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

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.

Effects of water saturation and loading rate on direct shear tests of andesite

For estimating the long-term stability of underground framework,it is vital to learn the mechanical and rheological characteristics of rock in multiple water saturation conditions.However,the majority of previous studies explored the rheological properties of rock in air-dried and water saturated conditions,as well as the water effects on compressive and tensile strengths.In this study,andesite was subjected to direct shear tests under five water saturation conditions,which were controlled by varying the wetting and drying time.The tests were conducted at alternating displacement rates under three vertical stresses.The results reveal that the shear strength decreases exponentially as water saturation increases,and that the increase in shear strength with a tenfold increase in displacement rate is nearly constant for each of the vertical stresses.Based on the findings of the shear tests in this study and the compression and tension tests in previous studies,the influences of both water saturation and loading rate on the Hoek-Brown failure criterion for the andesite was examined.These results indicate that the brittleness index of the andesite,which is defined as the ratio of uniaxial compressive strength to tensile strength,is independent of both water saturation and loading rate and that the influences of the water saturation dependence and the loading rate dependence of the failure criterion can be converted between each other.