Novice programmers often struggle through programming problem solving due to a lack of metacognitive awareness and strategies. Previous research has shown that novices can encounter multiple metacognitive difficulties while programming, such as forming ...

Programming skills are typically developed through completing various hands-on exercises. Such programming problems can be contextualized to students’ interests and cultural backgrounds. Prior research in educational psychology has demonstrated that ...

BACKGROUND AND CONTEXT. The pace of advancement of large language models (LLMs) motivates the use of existing infrastructure to automate the evaluation of LLM performance on computing education tasks. Concept inventories are well suited for evaluation ...

Debugging is a crucial skill for programmers, yet it can be challenging for novices to learn. The introduction of large language models (LLMs) has opened up new possibilities for providing personalized debugging support to students. However, concerns ...

Smart contracts are susceptible to various vulnerabilities that can lead to significant financial losses. The usage of tools for vulnerabilities is reducing the threats but presents some limitations related to the approach used by the tool itself. This ...

Wireless embedded systems have seen rapid growth. Nonetheless, the further growth of these systems is now threatened due to the complexities of programming, networking, and deploying them. Developing embedded systems today is a challenging task primarily ...

Network configuration is crucial for computer network design and operation. Network Operations (NetOps) teams typically use vendor-specific configuration languages to create and deploy scripts that achieve specific network intents, such as reachability ...

Geolocating network devices is essential for various research areas. Yet, despite notable advancements, it continues to be one of the most challenging issues for experimentalists. An approach for geolocating that has proved effective is leveraging ...

As large language models (LLMs) take on complex tasks, their inputs are supplemented with longer contexts that incorporate domain knowledge. Yet using long contexts is challenging as nothing can be generated until the whole context is processed by the ...

Serendipity means an unexpected but valuable discovery. Its elusive nature makes it susceptible to modeling. In this paper, we address the challenge of modeling serendipity in recommender systems using Large Language Models (LLMs), a recent breakthrough ...

Current IR evaluation paradigms are challenged by large language models (LLMs) and retrieval-augmented generation (RAG) methods. Furthermore, evaluation either resorts to expensive human judgments or lead to an over-reliance on LLMs.

To remedy this ...

Large Language Models (LLMs) have emerged as powerful generative Artificial Intelligence solutions. This paper presents results and reflections of an experiment done with the LLM GPT 3.5-Turbo to perform an inductive Thematic Analysis (TA). Previous ...

In empirical software engineering (EMSE), various activities require human participation, such as data collection, processing, analysis, and comprehension. On one hand, these processes are time-consuming and labor-intensive. On the other hand, human ...

Navigation aids such as headers and internal links provide vital support for screen-reader users on web documents to grasp a document’s structure. However, when such navigation aids are unavailable or not appropriately marked up, this situation can cause ...

Challenges related to game quality, whether occurring during initial release or after updates, can result in player dissatisfaction, media scrutiny, and potential financial setbacks. These issues may stem from factors like software bugs, performance ...

This study investigates the use of the GPT-4 Turbo, a large language model, to enhance the Self-Organizing Migrating Algorithm (SOMA), specifically its All to All variant (SOMA-ATA). Utilizing the model's extensive context capacity for iterative ...

In this study, we investigate the applicability, challenges, and effectiveness of the advanced large language model GPT 4 Turbo in enhancing the selected metaheuristic algorithm, which is Differential Evolution. Our research primarily examines whether ...

Repairing bugs can be a daunting task for even a human experienced in debugging, so naturally, attempting to automatically repair programs with a computer system is quite challenging. The existing methods of automated program repair leave a lot of room ...

Hyperparameter optimization is a crucial problem in Evolutionary Computation. In fact, the values of the hyperparameters directly impact the trajectory taken by the optimization process, and their choice requires extensive reasoning by human operators. ...

Code generation is one of the most valuable applications of AI, as it allows for automated programming and "self-building" programs. Both Large Language Models (LLMs) and evolutionary methods, such as Genetic Programming (GP) and Grammatical Evolution (...