Correction to: The potential of an adaptive computerized dynamic assessment tutor in diagnosing and assessing learners’ listening comprehension (Education and Information Technologies, (2024), 29, 3, (3637-3661), 10.1007/s10639-023-11871-w)

In the PDF of this article, the pages were incorrectly numbered as ‘2303–2327’ when it should have been ‘3637–3661’. The page range was found to be just correct in the HTML version of the article. The original article has been corrected.

Targeted Attack on GPT-Neo for the SATML Language Model Data Extraction Challenge [PRESENTATION]

Previous work has shown that Large Language Models are susceptible to so-called data extraction attacks. This allows an attacker to extract a sample that was contained in the training data, which has massive privacy implications. The construction of data extraction attacks is challenging, current attacks are quite inefficient, and there exists a...

Semantically-enhanced topic recommendation systems for software projects

Software-related platforms such as GitHub and Stack Overflow, have enabled their users to collaboratively label software entities with a form of metadata called topics. Tagging software repositories with relevant topics can be exploited for facilitating various downstream tasks. For instance, a correct and complete set of topics assigned to a...

The NLBSE'23 Tool Competition

We report on the organization and results of the second edition of the tool competition from the International Workshop on Natural Language-based Software Engineering (NLBSE'23). As in the prior edition, we organized the competition on automated issue report classification, with a larger dataset. This year, we featured an extra competition on...

Extending Source Code Pre-Trained Language Models to Summarise Decompiled Binaries

Binary reverse engineering is used to understand and analyse programs for which the source code is unavailable. Decompilers can help, transforming opaque binaries into a more readable source code-like representation. Still, reverse engineering is difficult and costly, involving considering effort in labelling code with helpful summaries....

Enriching Source Code with Contextual Data for Code Completion Models: An Empirical Study

Transformer-based pre-trained models have recently achieved great results in solving many software engineering tasks including automatic code completion which is a staple in a developer’s toolkit. While many have striven to improve the code-understanding abilities of such models, the opposite – making the code easier to understand – has not been...

The potential of an adaptive computerized dynamic assessment tutor in diagnosing and assessing learners’ listening comprehension

In today’s environment of growing class sizes due to the prevalence of online and e-learning systems, providing one-to-one instruction and feedback has become a challenging task for teachers. Anyhow, the dialectical integration of instruction and assessment into a seamless and dynamic activity can provide a continuous flow of assessment...

Correction to: The potential of an adaptive computerized dynamic assessment tutor in diagnosing and assessing learners’ listening comprehension (Education and Information Technologies, (2024), 29, 3, (3637-3661), 10.1007/s10639-023-11871-w)

The copyright holder in the original publication of this article was incorrect. The original article has been corrected.

STACC: Code Comment Classification using SentenceTransformers

Code comments are a key resource for information about software artefacts. Depending on the use case, only some types of comments are useful. Thus, automatic approaches to clas-sify these comments have been proposed. In this work, we address this need by proposing, STACC, a set of SentenceTransformers- based binary classifiers. These...

The (ab)use of Open Source Code to Train Large Language Models

In recent years, Large Language Models (LLMs) have gained significant popularity due to their ability to generate human-like text and their potential applications in various fields, such as Software Engineering. LLMs for Code are commonly trained on large unsanitized corpora of source code scraped from the Internet. The content of these datasets...

On the Evaluation of NLP-based Models for Software Engineering

NLP-based models have been increasingly incorporated to address SE problems. These models are either employed in the SE domain with little to no change, or they are greatly tailored to source code and its unique characteristics. Many of these approaches are considered to be outperforming or complementing existing solutions. However, an important...

CatIss: An Intelligent Tool for Categorizing Issues Reports using Transformers

Users use Issue Tracking Systems to keep track and manage issue reports in their repositories. An issue is a rich source of software information that contains different reports including a problem, a request for new features, or merely a question about the software product. As the number of these issues increases, it becomes harder to manage...

CodeFill: Multi-token Code Completion by Jointly learning from Structure and Naming Sequences

Code completion is an essential feature of IDEs, yet current auto-completers are restricted to either grammar-based or NLP-based single token completions. Both approaches have significant draw-backs: grammar-based autocompletion is restricted in dynamically-typed language environments, whereas NLP-based autocompleters struggle to understand...