Software Engineering Research Group

This event is hosted by the TU Delft Programming Languages Group as part of their seminar . Abstract: This talk presents a tool for genetic program repair utilizing the rich features provided by Haskell and the available frameworks. It covers basics of program repair, genetic search and typed holes. We then look at our novel approach of type-based program repair using properties and present some …

Abstract: Code completion is an essential feature of IDEs, yet current autocompleters are restricted to either grammar-based or NLP-based single token completions. Both approaches have significant drawbacks: grammar-based autocompletion is very restricted in dynamically-typed language scenarios, whereas NLP-based autocompletion struggles to understand the semantics of the programming language, gi…

Abstract: all Graphs are a rich data source and built the foundation for advanced static analysis that can, for example, detect security vulnerability or dead code. Call Graph generation is usually considered to be a full program analysis: not just the program, but also all its dependencies are processed together. Unfortunately, this is very expensive and makes it hard to run these static analyse…

Abstract Code mixes natural language in identifier names, comments, and stylistic choices (ordering and typesetting) with a formal language that defines a computation. The snippets in each language form a communication channel. Developers read both channels; a CPU processes only the formal channel. These two channels interact and constrain each other. The theory of dual channel constraints elucid…

Ask Maurício for the Zoom link.

Abstract: TBD

Ask Maurício for the Zoom link.

Ask Maurício for the Zoom link.

Ask Maurício for the Zoom link.

For my Go-No-Go I want to share with you what I worked on last year; I show the idea behind my first paper Lampion , my current work with Haskell Program Repair and our concept for a new java bug data-set. You will find the Teams-Invite on Mattermost

Ask Maurício for the Zoom link.

Abstract: The success of Deep Neural Networks with image classification prompted researchers to explore the applications of Deep Learning in Medical Imaging and Medical Image Analysis (MIA). Deep Neural Networks have sufficiently demonstrated their capabilities of performing MIA tasks tirelessly and with fewer errors as opposed to their human counterpart. However the challenge of training neural …

In the last session we looked at how Machine Learning is being used in Software Engineering (specifically, to predict flakiness of tests). In this session, we explore the opposite perspective ie. how is Software Engineering different for ML systems compared to traditional software? The paper for discussion is “ Software Engineering Challenges of Deep Learning ”.

Abstract: In this talk I will go over how we can use Emacs (the mighty text editor) and org-mode (markdown’s big brother) to craft a research workflow. We will look at how we can leverage the power of Emacs and org-mode to capture, store, search and retrieve research data, all in plain text! The talk will also touch upon how org-mode can be used as an environment for literate programming and repr…

A demonstration showing the reliability of serving users recommendations with tradeoff for large music collections, by leveraging diverse Recommender Systems and Evolutionary Algorithms. To sign up for the event you can either fill in this form ( https://forms.gle/icuLgPD8gn6Ej19i9 ) or email s.p.bobde@student.tudelft.nl for the link.

Abstract: Automatically detecting the positions of key-points (e.g., facial key-points or finger key-points) in an image is an essential problem in many applications, such as driver’s gaze detection and drowsiness detection in automated driving systems. With the recent advances of Deep Neural Networks (DNNs), Key-Points detection DNNs (KP-DNNs) have been increasingly employed for that purpose. Ne…

While SQL engines are now capable of detecting a large number of syntactic mistakes, most often semantic errors are not detected, which can lead to serious performance issues or even security vulnerabilities being introduced in the system. This thesis proposes a set of 25 validated heuristics together with a new rule-based static analysis tool for detecting the most common types of semantic bugs …