Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (7): 29-39.doi: 10.11896/jsjkx.230600126

• Computer Software • Previous Articles     Next Articles

Natural Language Requirements Based Approach for Automatic Test Cases Generation of SCADE Models

SHAO Wenxin1,2, YANG Zhibin1,2, LI Wei3, ZHOU Yong1,2   

  1. 1 School of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
    2 Key Laboratory of Safety-critical Software,Ministry of Industry and Information Technology,Nanjing 211106,China
    3 Aviation Key Laboratory of Science and Technology on Life-support Technology,Xiangyang,Hubei 441003,China
  • Received:2023-06-15 Revised:2023-10-18 Online:2024-07-15 Published:2024-07-10
  • About author:SHAO Wenxin,born in 1998,postgra-duate.Her main research interests include safety critical-system and formal verification.
    YANG Zhibin,born in 1982,Ph.D,professor,is a member of CCF(No.08632M).His main research interests include safety-critical system,formal verification and AI software enginee-ring.
  • Supported by:
    National Natural Science Foundation of China(62072233),National Defense Basic Scientific Research Project(JCKY2020205C006),Aeronautical Science Foundation of China(201919052002) and Postgraduate Research & Practice Innovation Program of NUAA(xcxjh20221607).

PDF (PC)

546

Abstract: With the increasing scale and complexity of safety-critical software,model-driven development(MDD) is widely used in safety-critical fields.As an important modeling method and tool,SCADE can express deterministic concurrent behavior and has precise time semantics,which is suitable for modeling,testing and verification of safety-critical software.At present,the existing methods mainly use manual methods to construct SCADE model test cases,and there are some problems such as inconsistency between requirements and test cases,high cost and easy to make mistakes.This paper presents an automatic generation method of SCADE model test cases based on natural language requirements.Firstly,an automatic test case generation method based on mo-del checking is presented,which generates atomic propositions by natural language requirements processing to generate the assume and observer models,and provides the rules of trap properties generation to generate trap properties for model checking.Secondly,a test case quality evaluation method based on coverage analysis and mutation testing is presented,and the mutation testing is carried out on SCADE model.Finally,the prototype tool is designed and implemented,and an industrial case of pilot ejection seat control system is analyzed to verify the effectiveness of the proposed method.

Key words: Safety-critical software, Model-driven development, SCADE, Automatic test case generation, Model checking

CLC Number: 

