for Programming Language Processing. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (Phoenix, Arizona) (AAAI’16) . 1287–1293
Lili Mou, Ge Li, Lu Zhang, Tao Wang, and Zhi Jin. 2016 · 2016
Earlier work this paper cites.
Summarizing Software Artifacts: A Literature Review
N. Nazar, Y. Hu, and He Jiang. 2016 · 2016
Earlier work this paper cites.
Smells like teen spirit: Improving bug prediction performance using the intensity of code smells. In 2016 IEEE International Conference on Software Maintenance and Evolution (ICSME) . IEEE, 244–255
Fabio Palomba, Marco Zanoni, Francesca Arcelli Fontana, Andrea De Lucia, and Rocco Oliveto. 2016 · 2016
Earlier work this paper cites.
Early Identification of Vulnerable Software Components via Ensemble Learning. In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) . 476–481
Y. Pang, X. Xue, and A. S. Namin. 2016 · 2016
Earlier work this paper cites.
Probabilistic Model for Code with Decision Trees
Veselin Raychev, Pavol Bielik, and Martin Vechev. 2016 · 2016
Earlier work this paper cites.
Improved approach for software defect prediction using artificial neural networks. In 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) . 480–485
T. Sethi and Gagandeep. 2016 · 2016
Earlier work this paper cites.
Designite — A Software Design Quality Assessment Tool. In Proceedings of the First International Workshop on Bringing Architecture Design Thinking into Developers’ Daily Activities (BRIDGE ’16)
Tushar Sharma, Pratibha Mishra, and Rohit Tiwari. 2016 · 2016
Earlier work this paper cites.
Semantic Clone Detection Using Machine Learning. In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) . 1024–1028
A. Sheneamer and J. Kalita. 2016 · 2016
Earlier work this paper cites.
Identifying Auto-Generated Code by Using Machine Learning Techniques. In 2016 7th International Workshop on Empirical Software Engineering in Practice (IWESEP) . 18–23
K. Shimonaka, S. Sumi, Y. Higo, and S. Kusumoto. 2016 · 2016
Earlier work this paper cites.
Software analytics in practice: a defect prediction model using code smells. In Proceedings of the 20th International Database Engineering & Applications Symposium . 148–155
Behjat Soltanifar, Shirin Akbarinasaji, Bora Caglayan, Ayse Basar Bener, Asli Filiz, and Bryan M Kramer. 2016 · 2016
Earlier work this paper cites.
SVF: interprocedural static value-flow analysis in LLVM. In Proceedings of the 25th international conference on compiler construction . ACM, 265–266
Yulei Sui and Jingling Xue. 2016 · 2016
Earlier work this paper cites.
Multiple kernel ensemble learning for software defect prediction
Tiejian Wang, Zhiwu Zhang, Xiaoyuan Jing, and Liqiang Zhang. 2016b · 2016
Earlier work this paper cites.
Deep Learning Code Fragments for Code Clone Detection. In Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering (Singapore, Singapore) (ASE 2016) . 87–98
Martin White, Michele Tufano, Christopher Vendome, and Denys Poshyvanyk. 2016 · 2016
Earlier work this paper cites.
Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks
Osama Abdeljaber, Onur Avci, Serkan Kiranyaz, Moncef Gabbouj, and Daniel J Inman. 2017 · 2017
Earlier work this paper cites.
Code smell severity classification using machine learning techniques
Francesca Arcelli Fontana and Marco Zanoni. 2017 · 2017
Earlier work this paper cites.
A parallel corpus of Python functions and documentation strings for automated code documentation and code generation
Antonio Valerio Miceli Barone and Rico Sennrich. 2017 · 2017
Earlier work this paper cites.
Machine learning for finding bugs: An initial report. In 2017 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE) . 21–26
T. Chappelly, C. Cifuentes, P. Krishnan, and S. Gevay. 2017 · 2017
Earlier work this paper cites.
Integrated approach to software defect prediction
Ebubeogu Amarachukwu Felix and Sai Peck Lee. 2017 · 2017
Earlier work this paper cites.
Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques: A Survey
Seyed Mohammad Ghaffarian and Hamid Reza Shahriari. 2017 · 2017
Earlier work this paper cites.
Learn Fuzz: Machine learning for input fuzzing. In 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) . 50–59
P. Godefroid, H. Peleg, and R. Singh. 2017 · 2017
Earlier work this paper cites.
Can Latent Topics in Source Code Predict Missing Architectural Tactics?. In 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE) . 15–26
R. Gopalakrishnan, P. Sharma, M. Mirakhorli, and M. Galster. 2017 · 2017
Earlier work this paper cites.
LSTM: A search space odyssey
Klaus Greff, Rupesh K Srivastava, Jan Koutník, Bas R Steunebrink, and Jürgen Schmidhuber. 2017 · 2017
Earlier work this paper cites.
DeepFix: Fixing Common C Language Errors by Deep Learning.. In AAAI . 1345–1351
Rahul Gupta, Soham Pal, Aditya Kanade, and Shirish Shevade. 2017 · 2017
Earlier work this paper cites.
Are Deep Neural Networks the Best Choice for Modeling Source Code?. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering (Paderborn, Germany) (ESEC/FSE 2017) . 763–773
Vincent J. Hellendoorn and Premkumar Devanbu. 2017 · 2017
Earlier work this paper cites.
Machine-Learning-Guided Selectively Unsound Static Analysis. In Proceedings of the 39th International Conference on Software Engineering (Buenos Aires, Argentina) (ICSE ’17) . 519–529
Kihong Heo, Hakjoo Oh, and Kwangkeun Yi. 2017 · 2017
Earlier work this paper cites.
Automatically generating commit messages from diffs using neural machine translation. In 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) . 135–146
S. Jiang, A. Armaly, and C. McMillan. 2017 · 2017
Earlier work this paper cites.
Towards automatic generation of short summaries of commits. In 2017 IEEE/ACM 25th International Conference on Program Comprehension (ICPC) . IEEE, 320–323
Siyuan Jiang and Collin McMillan. 2017 · 2017
Earlier work this paper cites.
A Support Vector Machine Based Approach for Code Smell Detection. In 2017 International Conference on Machine Learning and Data Science (MLDS) . 9–14
A. Kaur, S. Jain, and S. Goel. 2017 · 2017
Earlier work this paper cites.
An empirical study of software entropy based bug prediction using machine learning
Arvinder Kaur, Kamaldeep Kaur, and Deepti Chopra. 2017 · 2017
Earlier work this paper cites.
