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Schedule

Course Schedule

Note

Students can download the papers from this page. Please note that the paper downloads are password protected.

Week

Class Theme

Focus Topic

Readings

Presenter

Lecture Materials

Deadlines
Week 1 - January 27th Class Introduction and Overview of Research Area Introduction to Deep Learning and Software Engineering
  • Watson-TOSEM'22
  • Kevin
Week 2 - February 3rd From Canonical ML to DL of Source Code Modeling Source Code
  • Hindle-ICSE'12
  • White-MSR'15
  • Tufano-MSR'18
  • Kevin
 Paper Presentation Selections Due
Week 3 - February 10th New Horizons in Deep Code Representation Modeling Source Code
  • Tufano-ICSE'19
  • Ahmed-ICSE'22
  • Hellendoorn-ICLR'20
Week 4 - February 17th Augmenting Developers with DL Powered Tools Automating Code-related Tasks
  • Chen-TSE'19
  • Ciborowska-ICSE'22
  • Shetty-ICSE'22
Week 5 - February 24th Augmenting Developers with DL Powered Tools Automating Code-related Tasks
  • Wang-ICSE'22
  • Mastropaolo-ICSE'21
  • Ye-ICSE'22
 Plan of Work & Related Work Due
Week 6 - March 3rd Enhancing Developers: Building Human-centric AI Developer Tools Human Aspects of DL Tools for Code
  • Xu-TOSEM-21
  • Hu-ICSE'22
  • Noller-ICSE'22
Week 7 - March 10th Under the Hood: What do DL Models Learn about Code? Interpretability of AI Tools for Code
  • Rabin-FSE'21
  • Wan-ICSE'22
  • Gros-ASE'20
 Approach Description Due
Week 8 - March 17th
No Class Meeting - Spring Break
Week 9 - March 24th (Deep) Learning to Test Programs Deep Learning for Software Testing
  • Chen-FSE'21
  • Tufano-ICSE'22
  • Mariani-ISSTA'21
 Design of Experiments/Case Studies is Due
Week 10 - March 31st A Picture is Worth a Thousand Words: Learning to Understand GUIs Deep Learning for UI Analysis
  • Moran-TSE'18
  • Wang-UIST'21
  • Wu-UIST'21
Week 1 - April 7th How Can We Help You? Learning to Assist with Bug Report Management Deep Learning for Bug Reporting
  • Chaparro-FSE'19
  • Cooper-ICSE'21
  • Feng-ICSE'22
Week 12 - April 14th Software for All: Leveraging Deep Learning to Create Accessible Software Deep Learning for Software Accessibility
  • Zhang-CHI'21
  • WU-W4A'21
  • Mehralian-FSE'21
 Preliminary Results are Due
Week 13 - April 21st Towards Fairer Models: Examining Bias and Faults in Deep Learning Programs Software Engineering for Deep Learning Systems
  • Biaswas-FSE'21
  • Chakraborty-FSE'21
  • Wardat-ICSE'22
Week 14 - April 28th Going Deeper: Examining Faults and Testing of Neural Networks Software Testing for Deep Learning Systems
  • Luo-ICSE'21
  • Meng-ICSE'21
  • Humbavota-ISSTA'21
Week 15 - May 5th
Final Project Presentations
 Final Result Presentations in Class
Week 16 - May 12th
No Class Meeting - Work on Finalizing Project Reports
 Final Reports are Due

