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- research-articleAugust 2024
The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers
- James Prather,
- Brent N Reeves,
- Juho Leinonen,
- Stephen MacNeil,
- Arisoa S Randrianasolo,
- Brett A. Becker,
- Bailey Kimmel,
- Jared Wright,
- Ben Briggs
ICER '24: Proceedings of the 2024 ACM Conference on International Computing Education Research - Volume 1August 2024, Pages 469–486https://doi.org/10.1145/3632620.3671116Novice 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 ...
- research-articleAugust 2024
Evaluating Contextually Personalized Programming Exercises Created with Generative AI
ICER '24: Proceedings of the 2024 ACM Conference on International Computing Education Research - Volume 1August 2024, Pages 95–113https://doi.org/10.1145/3632620.3671103Programming 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 ...
- research-articleAugust 2024
Using Benchmarking Infrastructure to Evaluate LLM Performance on CS Concept Inventories: Challenges, Opportunities, and Critiques
ICER '24: Proceedings of the 2024 ACM Conference on International Computing Education Research - Volume 1August 2024, Pages 452–468https://doi.org/10.1145/3632620.3671097BACKGROUND 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 ...
- research-articleAugust 2024
Debugging with an AI Tutor: Investigating Novice Help-seeking Behaviors and Perceived Learning
ICER '24: Proceedings of the 2024 ACM Conference on International Computing Education Research - Volume 1August 2024, Pages 84–94https://doi.org/10.1145/3632620.3671092Debugging 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 ...
- research-articleAugust 2024
Smart Contract Vulnerability Detection: The Role of Large Language Model (LLM)
ACM SIGAPP Applied Computing Review (SIGAPP), Volume 24, Issue 2June 2024, Pages 19–29https://doi.org/10.1145/3687251.3687253Smart 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 ...
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- short-paperAugust 2024
POSTER: Simplifying the Networking of Wireless Embedded Systems using a Large Language Model
ACM SIGCOMM Posters and Demos '24: Proceedings of the ACM SIGCOMM 2024 Conference: Posters and DemosAugust 2024, Pages 78–80https://doi.org/10.1145/3672202.3673752Wireless 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 ...
- short-paperAugust 2024
Poster: Automating Network Configuration with Natural Language Intents
ACM SIGCOMM Posters and Demos '24: Proceedings of the ACM SIGCOMM 2024 Conference: Posters and DemosAugust 2024, Pages 19–21https://doi.org/10.1145/3672202.3673721Network 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 ...
- short-paperAugust 2024
POSTER: Revealing Hidden Secrets: Decoding DNS PTR records with Large Language Models
ACM SIGCOMM Posters and Demos '24: Proceedings of the ACM SIGCOMM 2024 Conference: Posters and DemosAugust 2024, Pages 10–12https://doi.org/10.1145/3672202.3673717Geolocating 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 ...
- research-articleAugust 2024
CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving
- Yuhan Liu,
- Hanchen Li,
- Yihua Cheng,
- Siddhant Ray,
- Yuyang Huang,
- Qizheng Zhang,
- Kuntai Du,
- Jiayi Yao,
- Shan Lu,
- Ganesh Ananthanarayanan,
- Michael Maire,
- Henry Hoffmann,
- Ari Holtzman,
- Junchen Jiang
ACM SIGCOMM '24: Proceedings of the ACM SIGCOMM 2024 ConferenceAugust 2024, Pages 38–56https://doi.org/10.1145/3651890.3672274As 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 ...
- research-articleAugust 2024
The Art of Asking: Prompting Large Language Models for Serendipity Recommendations
ICTIR '24: Proceedings of the 2024 ACM SIGIR International Conference on Theory of Information RetrievalAugust 2024, Pages 157–166https://doi.org/10.1145/3664190.3672521Serendipity 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 ...
- research-articleAugust 2024
Pencils Down! Automatic Rubric-based Evaluation of Retrieve/Generate Systems
ICTIR '24: Proceedings of the 2024 ACM SIGIR International Conference on Theory of Information RetrievalAugust 2024, Pages 175–184https://doi.org/10.1145/3664190.3672511Current 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 ...
- research-articleAugust 2024
Performing an Inductive Thematic Analysis of Semi-Structured Interviews With a Large Language Model: An Exploration and Provocation on the Limits of the Approach
Social Science Computer Review (SSCR), Volume 42, Issue 4Aug 2024, Pages 997–1019https://doi.org/10.1177/08944393231220483Large 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 ...
- research-articleJuly 2024
An Exploratory Evaluation of Large Language Models Using Empirical Software Engineering Tasks
Internetware '24: Proceedings of the 15th Asia-Pacific Symposium on InternetwareJuly 2024, Pages 31–40https://doi.org/10.1145/3671016.3674821In 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 ...
- research-articleJuly 2024
In-Page Navigation Aids for Screen-Reader Users with Automatic Topicalisation and Labelling
- Jorge Sassaki Resende Silva,
- Paula Christina Figueira Cardoso,
- Raphael Winckler De Bettio,
- Daniela Cardoso Tavares,
- Carlos Alberto Silva,
- Willian Massami Watanabe,
- AndrÉ Pimenta Freire
ACM Transactions on Accessible Computing (TACCESS), Volume 17, Issue 2Article No.: 12, Pages 1–45https://doi.org/10.1145/3649223Navigation 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 ...
- research-articleJuly 2024
Unit Test Generation using Large Language Models for Unity Game Development
FaSE4Games 2024: Proceedings of the 1st ACM International Workshop on Foundations of Applied Software Engineering for GamesJuly 2024, Pages 7–13https://doi.org/10.1145/3663532.3664466Challenges 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 ...
- research-articleAugust 2024
Using LLM for Automatic Evolvement of Metaheuristics from Swarm Algorithm SOMA
GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2024, Pages 2018–2022https://doi.org/10.1145/3638530.3664181This 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 ...
- research-articleAugust 2024
A Critical Examination of Large Language Model Capabilities in Iteratively Refining Differential Evolution Algorithm
GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2024, Pages 1855–1862https://doi.org/10.1145/3638530.3664179In 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 ...
- research-articleAugust 2024
LLM Fault Localisation within Evolutionary Computation Based Automated Program Repair
GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2024, Pages 1824–1829https://doi.org/10.1145/3638530.3664174Repairing 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 ...
- research-articleAugust 2024
An investigation on the use of Large Language Models for hyperparameter tuning in Evolutionary Algorithms
GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2024, Pages 1838–1845https://doi.org/10.1145/3638530.3664163Hyperparameter 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. ...
- research-articleAugust 2024
Comparing Large Language Models and Grammatical Evolution for Code Generation
GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2024, Pages 1830–1837https://doi.org/10.1145/3638530.3664162Code 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 (...