Using source code metrics to predict change-prone web services: A case-study on ebay services. In 2017 IEEE workshop on machine learning techniques for software quality evaluation (MaLTeSQuE) . IEEE, 1–7
Lov Kumar, Santanu Kumar Rath, and Ashish Sureka. 2017 · 2017
Earlier work this paper cites.
Application of LSSVM and SMOTE on Seven Open Source Projects for Predicting Refactoring at Class Level. In 2017 24th Asia-Pacific Software Engineering Conference (APSEC) . 90–99
L. Kumar and A. Sureka. 2017 · 2017
Earlier work this paper cites.
Code review analysis of software system using machine learning techniques. In 2017 11th International Conference on Intelligent Systems and Control (ISCO) . 8–13
H. Lal and G. Pahwa. 2017 · 2017
Earlier work this paper cites.
Root cause analysis of software bugs using machine learning techniques. In 2017 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence . IEEE, 105–111
Harsh Lal and Gaurav Pahwa. 2017 · 2017
Earlier work this paper cites.
Human activity recognition from accelerometer data using Convolutional Neural Network. In Big Data and Smart Computing (BigComp), 2017 IEEE International Conference on . IEEE, 131–134
Song-Mi Lee, Sang Min Yoon, and Heeryon Cho. 2017 · 2017
Earlier work this paper cites.
Boosting automatic commit classification into maintenance activities by utilizing source code changes. In Proceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering . 97–106
Stanislav Levin and Amiram Yehudai. 2017 · 2017
Earlier work this paper cites.
Software defect prediction via convolutional neural network. In 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS) . IEEE, 318–328
Jian Li, Pinjia He, Jieming Zhu, and Michael R Lyu. 2017 · 2017
Earlier work this paper cites.
Learning to generate comments for api-based code snippets
Yangyang Lu, Zelong Zhao, Ge Li, and Zhi Jin. 2017 · 2017
Earlier work this paper cites.
Neural Machine Translation (seq2seq) Tutorial
Minh-Thang Luong, Eugene Brevdo, and Rui Zhao. 2017 · 2017
Earlier work this paper cites.
Empirical comparison of machine learning algorithms for bug prediction in open source software. In 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC) . 40–45
R. Malhotra, L. Bahl, S. Sehgal, and P. Priya. 2017 · 2017
Earlier work this paper cites.
Prediction & Assessment of Change Prone Classes Using Statistical & Machine Learning Techniques
R. Malhotra and Rupender Jangra. 2017 · 2017
Earlier work this paper cites.
Machine learning aided Android malware classification
Nikola Milosevic, Ali Dehghantanha, and Kim-Kwang Raymond Choo. 2017 · 2017
Earlier work this paper cites.
Toward a smell-aware bug prediction model
Fabio Palomba, Marco Zanoni, Francesca Arcelli Fontana, Andrea De Lucia, and Rocco Oliveto. 2017 · 2017
Earlier work this paper cites.
Predicting Vulnerable Software Components through Deep Neural Network. In Proceedings of the 2017 International Conference on Deep Learning Technologies (Chengdu, China) (ICDLT ’17) . Association for Computing Machinery, New York, NY, USA, 6–10
Yulei Pang, Xiaozhen Xue, and Huaying Wang. 2017 · 2017
Earlier work this paper cites.
Software Fault Prediction and Classification using Cost based Random Forest in Spiral Life Cycle Model
Hosahalli Mahalingappa Premalatha and Chimanahalli Venkateshavittalachar Srikrishna. 2017 · 2017
Earlier work this paper cites.
Predicting Android Application Security and Privacy Risk with Static Code Metrics. In Proceedings of the 4th International Conference on Mobile Software Engineering and Systems (Buenos Aires, Argentina) (MOBILESoft ’17) . 149–153
Akond Rahman, Priysha Pradhan, Asif Partho, and Laurie Williams. 2017 · 2017
Earlier work this paper cites.
Elixir: Effective object-oriented program repair. In 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) . 648–659
R. K. Saha, Y. Lyu, H. Yoshida, and M. R. Prasad. 2017 · 2017
Earlier work this paper cites.
DARVIZ: Deep Abstract Representation, Visualization, and Verification of Deep Learning Models
Anush Sankaran, Rahul Aralikatte, Senthil Mani, Shreya Khare, Naveen Panwar, and Neelamadhav Gantayat. 2017 · 2017
Cited alongside, same era.
A survey on software smells
Tushar Sharma and Diomidis Spinellis. 2018 · 2017
Cited alongside, same era.
Software defect prediction analysis using machine learning algorithms. In 2017 7th International Conference on Cloud Computing, Data Science Engineering - Confluence . 775–781
P. Singh and A. Chug. 2017 · 2017
Cited alongside, same era.
Assessment of machine learning algorithms for determining defective classes in an object-oriented software. In 2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) . 204–209
P. Singh and R. Malhotra. 2017 · 2017
Cited alongside, same era.
An empirical framework for web service anti-pattern prediction using machine learning techniques. In 2019 9th Annual Information Technology, Electromechanical Engineering and Microelectronics Conference (IEMECON) . IEEE, 137–143
Sahithi Tummalapalli, Lov Kumar, and Lalita Bhanu Murthy Neti. 2019 · 2019
Later among the works it cites.
Neural Program Repair by Jointly Learning to Localize and Repair
Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, and Rishabh Singh. 2019 · 2019
Later among the works it cites.
A Machine Learning Based Automatic Folding of Dynamically Typed Languages. In Proceedings of the 3rd ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation (Tallinn, Estonia) (MaLTeSQuE 2019) . 31–36
Nickolay Viuginov and Andrey Filchenkov. 2019 · 2019
Later among the works it cites.
Multi-Modal Attention Network Learning for Semantic Source Code Retrieval. In Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering (San Diego, California) (ASE ’19) . 13–25
Yao Wan, Jingdong Shu, Yulei Sui, Guandong Xu, Zhou Zhao, Jian Wu, and Philip S. Yu. 2019 · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Towards a software vulnerability prediction model using traceable code patterns and software metrics. In 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) . 1022–1025
Kazi Zakia Sultana. 2017 · 2017
Cited alongside, same era.
Change Prediction through Coding Rules Violations. In Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering (Karlskrona, Sweden) (EASE’17) . 61–64
Irene Tollin, Francesca Arcelli Fontana, Marco Zanoni, and Riccardo Roveda. 2017 · 2017
Cited alongside, same era.
Attention is All you Need. In Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Machine Learning-Based Malicious Application Detection of Android
Linfeng Wei, Weiqi Luo, Jian Weng, Yanjun Zhong, Xiaoqian Zhang, and Zheng Yan. 2017 · 2017
Cited alongside, same era.
