lunch-talks

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 of the (mixed) results we achieved. In general, the patches are cool but often too fine-grained, but we hope that they can help especially for classroom settings.

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, giving suggestions that ignore a developer’s context. In this work, we present CodeFill, a language model for autocompletion that combines structure and naming information.

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 analyses in build systems that are typically limited in resources.

Abstract: TBD

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 networks using sensitive medical data, without violating the privacy of patients remains an active field of research.

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 reproducible research.

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. Nevertheless, KP-DNN testing and validation have remained a challenging problem because KP-DNNs predict many independent key-points at the same time—where each individual key-point may be critical in the targeted application—and images can vary a great deal according to many factors.

Abstract: In this talk, I will first explain some of my previous studies as a Ph.D. student. In these studies, we introduced multiple techniques to improve automated test generation using various available information sources, such as the source code and manually-written tests. I will also talk about some of the lessons that I learned in my journey as a Ph.D. student. Finally, I briefly talk about the new topics that I am currently working on as a PostDoc.

Abstract: In this talk, we will introduce SynTest - A framework for generating synthetic tests for programs based on JavaScript. One of the main use cases we have been working on lately, is smart contract testing for Solidity. It is very important for smart contracts to be thouroughly tested since after these contracts are deployed, it is impossible to change or update them. Hence any bugs or security exploits in the code will be in the contract for the duration of its lifetime.

Abstract: Researchers and practitioners have designed and implemented various automated test case generators to support effective software testing. Such generators exist for various languages (e.g., Java, C#, or Python) and for various platforms (e.g., desktop, web, or mobile applications). Such generators exhibit varying effectiveness and efficiency, depending on the testing goals they aim to satisfy (e.g., unit-testing of libraries vs. system-testing of entire applications) and the underlying techniques they implement. In this context, practitioners need to be able to compare different generators to identify the most suited one for their requirements, while researchers seek to identify future research directions.