Leveraging Large Language Models to Boost Dafny's Developers Productivity

被引:0
|
作者
Silva, Alvaro
Mendes, Alexandra [1 ,2 ]
Ferreira, Joao F. [3 ,4 ]
机构
[1] Univ Porto, INESC TEC, HASLab, Porto, Portugal
[2] Univ Porto, Fac Engn, Porto, Portugal
[3] Univ Lisbon, INESC ID, Lisbon, Portugal
[4] Univ Lisbon, IST, Lisbon, Portugal
关键词
Verification-Aware Languages; Dafny; Large Language Models; Generative AI; Software Productivity; Software Verification; Lemma Inference; Proof Inference; Automated Program Repair;
D O I
10.1145/3644033.3644374
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
This research idea paper proposes leveraging Large Language Models (LLMs) to enhance the productivity of Dafny developers. Although the use of verification-aware languages, such as Dafny, has increased considerably in the last decade, these are still not widely adopted. Often the cost of using such languages is too high, due to the level of expertise required from the developers and challenges that they often face when trying to prove a program correct. Even though Dafny automates a lot of the verification process, sometimes there are steps that are too complex for Dafny to perform on its own. One such case is that of missing lemmas, i.e. Dafny is unable to prove a result without being given further help in the form of a theorem that can assist it in the proof of the step. In this paper, we describe preliminary work on using LLMs to assist developers by generating suggestions for relevant lemmas that Dafny is unable to discover and use. Moreover, for the lemmas that cannot be proved automatically, we attempt to provide accompanying calculational proofs. We also discuss ideas for future work by describing a research agenda on using LLMs to increase the adoption of verification-aware languages in general, by increasing developers productivity and by reducing the level of expertise required for crafting formal specifications and proving program properties.
引用
收藏
页码:138 / 142
页数:5
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