GEMS: An Extract Method Refactoring Recommender. In 2017 IEEE 28th International Symposium on Software Reliability Engineering (ISSRE) . 24–34
Sihan Xu, Aishwarya Sivaraman, Siau-Cheng Khoo, and Jing Xu. 2017 · 2017
Cited alongside, same era.
A combined-learning based framework for improved software fault prediction
Chubato Wondaferaw Yohannese and Tianrui Li. 2017 · 2017
Cited alongside, same era.
Large-Scale and Language-Oblivious Code Authorship Identification. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (Toronto, Canada) (CCS ’18) . 101–114
Mohammed Abuhamad, Tamer AbuHmed, Aziz Mohaisen, and DaeHun Nyang. 2018 · 2018
Cited alongside, same era.
Compilation Error Repair: For the Student Programs, from the Student Programs. In Proceedings of the 40th International Conference on Software Engineering: Software Engineering Education and Training (Gothenburg, Sweden) (ICSE-SEET ’18) . 78–87
Umair Z. Ahmed, Pawan Kumar, Amey Karkare, Purushottam Kar, and Sumit Gulwani. 2018 · 2018
Cited alongside, same era.
Later among the works it cites.
How does Machine Learning Change Software Development Practices?
Z. Wan, X. Xia, D. Lo, and G. C. Murphy. 2019 · 2019
Later among the works it cites.
Deep Learning Based Code Completion Models for Programming Codes. In Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control (Amsterdam, Netherlands) (ISCSIC 2019) . Article 16, 9 pages
Shuai Wang, Jinyang Liu, Ye Qiu, Zhiyi Ma, Junfei Liu, and Zhonghai Wu. 2019 · 2019
Later among the works it cites.
How Different Is It Between Machine-Generated and Developer-Provided Patches? : An Empirical Study on the Correct Patches Generated by Automated Program Repair Techniques. In 2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) . 1–12
S. Wang, M. Wen, L. Chen, X. Yi, and X. Mao. 2019 · 2019
Later among the works it cites.
Sorting and transforming program repair ingredients via deep learning code similarities. In 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 479–490
Martin White, Michele Tufano, Matias Martinez, Martin Monperrus, and Denys Poshyvanyk. 2019 · 2019
Later among the works it cites.
Method Name Suggestion with Hierarchical Attention Networks. In Proceedings of the 2019 ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation (Cascais, Portugal) (PEPM 2019) . 10–21
Sihan Xu, Sen Zhang, Weijing Wang, Xinya Cao, Chenkai Guo, and Jing Xu. 2019 · 2019
Later among the works it cites.
A Novel Solutions for Malicious Code Detection and Family Clustering Based on Machine Learning
Hangfeng Yang, Shudong Li, Xiaobo Wu, Hui Lu, and Weihong Han. 2019b · 2019
Later among the works it cites.
Improve Language Modeling for Code Completion Through Learning General Token Repetition of Source Code with Optimized Memory
Yixiao Yang, Xiang Chen, and Jiaguang Sun. 2019a · 2019
Later among the works it cites.
CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning. In The World Wide Web Conference (San Francisco, CA, USA) (WWW ’19) . 2203–2214
Ziyu Yao, Jayavardhan Reddy Peddamail, and Huan Sun. 2019 · 2019
Later among the works it cites.
A Novel Neural Source Code Representation Based on Abstract Syntax Tree. In 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) . 783–794
J. Zhang, X. Wang, H. Zhang, H. Sun, K. Wang, and X. Liu. 2019 · 2019
Later among the works it cites.
Machine Learning Testing: Survey, Landscapes and Horizons
J. M. Zhang, M. Harman, L. Ma, and Y. Liu. 2020 · 2019
Later among the works it cites.
CodeAttention: translating source code to comments by exploiting the code constructs
Wenhao Zheng, Hongyu Zhou, Ming Li, and Jianxin Wu. 2019 · 2019
Later among the works it cites.
JavaScript Code Suggestion Based on Deep Learning. In Proceedings of the 2019 3rd International Conference on Innovation in Artificial Intelligence (Suzhou, China) (ICIAI 2019) . 145–149
Chaoliang Zhong, Ming Yang, and Jun Sun. 2019 · 2019
Later among the works it cites.
GitHub archive
2020 · 2020
Later among the works it cites.
Software change proneness prediction using machine learning. In 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT) . IEEE, 1–7
Raja Abbas, Fawzi Abdulaziz Albalooshi, and Mustafa Hammad. 2020 · 2020
Later among the works it cites.
A machine learning approach to improve the detection of ci skip commits
Rabe Abdalkareem, Suhaib Mujahid, and Emad Shihab. 2020 · 2020
Later among the works it cites.
Application of machine learning algorithms for code smell prediction using object-oriented software metrics
Mansi Agnihotri and Anuradha Chug. 2020 · 2020
Later among the works it cites.
A Transformer-based Approach for Source Code Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . 4998–5007
Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2020 · 2020
Later among the works it cites.
The influence of deep learning algorithms factors in software fault prediction
Osama Al Qasem, Mohammed Akour, and Mamdouh Alenezi. 2020 · 2020
Later among the works it cites.
Bad Smell Detection Using Machine Learning Techniques: A Systematic Literature Review
A. AL-Shaaby, Hamoud I. Aljamaan, and M. Alshayeb. 2020 · 2020
Later among the works it cites.
Software defect prediction using tree-based ensembles. In Proceedings of the 16th ACM international conference on predictive models and data analytics in software engineering . 1–10
Hamoud Aljamaan and Amal Alazba. 2020 · 2020
Later among the works it cites.
The Effectiveness of Supervised Machine Learning Algorithms in Predicting Software Refactoring
M. Aniche, E. Maziero, R. Durelli, and V. Durelli. 2020 · 2020
Later among the works it cites.
Vulnerability Prediction From Source Code Using Machine Learning
Z. Bilgin, M. A. Ersoy, E. U. Soykan, E. Tomur, P. Çomak, and L. Karaçay. 2020 · 2020
Later among the works it cites.
Compiler-Based Graph Representations for Deep Learning Models of Code. In Proceedings of the 29th International Conference on Compiler Construction (San Diego, CA, USA) (CC 2020) . 201–211
Alexander Brauckmann, Andrés Goens, Sebastian Ertel, and Jeronimo Castrillon. 2020 · 2020
Later among the works it cites.
A Comparative Analysis for Machine Learning based Software Defect Prediction Systems. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) . 1–7
M. Cetiner and O. K. Sahingoz. 2020 · 2020
Later among the works it cites.
CODIT: Code Editing with Tree-Based Neural Models
S. Chakraborty, Y. Ding, M. Allamanis, and B. Ray. 2020 · 2020
Later among the works it cites.
