Generative transformations and patterns in LLM-native approaches for software verification and falsification
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914078097670144 |
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| author | Braberman, Víctor A. Bonomo-Braberman, Flavia Charalambous, Yiannis Colonna, Juan G. Cordeiro, Lucas C. de Freitas, Rosiane |
| author_facet | Braberman, Víctor A. Bonomo-Braberman, Flavia Charalambous, Yiannis Colonna, Juan G. Cordeiro, Lucas C. de Freitas, Rosiane |
| contents | The emergence of prompting as the dominant paradigm for leveraging Large Language Models (LLMs) has led to a proliferation of LLM-native software, where application behavior arises from complex, stochastic data transformations. However, the engineering of such systems remains largely exploratory and ad-hoc, hampered by the absence of conceptual frameworks, ex-ante methodologies, design guidelines, and specialized benchmarks. We argue that a foundational step towards a more disciplined engineering practice is a systematic understanding of the core functional units--generative transformations--and their compositional patterns within LLM-native applications.
Focusing on the rich domain of software verification and falsification, we conduct a secondary study of over 100 research proposals to address this gap. We first present a fine-grained taxonomy of generative transformations, abstracting prompt-based interactions into conceptual signatures. This taxonomy serves as a scaffolding to identify recurrent transformation relationship patterns--analogous to software design patterns--that characterize solution approaches in the literature. Our analysis not only validates the utility of the taxonomy but also surfaces strategic gaps and cross-dimensional relationships, offering a structured foundation for future research in modular and compositional LLM application design, benchmarking, and the development of reliable LLM-native systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09384 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative transformations and patterns in LLM-native approaches for software verification and falsification Braberman, Víctor A. Bonomo-Braberman, Flavia Charalambous, Yiannis Colonna, Juan G. Cordeiro, Lucas C. de Freitas, Rosiane Software Engineering Artificial Intelligence Computation and Language Machine Learning F.3.1; D.2.4; D.2.5; I.2.7 The emergence of prompting as the dominant paradigm for leveraging Large Language Models (LLMs) has led to a proliferation of LLM-native software, where application behavior arises from complex, stochastic data transformations. However, the engineering of such systems remains largely exploratory and ad-hoc, hampered by the absence of conceptual frameworks, ex-ante methodologies, design guidelines, and specialized benchmarks. We argue that a foundational step towards a more disciplined engineering practice is a systematic understanding of the core functional units--generative transformations--and their compositional patterns within LLM-native applications. Focusing on the rich domain of software verification and falsification, we conduct a secondary study of over 100 research proposals to address this gap. We first present a fine-grained taxonomy of generative transformations, abstracting prompt-based interactions into conceptual signatures. This taxonomy serves as a scaffolding to identify recurrent transformation relationship patterns--analogous to software design patterns--that characterize solution approaches in the literature. Our analysis not only validates the utility of the taxonomy but also surfaces strategic gaps and cross-dimensional relationships, offering a structured foundation for future research in modular and compositional LLM application design, benchmarking, and the development of reliable LLM-native systems. |
| title | Generative transformations and patterns in LLM-native approaches for software verification and falsification |
| topic | Software Engineering Artificial Intelligence Computation and Language Machine Learning F.3.1; D.2.4; D.2.5; I.2.7 |
| url | https://arxiv.org/abs/2404.09384 |