Week 1 - Jan 27th - Class Introduction and Overview of Research Area
  • Watson-TOSEM'22: Cody Watson, Nathan Cooper, David Nader Palacio, Kevin Moran, & Denys Poshyvanyk, "A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research," In Transactions on Software Engineering & Methodology (TOSEM 2022).
Week 2 - Feb 3rd - From Canonical ML to DL of Source Code
  • Hindle-ICSE'12: Abram Hindle, Earl T. Barr, Zhendong Su, Mark Gabel, and Premkumar Devanbu. 2012. "On the Naturalness of Software". In Proceedings of the 34th International Conference on Software Engineering (ICSE '12). IEEE Press, 837–847.
  • White-MSR'15: Martin White, Christopher Vendome, Mario Linares-Vásquez, and Denys Poshyvanyk. 2015. "Toward Deep Learning Software Repositories". In Proceedings of the 12th Working Conference on Mining Software Repositories (MSR '15). IEEE Press, 334–345.
  • Tufano-MSR'18: Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk. 2018. "Deep Learning Similarities from Different Representations of Source Code". In Proceedings of the 15th International Conference on Mining Software Repositories (MSR '18). Association for Computing Machinery, New York, NY, USA, 542–553.
Week 3 - Feb 10th - New Horizons in Deep Code Representation
  • Tufano-ICSE'19: Michele Tufano, Jevgenija Pantiuchina, Cody Watson, Gabriele Bavota, and Denys Poshyvanyk. 2019. "On Learning Meaningful Code Changes via Neural Machine Translation," In Proceedings of the 41st International Conference on Software Engineering (ICSE '19). IEEE Press, 25–36.
  • Ahmed-ICSE'22: Toufique Ahmed, and Premkumar Devanbu. "Multilingual Training for Software Engineering," In Proceedings of the 44th ACM/IEEE International Conference on Software Engineering (ICSE 2022), May 22–27, 2022, Pittsburgh, PA, USA.
  • Hellendoorn-ICLR'20: Vincent Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, & David Bieber. "Global Relational Models of Source Code", In proceedings of the 2020 International Conference on Learning Representations (ICLR'20).
Week 4 - Feb 17th - Augmenting Developers with DL Powered Tools
  • Chen-TSE'19: Zimin Chen, Steve. Kommrusch, Michele. Tufano, Loius-Noel Pouchet, Denys Poshyvanyk and Martin Monperrus, "SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair," in IEEE Transactions on Software Engineering, vol. 47, no. 9, pp. 1943-1959, 1 Sept. 2021
  • Ciborowska-ICSE'22: Agnieszka Ciborowska, and Kostadin Damevski. "Fast Changeset-based Bug Localization with BERT", In Proceedings of the 44th ACM/IEEE International Conference on Software Engineering (ICSE 2022), May 22–27, 2022, Pittsburgh, PA, USA.
  • Shetty-ICSE'22: Manish Shetty, Chetan Bansal, Suman Nath, Sean Bowles, Henry Wang, Ozgur Arman, Siamak Ahari, “DeepAnalyze: Learning to Localize Crashes at Scale,” In Proceedings of the 44th ACM/IEEE International Conference on Software Engineering (ICSE 2022), May 22–27, 2022, Pittsburgh, PA, USA.
Week 5 - Feb 24th - Augmenting Developers with DL Powered Tools
  • Wang-ICSE'22: Deze Wang, Zhouyang Jia, Shanshan, Li, Yue Yu, Yun Xiong, Wei Dong, and Xiangke Liao, "Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding," In Proceedings of the 44th ACM/IEEE International Conference on Software Engineering (ICSE 2022), May 22–27, 2022, Pittsburgh, PA, USA.
  • Mastropaolo-ICSE'21: Antonio Mastropaolo, Simone Scalabrino, Nathan Cooper, David Nader Palacio, Denys Poshyvanyk, Rocco Oliveto, and Gabriele Bavota, "Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks," 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE), 2021, pp. 336-347
  • Ye-ICSE'22: He Ye, Matias Martinez, Martin Monperrus, "Neural Program Repair with Execution-based Backpropagation," In Proceedings of the 44th ACM/IEEE International Conference on Software Engineering (ICSE 2022), May 22–27, 2022, Pittsburgh, PA, USA.
Week 6 - March 3rd - Enhancing Developers: Building Human-centric AI Developer Tools