CODIT: Code Editing With Tree-Based Neural Models
Saikat Chakraborty, Yangruibo Ding, Miltiadis Allamanis, and Baishakhi Ray. 2022 · 2020
Later among the works it cites.
Source Code Summarization Using Attention-Based Keyword Memory Networks. In 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) . 564–570
Y. Choi, S. Kim, and J. Lee. 2020 · 2020
Later among the works it cites.
Embedding Java Classes with Code2vec: Improvements from Variable Obfuscation. In Proceedings of the 17th International Conference on Mining Software Repositories (Seoul, Republic of Korea) (MSR ’20) . 243–253
Rhys Compton, Eibe Frank, Panos Patros, and Abigail Koay. 2020 · 2020
Later among the works it cites.
Towards predictive analysis of android vulnerability using statistical codes and machine learning for IoT applications
Jianfeng Cui, Lixin Wang, Xin Zhao, and Hongyi Zhang. 2020 · 2020
Later among the works it cites.
Investigating Non-Usually Employed Features in the Identification of Architectural Smells: A Machine Learning-Based Approach
Warteruzannan Soyer Cunha, Guisella Angulo Armijo, and Valter Vieira de Camargo. 2020 · 2020
Later among the works it cites.
Understanding machine learning software defect predictions
Geanderson Esteves Dos Santos, E. Figueiredo, Adriano Veloso, Markos Viggiato, and N. Ziviani. 2020 · 2020
Later among the works it cites.
On the Relevance of Cross-project Learning with Nearest Neighbours for Commit Message Generation. In Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops . 470–475
Khashayar Etemadi and Martin Monperrus. 2020 · 2020
Later among the works it cites.
Functional Code Clone Detection with Syntax and Semantics Fusion Learning. In Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis (Virtual Event, USA) (ISSTA 2020) . 516–527
Chunrong Fang, Zixi Liu, Yangyang Shi, Jeff Huang, and Qingkai Shi. 2020b · 2020
Later among the works it cites.
FastEmbed: Predicting vulnerability exploitation possibility based on ensemble machine learning algorithm
Yong Fang, Yongcheng Liu, Cheng Huang, and Liang Liu. 2020a · 2020
Later among the works it cites.
Generating Question Titles for Stack Overflow from Mined Code Snippets
Zhipeng Gao, Xin Xia, John Grundy, David Lo, and Yuan-Fang Li. 2020 · 2020
Later among the works it cites.
Code Smell Prediction Employing Machine Learning Meets Emerging Java Language Constructs
Hanna Grodzicka, Arkadiusz Ziobrowski, Zofia Łakomiak, Michał Kawa, and Lech Madeyski. 2020 · 2020
Later among the works it cites.
Code smell detection using multi-label classification approach
Thirupathi Guggulothu and S. A. Moiz. 2020 · 2020
Later among the works it cites.
Improved automatic summarization of subroutines via attention to file context. In Proceedings of the 17th International Conference on Mining Software Repositories . 300–310
Sakib Haque, Alexander LeClair, Lingfei Wu, and Collin McMillan. 2020 · 2020
Later among the works it cites.
CC2Vec: Distributed Representations of Code Changes. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering (Seoul, South Korea) (ICSE ’20) . 518–529
Thong Hoang, Hong Jin Kang, David Lo, and Julia Lawall. 2020 · 2020
Later among the works it cites.
CommtPst: Deep learning source code for commenting positions prediction
Yuan Huang, Xinyu Hu, Nan Jia, Xiangping Chen, Zibin Zheng, and Xiapu Luo. 2020a · 2020
Later among the works it cites.
Towards automatically generating block comments for code snippets
Yuan Huang, Shaohao Huang, Huanchao Chen, Xiangping Chen, Zibin Zheng, Xiapu Luo, Nan Jia, Xinyu Hu, and Xiaocong Zhou. 2020b · 2020
Later among the works it cites.
CodeGRU: Context-aware deep learning with gated recurrent unit for source code modeling
Yasir Hussain, Zhiqiu Huang, Yu Zhou, and Senzhang Wang. 2020 · 2020
Later among the works it cites.
Learning and Evaluating Contextual Embedding of Source Code. In Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 119) , Hal Daumé III and Aarti Singh (Eds.). PMLR, 5110–5121
Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi. 2020 · 2020
Later among the works it cites.
Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering (Seoul, South Korea) (ICSE ’20) . 1073–1085
Rafael-Michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, and Andrea Janes. 2020 · 2020
Later among the works it cites.
Cross-Project Software Fault Prediction Using Data Leveraging Technique to Improve Software Quality. In Proceedings of the Evaluation and Assessment in Software Engineering (Trondheim, Norway) (EASE ’20) . 434–438
Bilal Khan, Danish Iqbal, and Sher Badshah. 2020 · 2020
Later among the works it cites.
Building Implicit Vector Representations of Individual Coding Style. In Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops (Seoul, Republic of Korea) (ICSEW’20) . 117–124
Vladimir Kovalenko, Egor Bogomolov, Timofey Bryksin, and Alberto Bacchelli. 2020 · 2020
Later among the works it cites.
Enhancing Source Code Refactoring Detection with Explanations from Commit Messages. In 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER) . 512–516
Rrezarta Krasniqi and Jane Cleland-Huang. 2020 · 2020
Later among the works it cites.
Recommendation of Move Method Refactoring Using Path-Based Representation of Code. In Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops (Seoul, Republic of Korea) (ICSEW’20) . 315–322
Zarina Kurbatova, Ivan Veselov, Yaroslav Golubev, and Timofey Bryksin. 2020 · 2020
Later among the works it cites.
Deep Learning for Source Code Modeling and Generation: Models, Applications, and Challenges
Triet H. M. Le, Hao Chen, and Muhammad Ali Babar. 2020 · 2020
Later among the works it cites.
Improved Code Summarization via a Graph Neural Network. In Proceedings of the 28th International Conference on Program Comprehension (Seoul, Republic of Korea) (ICPC ’20) . 184–195
Alexander LeClair, Sakib Haque, Lingfei Wu, and Collin McMillan. 2020 · 2020
Later among the works it cites.
DeepCommenter: A Deep Code Comment Generation Tool with Hybrid Lexical and Syntactical Information. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Virtual Event, USA) (ESEC/FSE 2020) . 1571–1575
Boao Li, Meng Yan, Xin Xia, Xing Hu, Ge Li, and David Lo. 2020b · 2020
Later among the works it cites.
Artificial Intelligence Applied to Software Testing: A Literature Review. In 2020 15th Iberian Conference on Information Systems and Technologies (CISTI) . 1–6
R. Lima, A. M. R. da Cruz, and J. Ribeiro. 2020 · 2020
Later among the works it cites.
