In-IDE code generation models - a literature review

Code generation is becoming one of the most important tools in an Integrated Development Environment (IDE) for programmers, be they novices or experts. It allows them to produce code faster, avoid typos and other mistakes, be more efficient and easily turn ideas into code. As such, there are many in-IDE code generation models to choose from,...

AI in Coding: How can code generation models support developing computational thinking skills?: The use of code generation models in programming support activities

Using AI to support programming has recently gained a lot of popularity. Researchers have been developing tools to support programming activities using GPT models such as ChatGPT and Codex In this paper, we present the most common pro-gramming activities that these models can support. We show that they have a varying range of success<br/>across...

How well does GPT-3.5 perform on course assignments from the TU Delft Computer science and engineering Bachelor?: Finding themes in course assignments GPT-3.5 performs well on and does not perform well on

Since large language models (LLM) have been emerged, they took a present role in today’s soci- ety. From society, they also found their way into the field of education that is why in this research paper, we looked into assignments and exams from the TU Delft Computer science and engineering bach- elor and assessed which problems Generative pre-...

How can large language models and prompt engineering be leveraged in Computer Science education?: Systematic literature review

In recent years, significant progress has been made in the field of natural language processing (NLP) through the development of large language models (LLMs) like BERT and ChatGPT. These models have showcased remarkable abilities across a range of NLP tasks. However, effectively harnessing their potential requires meticulous prompt engineering...

Use of AI-driven code generation models in teaching and learning programming: a systematic literature review

The recent emergence of AI-driven code generation models can potentially transform programming education. To pinpoint the current state of research on using AI code generators to support learning and teaching programming, we conducted a systematic literature review with 21 papers published since 2018. The review presents the teaching and...

Comparing approximate and optimal solution algorithms for the Multi-Level Bin Packing problem

Multi-Level variants of classic optimisation problems are becoming more noteworthy as the complexity of real life applications increases. In this research we investigate the Multi-Level Bin Packing optimisation problem, which models, for example, global logistics and part manufacturing. We will look at the performance of solving Integer Linear...

Solving the Multi-Level Bin Packing Problem with Time Windows using Integer Programming

This paper introduces and studies the Multi-Level Bin Packing Problem with Time Windows. This is a NP-hard problem with real-world applications in logistics. This problem states that items with certain sizes need to be packed inside bins without exceeding their capacity. These bins then again need to be assigned to other bins while minimizing...

Integer Programming Models for the Class Constrained Multi-Level Bin Packing Problem

The Multi-Level Bin Packing problem is a generalisation of the widely-known NP-hard Bin Packing problem. This work aims to investigate approaching this problem, as well as a version including Class Constraints, through integer programming. By modelling the problems in two ways: standard, and with a network flow approach. The studying of the...

Solving Integer Programming Models for the Multi-Level Bin Packing Problem with Conflict Constraints

The Multi-Level Bin Packing problem and its variants are some of the most popular combinatorial optimization problems. They have a wide range of real-life applications yet, they are some of the harder problems we know of. In this paper we solve the Multi-Level Bin Packing Problem and a variant of it, Multi-Level Bin Packing Problem with Conflict...

An Integer Programming Approach to the Multi-Level Bin Packing Problem with Partial Orders

The multi-level bin packing problem (MLBP) and its variant problem with partial orders (MLBPPO) are NP-Hard problems that can be applied in a logistical setting to improve efficiency in packing. However, despite their possible use cases, there is little to no literature on how to solve these problems in a reasonable amount of time. In this work,...

Correspondence Between Perplexity Scores and Human Evaluation of Generated TV-Show Scripts

In recent years many new text generation models have been developed while evaluation of text generation remains a considerable challenge.  Currently, the only metric that is able to fully capture the quality of a generated text is human evaluation, which is expensive and time consuming. One of the most used intrinsic...

Who said that? Comparing performance of TF-IDF and fastText to identify authorship of short sentences

Authorship identification is often applied to large documents, but less so to short, everyday sentences. The ability of identifying who said a short line could provide help to chatbots or personal assistants. This research compares performance of TF-IDF and fastText when identifying authorship of short sentences, by applying these feature...

Is Wikipedia succeeding in reducing gender bias?: Assessing the development of gender bias in word embeddings from Wikipedia

Large text corpora used for creating word embeddings (vectors which represent word meanings) often contain a stereotypical gender bias. This unwanted bias is then also present in the word embeddings and in downstream applications in the field of natural language processing. To prevent and reduce this, more knowledge about the gender bias is...

A Survey On Convolutional Neural Network Explainability Methods

Artificial Intelligence (AI) is increasingly affecting people’s lives. AI is even employed in fields where human lives depend on the AI’s decisions. However, these algorithms lack transparency, i.e. it is unclear how they determine the outcome. If, for instance, the AI’s purpose is to classify an image, the AI will learn this from examples...