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- research-articleAugust 2024
Evaluating and optimising compiler code generation for NVIDIA Grace
ICPP '24: Proceedings of the 53rd International Conference on Parallel ProcessingAugust 2024, Pages 691–700https://doi.org/10.1145/3673038.3673104In this paper, we explore the performance of the main optimising compiler toolchains currently available for high-performance AArch64 processors, namely the Arm Compiler for Linux (ACFL), GNU, LLVM and the NVIDIA HPC (NVHPC) compilers, on the recently ...
- research-articleAugust 2024
Cross-Platform Transpilation of Packet-Processing Programs using Program Synthesis
APNet '24: Proceedings of the 8th Asia-Pacific Workshop on NetworkingAugust 2024, Pages 74–80https://doi.org/10.1145/3663408.3663419The proliferation of programmable network devices offers a wide range of device options for developers of packet processing programs. However, there are several differences in programming language usage, hardware resource constraints, and hardware ...
- 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 (...
- posterAugust 2024
Evolving Assembly Code in an Adversarial Environment
GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2024, Pages 723–726https://doi.org/10.1145/3638530.3654209We evolve survivors for the CodeGuru competition --- assembly programs that run the longest in shared memory, by resisting attacks from adversary survivors and finding their weaknesses. For evolving top-notch solvers, we specify a Backus Normal Form (BNF)...
- research-articleJuly 2024
Go Static: Contextualized Logging Statement Generation
- Yichen Li,
- Yintong Huo,
- Renyi Zhong,
- Zhihan Jiang,
- Jinyang Liu,
- Junjie Huang,
- Jiazhen Gu,
- Pinjia He,
- Michael R. Lyu
Proceedings of the ACM on Software Engineering (PACMSE), Volume 1, Issue FSEArticle No.: 28, Pages 609–630https://doi.org/10.1145/3643754Logging practices have been extensively investigated to assist developers in writing appropriate logging statements for documenting software behaviors. Although numerous automatic logging approaches have been proposed, their performance remains ...
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- research-articleJuly 2024
CIQA: A Coding Inspired Question Answering Model
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2024, Pages 1973–1983https://doi.org/10.1145/3626772.3657830Methods in question-answering (QA) that transform texts detailing processes into an intermediate code representation, subsequently executed to generate a response to the presented question, have demonstrated promising results in analyzing scientific ...
- abstractJuly 2024
Machine Generated Explanations and Their Evaluation
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2024, Page 3074https://doi.org/10.1145/3626772.3657652Rapid adoption of a new generation of LLMs has demonstrated their considerable capabilities. However, these models are far from infallible, raising significant ethical concerns, especially in decision-making applications, prompting calls for increased ...
- research-articleJuly 2024
The Art of Programming: Challenges in Generating Code for Creative Applications
AIware 2024: Proceedings of the 1st ACM International Conference on AI-Powered SoftwareJuly 2024, Pages 139–143https://doi.org/10.1145/3664646.3664774Programming has been a key tool for artists and other creatives for decades, and the creative use of programming presents unique challenges, opportunities and perspectives for researchers considering how AI can be used to support coding more generally. ...
- research-articleJune 2024
NetConfEval: Can LLMs Facilitate Network Configuration?
Proceedings of the ACM on Networking (PACMNET), Volume 2, Issue CoNEXT2Article No.: 7, Pages 1–25https://doi.org/10.1145/3656296This paper explores opportunities to utilize Large Language Models (LLMs) to make network configuration human-friendly, simplifying the configuration of network devices & development of routing algorithms and minimizing errors. We design a set of ...
- research-articleMay 2024
Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study
Proceedings of the ACM on Management of Data (PACMMOD), Volume 2, Issue 3Article No.: 115, Pages 1–28https://doi.org/10.1145/3654992The Natural Language to Visualization (NL2Vis) task aims to transform natural-language descriptions into visual representations for a grounded table, enabling users to gain insights from vast amounts of data. Recently, many deep learning-based approaches ...