Deep Learning-Based Vulnerable Function Detection: A Benchmark. In Information and Communications Security (Lecture Notes in Computer Science) , Jianying Zhou, Xiapu Luo, Qingni Shen, and Zhen Xu (Eds.). Springer International Publishing, Cham, 219–232
Guanjun Lin, Wei Xiao, Jun Zhang, and Yang Xiang. 2020 · 2020
Later among the works it cites.
Adaptive Deep Code Search. In Proceedings of the 28th International Conference on Program Comprehension (ICPC ’20) . Association for Computing Machinery, 48––59
Chunyang Ling, Zeqi Lin, Yanzhen Zou, and Bing Xie. 2020 · 2020
Later among the works it cites.
Multi-task Learning based Pre-trained Language Model for Code Completion. In 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) . 473–485
F. Liu, G. Li, Y. Zhao, and Z. Jin. 2020 · 2020
Later among the works it cites.
On the Efficiency of Test Suite Based Program Repair: A Systematic Assessment of 16 Automated Repair Systems for Java Programs. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering (Seoul, South Korea) (ICSE ’20) . 615?–627
Kui Liu, Shangwen Wang, Anil Koyuncu, Kisub Kim, Tegawendé F. Bissyandé, Dongsun Kim, Peng Wu, Jacques Klein, Xiaoguang Mao, and Yves Le Traon. 2020 · 2020
Later among the works it cites.
ATOM: Commit message generation based on abstract syntax tree and hybrid ranking
Shangqing Liu, Cuiyun Gao, Sen Chen, Nie Lun Yiu, and Yang Liu. 2020a · 2020
Later among the works it cites.
Can automated program repair refine fault localization? a unified debugging approach. In Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis . 75–87
Yiling Lou, Ali Ghanbari, Xia Li, Lingming Zhang, Haotian Zhang, Dan Hao, and Lu Zhang. 2020 · 2020
Later among the works it cites.
A Preliminary Study on the Adequacy of Static Analysis Warnings with Respect to Code Smell Prediction. In Proceedings of the 4th ACM SIGSOFT International Workshop on Machine-Learning Techniques for Software-Quality Evaluation (Virtual, USA) (MaLTeSQuE 2020) . 1–6
Savanna Lujan, Fabiano Pecorelli, Fabio Palomba, Andrea De Lucia, and Valentina Lenarduzzi. 2020 · 2020
Later among the works it cites.
CoCoNuT: Combining Context-Aware Neural Translation Models Using Ensemble for Program Repair. In Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis (Virtual Event, USA) (ISSTA 2020) . 101–114
Thibaud Lutellier, Hung Viet Pham, Lawrence Pang, Yitong Li, Moshi Wei, and Lin Tan. 2020 · 2020
Later among the works it cites.
Decompiled APK based malicious code classification
Roni Mateless, Daniel Rejabek, Oded Margalit, and Robert Moskovitch. 2020 · 2020
Later among the works it cites.
Predicting Code Smells and Analysis of Predictions: Using Machine Learning Techniques and Software Metrics
Mohammad Y. Mhawish and Manjari Gupta. 2020 · 2020
Later among the works it cites.
A machine learning based framework for code clone validation
Golam Mostaeen, Banani Roy, Chanchal K. Roy, Kevin Schneider, and Jeffrey Svajlenko. 2020 · 2020
Later among the works it cites.
Feature Requests-Based Recommendation of Software Refactorings
Ally S. Nyamawe, Hui Liu, Nan Niu, Qasim Umer, and Zhendong Niu. 2020 · 2020
Later among the works it cites.
Applying Machine Learning to Customized Smell Detection: A Multi-Project Study (SBES ’20) . 233–242
Daniel Oliveira, Wesley K. G. Assunção, Leonardo Souza, Willian Oizumi, Alessandro Garcia, and Baldoino Fonseca. 2020 · 2020
Later among the works it cites.
Deep Learning for Software Defect Prediction: A Survey. In Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops (Seoul, Republic of Korea) (ICSEW’20) . 209–214
Safa Omri and Carsten Sinz. 2020 · 2020
Later among the works it cites.
Statistical Machine Translation Outperforms Neural Machine Translation in Software Engineering: Why and How. In Proceedings of the 1st ACM SIGSOFT International Workshop on Representation Learning for Software Engineering and Program Languages (Virtual, USA) (RL+SE&PL 2020) . 3–12
Hung Phan and Ali Jannesari. 2020 · 2020
Later among the works it cites.
Software Defect Prediction Using Machine Learning Techniques. In 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) . 728–733
C. L. Prabha and N. Shivakumar. 2020 · 2020
Later among the works it cites.
Deep learning based software defect prediction
Lei Qiao, Xuesong Li, Qasim Umer, and Ping Guo. 2020 · 2020
Later among the works it cites.
Towards Demystifying Dimensions of Source Code Embeddings. In Proceedings of the 1st ACM SIGSOFT International Workshop on Representation Learning for Software Engineering and Program Languages (Virtual, USA) (RL+SE&PL 2020) . 29–38
Md Rafiqul Islam Rabin, Arjun Mukherjee, Omprakash Gnawali, and Mohammad Amin Alipour. 2020 · 2020
Later among the works it cites.
A Neural Network Based Intelligent Support Model for Program Code Completion
M. Rahman, Yutaka Watanobe, and K. Nakamura. 2020 · 2020
Later among the works it cites.
A survey of deep active learning
Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Xiaojiang Chen, and Xin Wang. 2020 · 2020
Later among the works it cites.
Web Service API Anti-patterns Detection as a Multi-label Learning Problem. In International Conference on Web Services . Springer, 114–132
Islem Saidani, Ali Ouni, and Mohamed Wiem Mkaouer. 2020 · 2020
Later among the works it cites.
Type Error Feedback via Analytic Program Repair. In Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation (London, UK) (PLDI 2020) . 16–30
Georgios Sakkas, Madeline Endres, Benjamin Cosman, Westley Weimer, and Ranjit Jhala. 2020 · 2020
Later among the works it cites.
Improving Code Recommendations by Combining Neural and Classical Machine Learning Approaches. In Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops (Seoul, Republic of Korea) (ICSEW’20) . 476–482
Max Eric Henry Schumacher, Kim Tuyen Le, and Artur Andrzejak. 2020 · 2020
Later among the works it cites.
Applying Probabilistic Models to C++ Code on an Industrial Scale. In Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops (Seoul, Republic of Korea) (ICSEW’20) . 595–602
Andrey Shedko, Ilya Palachev, Andrey Kvochko, Aleksandr Semenov, and Kwangwon Sun. 2020 · 2020
Later among the works it cites.
