Software Engineering 2.0: AI for SE

About the Seminar

Artificial intelligence has gained momentum in software engineering. In the past, AI has already shown to increase effectiveness and efficiency when applying AI on various software engineering tasks. The current state of AI could herald the start of a new era of "programming 2.0" where code is mainly written, tested, and maintained by AI systems. Even better, AI affects the way software engineers plan, develop, acquire, deploy, test, and verify software. Still, machine learning comes with its very own difficulties and pitfalls, so care has to be taken when applying several AI approaches solely "out of the box" on software engineering tasks.

In this course, we address and discuss past, recent, and future advancements through AI in the area of software engineering.

Registration

Registration for the seminar is mandatory. To distribute students among the available seminars offered by the computer science department, you have to select your preferences for a seminar or a proseminar on the central registration platform for seminars and will be automatically assigned to a seminar according to your preferences.

If you are assigned to this seminar, for organizational reasons, you have to sign up both in the course registration form that will be given above and in the LSF (seminar, proseminar). Deadlines for the LSF (HISPOS) registration will be posted in the LSF (HISPOS) portal. Registration is possible up to three weeks after the topic assignment / kick-off.

Kick-off and topic assignment will take place on May 7, at 12:15 in E1 1 room 206 on Zoom. Please note that the schedule can still change due to the current situation with the SARS-CoV-2 virus.

 

Literature

General

• Harman, Mark. The Role of Artificial Intelligence in Software Engineering. IEEE, 2012.
• Ben Lorica, Mike Loukides. What Machine Learning Means for Software Development. 2018.

Topics

• Abdessalem, Raja Ben Nejati, Shiva Briand, Lionel C. Stifter, Thomas. Testing Vision-based Control Systems Using Learnable Evolutionary Algorithms. In Proc. Int. Conf. Software Engineering (ICSE), pages 1016--1026, ACM, 2018.
• Chen, Chunyang Su, Ting Meng, Guozhu Xing, Zhenchang Liu, Yang. From UI Design Image to GUI Skeleton: A Neural Machine Translator to Bootstrap Mobile GUI Implementation. In Proc. Int. Conf. Software Engineering (ICSE), pages 665--676, ACM, 2018.
• Sayyad, Abdel Salam Ingram, Joseph Menzies, Tim Ammar, Hany. Scalable Product Line Configuration: A Straw to Break the Camel's Back. In Proc. Int. Conf. Automated Software Engineering (ASE), pages 465--474, IEEE, 2013
• Tufano, Michele Pantiuchina, Jevgenija Watson, Cody Bavota, Gabriele Poshyvanyk, Denys. On Learning Meaningful Code Changes Via Neural Machine Translation. In Proc. Int. Conf. Software Engineering (ICSE), pages 25--36, IEEE/ACM, 2019.
• Cruciani, Emilio Miranda, Breno Verdecchia, Roberto Bertolino, Antonia. Scalable Approaches for Test Suite Reduction. In Proc. Int. Conf. Software Engineering (ICSE), pages 419--429, IEEE/ACM, 2019.
• Ha, Huong Zhang, Hongyu. DeepPerf: Performance Prediction for Configurable Software with Deep Sparse Neural Network. In Proc. Int. Conf. Software Engineering (ICSE), pages 1095--1106, IEEE/ACM, 2019.
• M. Moein Almasi , Hadi Hemmati, Gordon Fraser, Andrea Arcuri, Janis Benefelds. An Industrial Evaluation of Unit Test Generation: Finding Real Faults in a Financial Application. In Proc. Int. Conf. Software Engineering (ICSE), pages 263--272, IEEE, 2017.
• Wang, Chong Peng, Xin Liu, Mingwei Xing, Zhenchang Bai, Xuefang Xie, Bing Wang, Tuo. A Learning-based Approach for Automatic Construction of Domain Glossary from Source Code and Documentation. In Proc. European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), pages 97--108, ACM, 2019.
• Fucci, Davide Mollaalizadehbahnemiri, Alireza Maalej, Walid. On Using Machine Learning to Identify Knowledge in API Reference Documentation. In Proc. European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), pages 109--119, ACM, 2019.
• Zhang, Xu Xu, Yong Lin, Qingwei Qiao, Bo Zhang, Hongyu Dang, Yingnong Xie, Chunyu Yang, Xinsheng Cheng, Qian Li, Ze Chen, Junjie He,  Xiaoting Yao, Randolph Lou, Jian Guang Chintalapati, Murali Shen, Furao Zhang, Dongmei. Robust Log-based Anomaly Detection on Unstable Log Data. In Proc. European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), pages 807--817, ACM, 2019.
• Zhao, Gang Huang, Jeff. DeepSim: Deep Learning Code Functional Similarity. In Proc. European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), pages 141--151, ACM, 2018.
• Saini, Vaibhav Farmahinifarahani, Farima Lu, Yadong Baldi, Pierre Lopes, Cristina V. Oreo: Detection of Clones in the Twilight Zone. In Proc. European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), pages 354--365, ACM, 2018.
• Jamshidi, Pooyan Siegmund, Norbert Velez, Miguel Kästner, Christian Patel, Akshay Agarwal, Yuvraj. Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory Analysis. In Proc. Int. Conf. Automated Software Engineering (ASE), pages 497--508, IEEE, 2017.
• Guo, Jianmei Yang, Dingyu Siegmund, Norbert Apel, Sven Sarkar, Atrisha Valov, Pavel Czarnecki, Krzystof Wasowski, Anrzej Yu, Huiqun. Data-efficient Performance Learning for Configurable Systems. In Empirical Software Engineering (ESE), pages 1826--1867, 2018.