  • TP311
[1]YANG Z B,YUAN S H,XIE J,et al.A synchronous language multi-threaded code automatic generation tool[J].Journal of Software,2019,30(7):23.
[2]SINGH P,SINGH L K.Reliability and Safety Engineering for Safety Critical Systems:An Interview Study With Industry Practitioners[J].IEEE Transactions on Reliability,2021,70(2):643-653.
[3]RIERSON L.Developing safety-critical software:a practicalguide for aviation software and DO-178C compliance[M].CRC Press,2017.
[4]DO-331,Model-based development and verification supplement to DO-178C and DO-278A[S].RTCA:Washington,DC,USA,2011.
[5]DO-333,Formal methods supplement to DO-178C and DO-278A[S].RTCA:Washington,DC,USA,2011.
[6]WAKANKAR A,BHATTACHARJEE A K,DHODAPKAR S D,et al.Automatic test case generation in model based software design to achieve higher reliability[C]//2010 2nd International Conference on Reliability,Safety and Hazard - Risk-Based Technologies and Physics-of-Failure Methods(ICRESH).2010:493-499.
[7]SCADE[OL].https://www.ansys.com/products/em-beddedsoft-ware/ansys-scade-suite.
[8]JING H,SHEN Y Y.ANSYS SCADE Suite Modeling Foundation [M].Beijing:China Water Resources and Hydropower Press,2018.
[9]ELKHOLY W,EL-MENSHAWY M,BENTAHAR J,et al.Model checking intelligent avionics systems for test cases gene-ration using multi-agent systems[J].Expert Systems with Applications,2020,156:113458.
[10]KADRY S.A New Proposed Technique to Improve Software Regression Testing Cost[J].International Journal of Security & Its Applications,2011,5(3):21-39.
[11]BRILLOUT A,HE N,MAZZUCCHI M,et al.Mutation-Based Test Case Generation for Simulink Models[C]//Formal Me-thods for Components and Objects.Berlin,Heidelberg:Sprin-ger,2010:208-227.
[12]WANG W X,HU J,HU J C,et al.Test Case Generation MethodOriented to Tabular Form Formal Requirement Model[J].Computer Science,2021,48(5):16-24.
[13]DAI X,GONG W,GU Q.Automated test case generation based on differential evolution with node branch archive[J].Compu-ters & Industrial Engineering,2021,156:107290.
[14]TIAN Y,YIN B,LI C.A Model-based Test Cases Generation Method for Spacecraft Software[C]//2021 8th International Conference on Dependable Systems and Their Applications(DSA).2021:373-382.
[15]RAYADURGAM S,HEIMDAHL M P E.Coverage based test-case generation using model checkers[C]//Proceedings of the Eighth Annual IEEE International Conference and Workshop on the Engineering of Computer-Based Systems(ECBS 2001).2001:83-91.
[16]CHANDLER R E.Model Checking[M].MIT Press,2005.
[17]ANICULAESEI A,VORWALD A,RAUSCH A.Using theSCADE Toolchain to Generate Requirements-Based Test Cases for an Adaptive Cruise Control System[C]//2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion(MODELS-C).2019:503-513.
[18]CHILENDKI J J,MILLER S P.Applicability of modified condition/decision coverage to software testing[J].Software Engineering Journal,1994,9(5):193-200.
[19]DEMILLO R A,LIPTON R J,SAYWARD F G.Hints on Test Data Selection:Help for the Practicing Programmer[J].Computer,1978,11(4):34-41.
[20]YANG H,HONG M,QU Y Y.Approach of Mutation Test CaseGeneration Based on Model Checking[J].Computer Science,2018,45(11A):488-493.
[21]GAALOUL K,MENGHI C,NEJATI S,et al.Combining Gene-tic Programming and Model Checking to Generate Environment Assumptions[J].arXiv:2101.01933,2021.
[22]ZHANG Z L,YANG Z B,LI X J,et al.Machine Learning Based Environment Assumption Automatic Generation for Compositional Verification of SCADE Models[J].Computer Science,2023,50(6):297-306.
[23]YANG Z B,YANG Y Q,YUAN S H,et al.Terminology Recommendation and Requirement Classification Method for Safety-critical Software[J].Computer Science,2021,48(5):32-44.
[24]HE H,CHOI J D.The Stem Cell Hypothesis:Dilemma behind Multi-Task Learning with Transformer Encoders[J/OL].https://arxiv.org/abs/2109.06939.