A Survey of Automatic Software Vulnerability Detection, Program Repair, and Defect Prediction Techniques
Zhidong Shen and S. Chen. 2020 · 2020
Later among the works it cites.
PathPair2Vec: An AST path pair-based code representation method for defect prediction
Ke Shi, Yang Lu, Jingfei Chang, and Zhen Wei. 2020 · 2020
Later among the works it cites.
DeeperCoder: Code Generation Using Machine Learning. In 2020 10th Annual Computing and Communication Workshop and Conference (CCWC) . 0194–0199
S. Shim, P. Patil, R. R. Yadav, A. Shinde, and V. Devale. 2020 · 2020
Later among the works it cites.
Improving Code Search with Co-Attentive Representation Learning. In Proceedings of the 28th International Conference on Program Comprehension (Seoul, Republic of Korea) (ICPC ’20) . 196–207
Jianhang Shuai, Ling Xu, Chao Liu, Meng Yan, Xin Xia, and Yan Lei. 2020 · 2020
Later among the works it cites.
A machine learning approach to software model refactoring
Brahmaleen Kaur Sidhu, Kawaljeet Singh, and Neeraj Sharma. 2022 · 2020
Later among the works it cites.
Transfer Learning Code Vectorizer based Machine Learning Models for Software Defect Prediction. In 2020 International Conference on Computational Performance Evaluation (ComPE) . 497–502
R. Singh, J. Singh, M. S. Gill, R. Malhotra, and Garima. 2020 · 2020
Later among the works it cites.
A Human Study of Comprehension and Code Summarization. In Proceedings of the 28th International Conference on Program Comprehension (Seoul, Republic of Korea) (ICPC ’20) . 2–13
Sean Stapleton, Yashmeet Gambhir, Alexander LeClair, Zachary Eberhart, Westley Weimer, Kevin Leach, and Yu Huang. 2020 · 2020
Later among the works it cites.
Flow2Vec: Value-Flow-Based Precise Code Embedding
Yulei Sui, Xiao Cheng, Guanqin Zhang, and Haoyu Wang. 2020 · 2020
Later among the works it cites.
IntelliCode Compose: Code Generation Using Transformer. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Virtual Event, USA) (ESEC/FSE 2020) . 1433–1443
Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, and Neel Sundaresan. 2020 · 2020
Later among the works it cites.
Design Flaws Prediction for Impact on Software Maintainability using Extreme Learning Machine. In 2020 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT NCON) . 79–82
P. Thongkum and S. Mekruksavanich. 2020 · 2020
Later among the works it cites.
Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair. In 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) . 981–992
H. Tian, K. Liu, A. K. Kaboré, A. Koyuncu, L. Li, J. Klein, and T. F. Bissyandé. 2020 · 2020
Later among the works it cites.
RefactoringMiner 2.0
Nikolaos Tsantalis, Ameya Ketkar, and Danny Dig. 2020 · 2020
Later among the works it cites.
Applying Machine Learning in Technical Debt Management: Future Opportunities and Challenges. In Quality of Information and Communications Technology , Martin Shepperd, Fernando Brito e Abreu, Alberto Rodrigues da Silva, and Ricardo Pérez-Castillo (Eds.). 53–67
Angeliki-Agathi Tsintzira, Elvira-Maria Arvanitou, Apostolos Ampatzoglou, and Alexander Chatzigeorgiou. 2020 · 2020
Later among the works it cites.
Prediction of Web Service Anti-Patterns Using Aggregate Software Metrics and Machine Learning Techniques. In Proceedings of the 13th Innovations in Software Engineering Conference on Formerly Known as India Software Engineering Conference (Jabalpur, India) (ISEC 2020) . Article 8, 11 pages
Sahithi Tummalapalli, Lov Kumar, and N. L. Bhanu Murthy. 2020a · 2020
Later among the works it cites.
Identifying and Generating Missing Tests using Machine Learning on Execution Traces. In 2020 IEEE International Conference On Artificial Intelligence Testing (AITest) . 83–90
M. Utting, B. Legeard, F. Dadeau, F. Tamagnan, and F. Bouquet. 2020 · 2020
Later among the works it cites.
A Multi-Task Representation Learning Approach for Source Code. In Proceedings of the 1st ACM SIGSOFT International Workshop on Representation Learning for Software Engineering and Program Languages (Virtual, USA) (RL+SE&PL 2020) . 1–2
Deze Wang, Wei Dong, and Shanshan Li. 2020a · 2020
Later among the works it cites.
Fret: Functional Reinforced Transformer With BERT for Code Summarization
R. Wang, H. Zhang, G. Lu, L. Lyu, and C. Lyu. 2020a · 2020
Later among the works it cites.
Modular Tree Network for Source Code Representation Learning
Wenhan Wang, Ge Li, Sijie Shen, Xin Xia, and Zhi Jin. 2020b · 2020
Later among the works it cites.
Reinforcement-Learning-Guided Source Code Summarization via Hierarchical Attention
W. Wang, Y. Zhang, Y. Sui, Y. Wan, Z. Zhao, J. Wu, P. Yu, and G. Xu. 2020b · 2020
Later among the works it cites.
Reinforcement-learning-guided source code summarization via hierarchical attention
Wenhua Wang, Yuqun Zhang, Yulei Sui, Yao Wan, Zhou Zhao, Jian Wu, Philip Yu, and Guandong Xu. 2020 · 2020
Later among the works it cites.
A Machine Learning Approach to Classify Security Patches into Vulnerability Types. In 2020 IEEE Conference on Communications and Network Security (CNS) . 1–9
Xinda Wang, Shu Wang, Kun Sun, Archer Batcheller, and Sushil Jajodia. 2020d · 2020
Later among the works it cites.
Learning Semantic Program Embeddings with Graph Interval Neural Network
Yu Wang, Ke Wang, Fengjuan Gao, and Linzhang Wang. 2020c · 2020
Later among the works it cites.
GGF: A Graph-Based Method for Programming Language Syntax Error Correction. In Proceedings of the 28th International Conference on Program Comprehension (ICPC ’20) . Association for Computing Machinery, 139––148
Liwei Wu, Fei Li, Youhua Wu, and Tao Zheng. 2020 · 2020
Later among the works it cites.
LSTM-based deep learning for spatial–temporal software testing
L. Xiao, HuaiKou Miao, Tingting Shi, and Y. Hong. 2020 · 2020
Later among the works it cites.
Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning. In Proceedings of The Web Conference 2020 (Taipei, Taiwan) (WWW ’20) . 2309–2319
Wei Ye, Rui Xie, Jinglei Zhang, Tianxiang Hu, Xiaoyin Wang, and Shikun Zhang. 2020 · 2020
Later among the works it cites.
