Software repositories archive valuable software engineering data, such as source code, execution traces, historical code changes, mailing lists, and bug reports. This data contains a wealth of information about a project’s status and history. Doing data science on software repositories, researchers can gain empirically based understanding of software development practices, and practitioners can better manage, maintain, and evolve complex software projects.
In the recent years, the advances in Machine Learning and AI technologies, as demonstrated by the successful application of Deep Neural Networks in various domains did not go unnoticed in the field of Software Engineering. Researchers have applied DNNs to tackle issues such as automated program repair, code summarization, code completion, code structure representation, etc.
IN4334 is a seminar course that aims to give students a deep understanding of and hands-on approach on how deep neural networks and NLP techniques are used to represent knowledge and solve existing SE problems in novel ways.
This course will enable students to:
We welcome all students that are willing to dive into cutting edge ML research.
We expect students to have experience with ML already. We also expect them to have some interest in program analysis. This is not an introductory course.
Due to resource constraints, the course is strictly offered for 40 students. If we have more than 40 registrations, we will apply CV-based selection. Students with demonstrable experience (i.e., GitHub repos) in ML will be preferred.
5 ECTS: This means that you need to devote at least 140 hours of study for this course, per person. Given that the course runs in a period of 7 weeks, the workload is around 20 hours a week.
Reading sessions: The course consists of lectures and reading sessions. You are not required, but you are strongly encouraged, to attend. Per meeting, we will be discussing 1 paper (presentations given either by the lecturer or by teams) in terms of techniques, insights and impact.
Homework: Before each lecture, you must read and prepare questions about the papers that will be discussed during the lecture. You can find the list of the papers to read on the beginning of each week’s lecture. You must also watch/read any material pointed to by the sylabus.
Please keep in mind that you are attending this course on voluntary basis. Coming to the classroom unprepared will not be the best use of your time, so do your homework first!
Lecturers: The course is supervised by Georgios Gousios and Maliheh Izadi, who are responsible for the content and the assignments. Several people will provide help in topics of their expertise.
Course deliverables: To finish the course you will need to:
Groups: You will work in groups of 4-5 persons. You are free to choose your group partners; if this is not possible, the course lecturers will assign you to a group.
Labs: Unsupervised, optional. 4 hours per week, designed to give you a place and time to work together. No feedback will be provided during lab hours by the lecturers.
Lately, machine learning techniques have been successfully tailored to many software engineering problems. For instance, intelligent code completion helps developers finish their programming tasks faster and more efficiently by decreasing the typing effort, providing type-correct solutions, and enabling them to explore APIs. InCoder, UniXcoder, and CoPilot are among the most recent deep learning-based solutions for an enhanced software development experience. In this project, we aim to tailor pre-trained language models for source code to solve software engineering tasks including code completion, type completion, and code summarization. Each gorup will fine-tune a pre-trained model for the specific task at hand. Then, you will evaluate your model on the provided test set. As for the dataset, you will use the benchmark datasets provided by CodeXGlue, the General Language Understanding Evaluation benchmark for CODE. If you aim to use your models on more languages or data scoures, you should use other publicly available datasets or scrape and proeprocess the new data yourself.
You will implement different ML/DL models. You are required to use Python and more specifically, Pytorch. Check our curated list of tutorials that might help you in getting started with different NLP, DL, and ML topics.
|6/9||1||1||Course Introduction, How to read a paper in a group, DeepBugs||GG|
|9/9||1||2||Representing source code as text, Naturalness of software||GG|
|13/9||2||3||Large language models and alt representations, Code2Seq||GG|
|16/9||2||4||Graph Neural Networks: Introduction, Learning to represent programs with graphs||MA|
|20/9||3||5||Code Understanding and Generation: CodeT5||MI|
|23/9||3||6||Code Representation: UniXcoder||MI|
|27/9||4||7||Code Filling: InCoder||GG / MI|
|30/9||4||8||Code summarization: On the Evaluation of Neural Code Summarization||GG / MI|
|4/10||5||9||Type prediction: Type4Py||AM / GG|
|7/10||5||10||Feedback session||GG / MI|
|11/10||6||11||Type prediction: HiTyper||AM|
|14/10||6||12||Reverse engineering Learning to Find Usages of Library Functions in Optimized Binaries||AS|
|18/10||7||13||Software Effort Estimation: Heterogeneous Graph Neural Networks for Software Effort Estimation||EK / GG|
|21/10||7||14||MI / GG|
|28/10||8||15||Presentation day||GG / MI|
The course grade will be calculated as:
The final papers will be peer-reviewed by 2 other teams.
Here are some resources for extra study, if you are interested in the field:
The course contents are copyrighted (c) 2018,2019,2020 - onwards by TU Delft and their respective authors and licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license.