  • Xu-TOSEM-21: Xu, F. F., Vasilescu, B., & Neubig, G. (2021). "In-IDE Code Generation from Natural Language: Promise and Challenges," In ACM Transactions on Software Engineering and Methodology (TOSEM). Vol 37, No. 4, 2021
  • Hu-ICSE'22: Xing Hu, Xin Xia, David Lo, Zhiyuan Wan, Qiuyuan Chen, Tom Zimmermann. “Practitioners’ Expectations on Automated Code Comment Generation”. In Proceedings of the 44th ACM/IEEE International Conference on Software Engineering (ICSE 2022), May 22–27, 2022, Pittsburgh, PA, USA.
  • Noller-ICSE'22: Yannic Noller, Ridwan Shariffdeen, Xiang Gao, Abhik Roychoudhury, "Trust Enhancement Issues in Program Repair," In Proceedings of the 44th ACM/IEEE International Conference on Software Engineering (ICSE 2022), May 22–27, 2022, Pittsburgh, PA, USA.
Week 7 - March 10th - Under the Hood: What do DL Models Learn about Code?
  • Rabin-FSE'21: Md Rafiqul Islam Rabin, Vincent J. Hellendoorn, and Mohammad Amin Alipour. 2021. "Understanding Neural Code Intelligence through Program Simplification". In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2021). Association for Computing Machinery, New York, NY, USA, 441–452.
  • Wan-ICSE'22: Yao Wan, Wei Zhao, Hongyu Zhang, Yulei Sui, Guandong Xu and Hai Jin, "What Do They Capture? - A Structural Analysis of Pre-Trained Language Models for Source Code", In proceedings of the 44th ACM/IEEE International Conference on Software Engineering (ICSE 2022), May 22–27, 2022, Pittsburgh, PA, USA.
  • Gros-ASE'20: David Gros, Hariharan Sezhiyan, Prem Devanbu, and Zhou Yu. 2020. "Code to Comment "Translation": Data, Metrics, Baselining & Evaluation," In Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering (ASE 2020). Association for Computing Machinery, New York, NY, USA, 746–757.
Week 9 - March 24th - (Deep) Learning to Test Programs
  • Chen-FSE'21: Ke Chen, Yufei Li, Yingfeng Chen, Changjie Fan, Zhipeng Hu, and Wei Yang. 2021. "GLIB: Towards Automated Test Oracle for Graphically-rich Applications," In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2021). Association for Computing Machinery, New York, NY, USA, 1093–1104.
  • Tufano-ICSE'22: Rosalia Tufano, Simone Scalabrino, Luca Pascarella, Emad Aghajani, Rocco Oliveto, and Gabriele Bavota, "Using Reinforcement Learning for Load Testing of Video Games," In Proceedings of the 44th ACM/IEEE International Conference on Software Engineering (ICSE 2022), May 22–27, 2022, Pittsburgh, PA, USA.
  • Mariani-ISSTA'21: Leonardo Mariani, Ali Mohebbi, Mauro Pezzè, and Valerio Terragni. 2021. "Semantic Matching of GUI Events for Test Reuse: Are We There Yet?," In Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2021). Association for Computing Machinery, New York, NY, USA, 177–190.
Week 10 - March 31st - A Picture is Worth a Thousand Words: Learning to Understand GUIs
  • Moran-TSE'18: Kevin Moran, Ccarlos Bernal-Cárdenas, Michael Curcio, Ricahrd Bonett and Denys Poshyvanyk, "Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps," in IEEE Transactions on Software Engineering, vol. 46, no. 2, pp. 196-221, 1 Feb. 2020
  • Wang-UIST'21: Bryan Wang, Gang Li, Xin Zhou, Zhourong Chen, Tovi Grossman, and Yang Li. 2021. "Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning," The 34th Annual ACM Symposium on User Interface Software and Technology (UIST 2021). Association for Computing Machinery, New York, NY, USA, 498–510.
  • Wu-UIST'21: Jason Wu, Xiaoyi Zhang, Jeff Nichols, and Jeffrey P Bigham. 2021. "Screen Parsing: Towards Reverse Engineering of UI Models from Screenshots," The 34th Annual ACM Symposium on User Interface Software and Technology (UIST'21). Association for Computing Machinery, New York, NY, USA, 470–483.
Week 11 - April 7th - How Can We Help You? Learning to Assist with Bug Report Management