Software Defect Prediction via Transformer. In 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) , Vol. 1. 874–879
Q. Zhang and B. Wu. 2020 · 2020
Later among the works it cites.
Malicious Code Detection Based on Code Semantic Features
Yu Zhang and Binglong Li. 2020 · 2020
Later among the works it cites.
The impact factors on the performance of machine learning-based vulnerability detection: A comparative study
Wei Zheng, Jialiang Gao, Xiaoxue Wu, Fengyu Liu, Yuxing Xun, Guoliang Liu, and Xiang Chen. 2020 · 2020
Later among the works it cites.
Code smell detection using feature selection and stacking ensemble: An empirical investigation
Amal Alazba and Hamoud Aljamaan. 2021 · 2021
Closest in time.
E-APR: mapping the effectiveness of automated program repair techniques
Aldeida Aleti and Matias Martinez. 2021 · 2021
Closest in time.
Vishnu Banna, Akhil Chinnakotla, Zhengxin Yan, Anirudh Vegesana, Naveen Vivek, Kruthi Krishnappa, Wenxin Jiang, Yung-Hsiang Lu, George K. Thiruvathukal, and James C. Davis. 2021 · 2021
Closest in time.
Project-Level Encoding for Neural Source Code Summarization of Subroutines. In 2021 2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) (ICPC) . IEEE Computer Society, 253–264
A. Bansal, S. Haque, and C. McMillan. 2021 · 2021
Closest in time.
A Novel Deep Learning-Based Feature Selection Model for Improving the Static Analysis of Vulnerability Detection
Canan Batur Şahin and Laith Abualigah. 2021 · 2021
Closest in time.
On Multi-Modal Learning of Editing Source Code. In 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) . 443–455
Saikat Chakraborty and Baishakhi Ray. 2021 · 2021
Closest in time.
Why my code summarization model does not work: Code comment improvement with category prediction
Qiuyuan Chen, Xin Xia, Han Hu, David Lo, and Shanping Li. 2021 · 2021
Closest in time.
A Novel Approach for Code Smell Detection: An Empirical Study
Seema Dewangan, Rajwant Singh Rao, Alok Mishra, and Manjari Gupta. 2021 · 2021
Closest in time.
Software Engineering Meets Deep Learning: A Mapping Study. In Proceedings of the 36th Annual ACM Symposium on Applied Computing (Virtual Event, Republic of Korea) (SAC ’21) . Association for Computing Machinery, New York, NY, USA, 1542–1549
Fabio Ferreira, Luciana Lourdes Silva, and Marco Tulio Valente. 2021 · 2021
Closest in time.
Augmenting commit classification by using fine-grained source code changes and a pre-trained deep neural language model
Lobna Ghadhab, Ilyes Jenhani, Mohamed Wiem Mkaouer, and Montassar Ben Messaoud. 2021 · 2021
Closest in time.
A software engineering perspective on engineering machine learning systems: State of the art and challenges
Görkem Giray. 2021 · 2021
Closest in time.
Extracting rules for vulnerabilities detection with static metrics using machine learning
Aakanshi Gupta, Bharti Suri, Vijay Kumar, and Pragyashree Jain. 2021d · 2021
Closest in time.
Tracing Bad Code Smells Behavior Using Machine Learning with Software Metrics
Aakanshi Gupta, Bharti Suri, and Lakshay Lamba. 2021c · 2021
Closest in time.
Clone-advisor: recommending code tokens and clone methods with deep learning and information retrieval
Muhammad Hammad, "̈Onder Babur, Hamid Abdul Basit, and Mark van den Brand. 2021 · 2021
Closest in time.
The rise of software vulnerability: Taxonomy of software vulnerabilities detection and machine learning approaches
Hazim Hanif, Mohd Hairul Nizam Md Nasir, Mohd Faizal Ab Razak, Ahmad Firdaus, and Nor Badrul Anuar. 2021 · 2021
Closest in time.
Action word prediction for neural source code summarization. In 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 330–341
Sakib Haque, Aakash Bansal, Lingfei Wu, and Collin McMillan. 2021 · 2021
Closest in time.
A Survey of Performance Optimization for Mobile Applications
Max Hort, Maria Kechagia, Federica Sarro, and Mark Harman. 2021 · 2021
Closest in time.
Improving performance with hybrid feature selection and ensemble machine learning techniques for code smell detection
Shivani Jain and Anju Saha. 2021 · 2021
Closest in time.
CURE: Code-aware neural machine translation for automatic program repair. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 1161–1173
Nan Jiang, Thibaud Lutellier, and Lin Tan. 2021 · 2021
Closest in time.
A novel four-way approach designed with ensemble feature selection for code smell detection
Inderpreet Kaur and Arvinder Kaur. 2021 · 2021
Closest in time.
Ensemble Models for Neural Source Code Summarization of Subroutines. In 2021 IEEE International Conference on Software Maintenance and Evolution (ICSME) . IEEE, 286–297
Alexander LeClair, Aakash Bansal, and Collin McMillan. 2021 · 2021
Closest in time.
Deep Learning-Based Logging Recommendation Using Merged Code Representation. In IT Convergence and Security , Hyuncheol Kim and Kuinam J. Kim (Eds.). 49–53
Suin Lee, Youngseok Lee, Chan-Gun Lee, and Honguk Woo. 2021 · 2021
Closest in time.
EditSum: A Retrieve-and-Edit Framework for Source Code Summarization. In 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 155–166
Jia Li, Yongmin Li, Ge Li, Xing Hu, Xin Xia, and Zhi Jin. 2021a · 2021
Closest in time.
A Context-based Automated Approach for Method Name Consistency Checking and Suggestion. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 574–586
Yi Li, Shaohua Wang, and Tien N Nguyen. 2021b · 2021
Closest in time.
Context-Aware Code Change Embedding for Better Patch Correctness Assessment
BO LIN, SHANGWEN WANG, MING WEN, and XIAOGUANG MAO. 2021 · 2021
Closest in time.
Improving code summarization with block-wise abstract syntax tree splitting. In 2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) . IEEE, 184–195
Chen Lin, Zhichao Ouyang, Junqing Zhuang, Jianqiang Chen, Hui Li, and Rongxin Wu. 2021 · 2021
Closest in time.
Semantic feature learning via dual sequences for defect prediction
Junhao Lin and Lu Lu. 2021 · 2021
Closest in time.
Improve Classification of Commits Maintenance Activities with Quantitative Changes in Source Code
Richard VR Mariano, Geanderson E dos Santos, and Wladmir Cardoso Brandao. 2021 · 2021
Closest in time.
