DeepOCL:A deep neural network for Object Constraint Language generation from unrestricted nature language  

作　　者：Yilong Yang Yibo Liu Tianshu Bao Weiru Wang Nan Niu Yongfeng Yin 

机构地区：[1]School of Software,Beihang University,Beijing,China [2]College of Computer Science and Technology,Guizhou University,Guiyang,Guizhou,China [3]Faculty of Information Technology,Beijing University of Technology,Beijing,China [4]Department of Electrical Engineering and Computer Sciences,University of Cincinnati,Cincinnati,Ohio,USA

出　　处：《CAAI Transactions on Intelligence Technology》2024年第1期250-263,共14页智能技术学报（英文）

基　　金：The National Key Research and Development Program of China,Grant/Award Number:2021YFB2501301。

摘　　要：Object Constraint Language(OCL)is one kind of lightweight formal specification,which is widely used for software verification and validation in NASA and Object Management Group projects.Although OCL provides a simple expressive syntax,it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first-order logic,which is approximately half accurate at the first stage of devel-opment.A deep neural network named DeepOCL is proposed,which takes the unre-stricted natural language as inputs and automatically outputs the best-scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule-based generation approaches.To demonstrate the validity of our proposed approach,ablation experiments were conducted on a new sentence-aligned dataset named OCLPairs.The experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation,scored 74.30 on BLEU,and greatly outperformed experienced developers by 35.19%.The proposed approach is the first deep learning approach to generate the OCL expression from the natural language.It can be further developed as a CASE tool for the software industry.

关 键 词：deep learning OCL software engineering 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]