  • Chaparro-FSE'19: Oscar Chaparro, Carlos Bernal-Cárdenas, Jing Lu, Kevin Moran, Andrian Marcus, Massimiliano Di Penta, Denys Poshyvanyk, and Vincent Ng. 2019. "Assessing the Quality of the Steps to Reproduce in Bug Reports," In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2019). Association for Computing Machinery, New York, NY, USA, 86–96.
  • Cooper-ICSE'21: Nathan Cooper, Carlos Bernal-Cárdenas, Oscar Chaparro, Kevin Moran, and Denys Poshyvanyk. 2021. "It Takes Two to Tango: Combining Visual and Textual Information for Detecting Duplicate Video-Based Bug Reports," Proceedings of the 43rd International Conference on Software Engineering (ICSE 2021). IEEE Press, 957–969.
  • Feng-ICSE'22: Feng, Sidong, and Chunyang Chen. "GIFdroid: Automated Replay of Visual Bug Reports for Android Apps," In Proceedings of the 44th ACM/IEEE International Conference on Software Engineering (ICSE 2022), May 22–27, 2022, Pittsburgh, PA, USA.
Week 12 - April 14th - Software for All: Leveraging Deep Learning to Create Accessible Software
  • Zhang-CHI'21: Xiaoyi Zhang, Lilian de Greef, Amanda Swearngin, Samuel White, Kyle Murray, Lisa Yu, Qi Shan, Jeffrey Nichols, Jason Wu, Chris Fleizach, Aaron Everitt, and Jeffrey P Bigham. 2021. "Screen Recognition: Creating Accessibility Metadata for Mobile Applications from Pixels," In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI '21). Association for Computing Machinery, New York, NY, USA, Article 275, 1–15.
  • Wu-W4A'21: Jason Wu, Gabriel Reyes, Sam C. White, Xiaoyi Zhang, and Jeffrey P. Bigham. 2021. "When Can Accessibility Help? an Exploration of Accessibility Feature Recommendation on Mobile Devices". In Proceedings of the 18th International Web for All Conference (W4A '21). Association for Computing Machinery, New York, NY, USA, Article 21, 1–12.
  • Mehralian-FSE'21: Forough Mehralian, Navid Salehnamadi, and Sam Malek. 2021. "Data-driven Accessibility Repair Revisited: On the Effectiveness of Generating Labels for Icons in Android Apps". In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2021). Association for Computing Machinery, New York, NY, USA, 107–118.
Week 13 - April 21st - Towards Fairer Models: Examining Bias and Faults in Deep Learning Programs
  • Biaswas-FSE'21: Sumon Biswas and Hridesh Rajan. 2021. "Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline," In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2021). Association for Computing Machinery, New York, NY, USA, 981–993.
  • Chakraborty-FSE'21: Joymallya Chakraborty, Suvodeep Majumder, and Tim Menzies. 2021. "Bias in Machine Learning Software: Why? How? What to do?," In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2021). Association for Computing Machinery, New York, NY, USA, 429–440.
  • Wardat-ICSE'22: Mohammad Wardat, Breno Dantas Cruz, Wei Le, and Hridesh Rajan, "DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs," In Proceedings of the 44th ACM/IEEE International Conference on Software Engineering (ICSE 2022), May 22–27, 2022, Pittsburgh, PA, USA.
Week 14 - April 28th - Going Deeper: Examining Faults and Testing of Neural Networks
  • Luo-ICSE'21: Weisi Luo, Dong Chai, Xiaoyue Ruan, Jiang Wang, Chunrong Fang and Zhenyu Chen, "Graph-Based Fuzz Testing for Deep Learning Inference Engines," In Proceedings of the 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE 2021), 2021, pp. 288-299
  • Meng-ICSE'21: Linghan Meng, Yanhui Li, Lin Chen, Zhi Wang, Di Wu, Yuming Zhou, and Baowen Xu. 2021. "Measuring Discrimination to Boost Comparative Testing for Multiple Deep Learning Models," In Proceedings of the 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE 2021). IEEE Press, 385–396.
  • Humbavota-ISSTA'21: Nargiz Humbatova, Gunel Jahangirova, and Paolo Tonella. 2021. "DeepCrime: Mutation Testing of Deep Learning Systems Based on Real Faults," In Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2021). Association for Computing Machinery, New York, NY, USA, 67–78.