Applying codebert for automated program repair of java simple bugs. In 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR) . IEEE, 505–509
Ehsan Mashhadi and Hadi Hemmati. 2021 · 2021
Closest in time.
Classifying Code Commits with Convolutional Neural Networks. In 2021 International Joint Conference on Neural Networks (IJCNN) . IEEE, 1–8
Na Meng, Zijian Jiang, and Hao Zhong. 2021 · 2021
Closest in time.
CoreGen: Contextualized Code Representation Learning for Commit Message Generation
Lun Yiu Nie, Cuiyun Gao, Zhicong Zhong, Wai Lam, Yang Liu, and Zenglin Xu. 2021 · 2021
Closest in time.
Machine learning based methods for software fault prediction: A survey
Sushant Kumar Pandey, Ravi Bhushan Mishra, and Anil Kumar Tripathi. 2021 · 2021
Closest in time.
A Comparative Study of Automatic Program Repair Techniques for Security Vulnerabilities. In 2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE) . IEEE, 196–207
Eduard Pinconschi, Rui Abreu, and Pedro Ad textasciitilde ao. 2021 · 2021
Closest in time.
A search-based testing framework for deep neural networks of source code embedding. In 2021 14th IEEE Conference on Software Testing, Verification and Validation (ICST) . IEEE, 36–46
Maryam Vahdat Pour, Zhuo Li, Lei Ma, and Hadi Hemmati. 2021 · 2021
Closest in time.
Software fault prediction based on the dynamic selection of learning technique: findings from the eclipse project study
Santosh S Rathore and Sandeep Kumar. 2021 · 2021
Closest in time.
Multiplicative Weights Algorithms for Parallel Automated Software Repair. In 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS) . IEEE, 984–993
Joseph Renzullo, Westley Weimer, and Stephanie Forrest. 2021 · 2021
Closest in time.
Comparing Commit Messages and Source Code Metrics for the Prediction Refactoring Activities
Priyadarshni Suresh Sagar, Eman Abdulah AlOmar, Mohamed Wiem Mkaouer, Ali Ouni, and Christian D. Newman. 2021 · 2021
Closest in time.
You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion. In 30th USENIX Security Symposium (USENIX Security 21)
R. Schuster, Congzheng Song, Eran Tromer, and Vitaly Shmatikov. 2021 · 2021
Closest in time.
Code smell detection by deep direct-learning and transfer-learning
Tushar Sharma, Vasiliki Efstathiou, Panos Louridas, and Diomidis Spinellis. 2021 · 2021
Closest in time.
QScored: A Large Dataset of Code Smells and Quality Metrics. In 2021 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR) (MSR) . IEEE Computer Society, Los Alamitos, CA, USA, 590–594
T. Sharma and M. Kessentini. 2021 · 2021
Closest in time.
Cross-project smell-based defect prediction
Bruno Sotto-Mayor and Meir Kalech. 2021 · 2021
Closest in time.
Using software metrics for predicting vulnerable classes and methods in Java projects: A machine learning approach
Kazi Zakia Sultana, Vaibhav Anu, and Tai-Yin Chong. 2021 · 2021
Closest in time.
Fast and memory-efficient neural code completion. In 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR) . IEEE, 329–340
Alexey Svyatkovskiy, Sebastian Lee, Anna Hadjitofi, Maik Riechert, Juliana Vicente Franco, and Miltiadis Allamanis. 2021 · 2021
Closest in time.
Predicting design impactful changes in modern code review: A large-scale empirical study. In 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR) . IEEE, 471–482
Anderson Uchôa, Caio Barbosa, Daniel Coutinho, Willian Oizumi, Wesley KG Assunçao, Silvia Regina Vergilio, Juliana Alves Pereira, Anderson Oliveira, and Alessandro Garcia. 2021 · 2021
Closest in time.
Context-aware retrieval-based deep commit message Generation
Haoye Wang, Xin Xia, David Lo, Qiang He, Xinyu Wang, and John Grundy. 2021 · 2021
Closest in time.
Exploiting Method Names to Improve Code Summarization: A Deliberation Multi-Task Learning Approach. In 2021 2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) (ICPC) . 138–148
R. Xie, W. Ye, J. Sun, and S. Zhang. 2021 · 2021
Closest in time.
A Multi-Modal Transformer-based Code Summarization Approach for Smart Contracts. In 2021 2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC) (ICPC) . 1–12
Z. Yang, J. Keung, X. Yu, X. Gu, Z. Wei, X. Ma, and M. Zhang. 2021 · 2021
Closest in time.
Predicting Vulnerability Type in Common Vulnerabilities and Exposures (CVE) Database with Machine Learning Classifiers. In 2021 12th National Conference with International Participation (ELECTRONICA) . 1–6
Veneta Yosifova, Antoniya Tasheva, and Roumen Trifonov. 2021 · 2021
Closest in time.
An empirical study on clone consistency prediction based on machine learning
Fanlong Zhang and Siau-cheng Khoo. 2021 · 2021
Closest in time.
"Ignorance and Prejudice" in Software Fairness. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . 1436–1447
Jie M. Zhang and Mark Harman. 2021 · 2021
Closest in time.
MARS: Detecting brain class/method code smell based on metric–attention mechanism and residual network
Yang Zhang and Chunhao Dong. 2021 · 2021
Closest in time.
Adversarial training and ensemble learning for automatic code summarization
Ziyi Zhou, Huiqun Yu, and Guisheng Fan. 2021 · 2021
Closest in time.
A syntax-guided edit decoder for neural program repair. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 341–353
Qihao Zhu, Zeyu Sun, Yuan-an Xiao, Wenjie Zhang, Kang Yuan, Yingfei Xiong, and Lu Zhang. 2021 · 2021
Closest in time.
Code smells detection using artificial intelligence techniques: A business-driven systematic review
Tomasz Lewowski and Lech Madeyski. 2022 · 2022
Closest in time.
Replication package for Machine Learning for Source Code Analysis survey paper
Tushar Sharma, Maria Kechagia, Stefanos Georgiou, Rohit Tiwari, Indira Vats, Hadi Moazen, and Federica Sarro. 2022 · 2022
Closest in time.
Detection of Web Service Anti-Patterns Using Weighted Extreme Learning Machine
Sahithi Tummalapalli, Lov Kumar, NL Bhanu Murthy, and Aneesh Krishna. 2022 · 2022
Closest in time.
Automatic source code summarization with graph attention networks
Yu Zhou, Juanjuan Shen, Xiaoqing Zhang, Wenhua Yang, Tingting Han, and Taolue Chen. 2022 · 2022
Closest in time.
Summarizing Source Code using a Neural Attention Model. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 2073–2083
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer. 2016 · 2083
Closest in time.