Int J Performability Eng ›› 2017, Vol. 13 ›› Issue (7): 1111-1122.doi: 10.23940/ijpe.17.07.p14.11111122

• Original articles • Previous Articles     Next Articles

Automatic Generation of the AADL ALISA Verification Plan with ATL

Tianyi Wua, Zhiqiu Huanga, *, Zhibin Yanga, Tiexin Wanga, and Lei Xueb   

  1. aNanjing University of Aeronautics and Astronautics, 29 Jiangjun Road, Nanjing 211106, China
    bShanghai Aerospace Electronic Technology Institute, Shanghai 201109, China

Abstract: Architecture Led Incremental System Assurance short for ALISA presents a method to check if a system implementation meets its requirements. This method helps find errors in the early phase of system integration. ALISA provides four notations—requirements specifications, architecture models, verification techniques and assurance cases. The verification plan, which is designed by extracting information from requirement specification and architecture model, can be executed on the system and the result is significant metrics to judge the system quality. There are problems when generating a verification plan. As for the hierarchical architecture model with increasing complexity, the system may be divided into several parts and it is difficult to accomplish the assurance manually for each tier of the architecture. The approach also needs to respond to the ever-changing demands rapidly. New faults may be introduced artificially when designing requirement specifications and verification plans. In the paper, we propose an approach which uses ATL, whose full name is ATLAS transformation language, to help automatically generate verification plans. The meta-model of the verification plan and of requirement specification are given. Thus, designing the transformation rules from the verification part to the requirement part is easy. A lightweight template described in ATL is used to generate the verification plan for critical requirement and quality property.


Submitted on July 25, 2017; Revised on August 30, 2017; Accepted on September 15, 2017
References: 22