- ArticleJune 2024
MetaOCaml: Ten Years Later: System Description
AbstractMetaOCaml is a superset of OCaml for convenient code generation with static guarantees: the generated code is well-formed, well-typed and well-scoped, by construction. Not only the completed generated code always compiles; code fragments with a ...
- research-articleMay 2024
R-Blocks: an Energy-Efficient, Flexible, and Programmable CGRA
ACM Transactions on Reconfigurable Technology and Systems (TRETS), Volume 17, Issue 2Article No.: 34, Pages 1–34https://doi.org/10.1145/3656642Emerging data-driven applications in the embedded, e-Health, and internet of things (IoT) domain require complex on-device signal analysis and data reduction to maximize energy efficiency on these energy-constrained devices. Coarse-grained reconfigurable ...
- Work in ProgressMay 2024
To Search or To Gen? Exploring the Synergy between Generative AI and Web Search in Programming
CHI EA '24: Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing SystemsMay 2024, Article No.: 327, Pages 1–8https://doi.org/10.1145/3613905.3650867The convergence of generative AI and web search is reshaping problem-solving for programmers. However, the lack of understanding regarding their interplay in the information-seeking process often leads programmers to perceive them as alternatives rather ...
- research-articleAugust 2024
An Empirical Comparison of Code Generation Approaches for Ansible
InteNSE '24: Proceedings of the ACM/IEEE 2nd International Workshop on Interpretability, Robustness, and Benchmarking in Neural Software EngineeringApril 2024, Pages 1–6https://doi.org/10.1145/3643661.3643951The rapid proliferation of LLM-based programming assistants has enabled fast and accurate automatic code generation for general purpose programming languages. Domain-specific languages like Ansible, a DSL for IT Automation, have seen a lack of support ...
- short-paperJuly 2024
Systematic Testing of a ROS Interface Specification Backend
RoSE '24: Proceedings of the 2024 ACM/IEEE 6th International Workshop on Robotics Software EngineeringApril 2024, Pages 25–30https://doi.org/10.1145/3643663.3643964Code generators are frequently used when language-independent specifications are compiled into client libraries to support multiple languages. One example is the message definition specification of the Robot Operating System (ROS). This work discusses ...
- research-articleJuly 2024
Write me this Code: An Analysis of ChatGPT Quality for Producing Source Code
MSR '24: Proceedings of the 21st International Conference on Mining Software RepositoriesApril 2024, Pages 147–151https://doi.org/10.1145/3643991.3645070Developers nowadays are increasingly turning to large language models (LLMs) like ChatGPT to assist them with coding tasks, inspired by the promise of efficiency and the advanced capabilities they offer. However, this raises important questions about the ...
- research-articleJune 2024
Knowledge-Aware Code Generation with Large Language Models
ICPC '24: Proceedings of the 32nd IEEE/ACM International Conference on Program ComprehensionApril 2024, Pages 52–63https://doi.org/10.1145/3643916.3644418Large Language Models (LLMs) perform well on basic programming problems. However, they encounter challenges when dealing with complex tasks involving the use of diverse algorithmic and data structure skills, particularly programming competition-level ...
- research-articleJune 2024
Analyzing Prompt Influence on Automated Method Generation: An Empirical Study with Copilot
ICPC '24: Proceedings of the 32nd IEEE/ACM International Conference on Program ComprehensionApril 2024, Pages 24–34https://doi.org/10.1145/3643916.3644409Generative AI is changing the way developers interact with software systems, providing services that can produce and deliver new content, crafted to satisfy the actual needs of developers. For instance, developers can ask for new code directly from ...
- research-articleJune 2024
Compositional API Recommendation for Library-Oriented Code Generation
ICPC '24: Proceedings of the 32nd IEEE/ACM International Conference on Program ComprehensionApril 2024, Pages 87–98https://doi.org/10.1145/3643916.3644403Large language models (LLMs) have achieved exceptional performance in code generation. However, the performance remains unsatisfactory in generating library-oriented code, especially for the libraries not present in the training data of LLMs. Previous ...