[25]WHALEN M W,RAJAN A,HEIMDAHL M P E,et al.Cove-rage metrics for requirements-based testing[C]//Proceedings of the 2006 International Symposium on Software Testing and Dnalysis.New York,NY,USA:Association for Computing Machinery,2006:25-36.
[26]MA C,PROVOST J.Design-to-Test Approach for Programmable Controllers in Safety-Critical Automation Systems[J].IEEE Transactions on Industrial Informatics,2020,16(10):6499-6508.
[27]MOHD-SHAFIE M L,KADIR W M N W,LICHTER H,et al.Model-based test case generation and prioritization:a systematic literature review[J].Software and Systems Modeling,2022,21(2):717-753.
[28]LI Y P,LI H,ZHAO J L.Research about FSM Test Sequence Generation Algorithm[J].Computer Science,2016,43(Z11):474-481.
[29]HUANG S Q,LIU Y B,HUANG X S.Research on Automatic Testing Technology of Model Driven Development Tools[J].Computer Science,2021,48(6A):568-571.
[30]MINJ B.Generation of Test Cases based on Analysis of Simulink Stateflow Models[C]//IJCA Proceedings on National Conference on Knowledge,Innovation in Technology andEnginee-ring(NCKITE 2015).Foundation of Computer Science(FCS),2015.
[31]MOITRA A,SIU K,CRAPO A W,et al.Automating requirements analysis and test case generation[J].Requirements Engineering,2019,24(3):341-364.
[32]YANG B,WU J,XU L,et al.A Method for Modeling Software Testing Requirements and Generating Test Cases [J].Chinese Journal of Computers,2014,37(3):17.
[33]DURRIEU G,LAURENT O,SEGUIN C,et al.Formal Proof and Test Case Generation for Critical Embedded Systems Using Scade[C]//Building the Information Society.Boston,MA:Springer US,2004:499-504.
[34]ANICULAESEI A,HOWAR F,DENECKE P,et al.Automated generation of requirements-based test cases for an adaptive cruise control system[C]//2018 IEEE Workshop on Validation,Analysis and Evolution of Software Tests(VST).2018:11-15.
[35]WHALEN M W,RAJAN A,HEIMDAHL M P E,et al.Cove-age metrics for requirements-based testing[C]//Proceedings of the 2006 International Symposium on Software Testing and Analysis.New York,NY,USA:Association for Computing Machinery,2006:25-36.
[36]ANICULAESEI A,VORWALD A,RAUSCH A.Automated Generation of Requirements-Based Test Cases for an Automotive Function using the SCADE Toolchain[C]//The 11th International Conference on Adaptive and Self-Ada Ptive Systems and Application.2019.
[1] DENG Ziwei, CHEN Ling, LIU Wei. Continuous Influence Maximization Under Independent Cascade Propagation Model [J]. Computer Science, 2024, 51(6): 161-171.
[2] LI Yu, YANG Xiangli, ZHANG Le, LIANG Yalin, GAO Xian, YANG Jianxi. Combined Road Segmentation and Contour Extraction for Remote Sensing Images Based on Cascaded U-Net [J]. Computer Science, 2024, 51(3): 174-182.
[3] WANG Yuchen, GAO Chao, WANG Zhen. Survey of Inferring Information Diffusion Networks [J]. Computer Science, 2024, 51(1): 99-112.
[4] ZHENG Hong, QIAN Shihui, LIU Zerun, DU Wen. Formal Verification of Supply Chain Contract Based on Coloured Petri Nets [J]. Computer Science, 2023, 50(6A): 220300220-7.
[5] BAI Mingli, WANG Mingwen. Fabric Defect Detection Algorithm Based on Improved Cascade R-CNN [J]. Computer Science, 2023, 50(6A): 220300224-6.
[6] ZHANG Zelun, YANG Zhibin, LI Xiaojie, ZHOU Yong, LI Wei. Machine Learning Based Environment Assumption Automatic Generation for Compositional Verification of SCADE Models [J]. Computer Science, 2023, 50(6): 297-306.
[7] YANG Liu, FAN Hongyu, LI Dongfang, HE Fei. IC3 Hardware Verification Algorithm Based on Variable Hiding Abstraction [J]. Computer Science, 2023, 50(11A): 230200112-6.
[8] CHEN Bonian, HAN Yutong, HE Tao, LIU Bin, ZHANG Jianxin. Cascade Dynamic Attention U-Net Based Brain Tumor Segmentation [J]. Computer Science, 2023, 50(11A): 221100180-7.
[9] YUE Qing, YIN Jian-yu, WANG Sheng-sheng. Automatic Detection of Pulmonary Nodules in Low-dose CT Images Based on Improved CNN [J]. Computer Science, 2022, 49(6A): 54-59.
[10] SHAO Yan-hua, LI Wen-feng, ZHANG Xiao-qiang, CHU Hong-yu, RAO Yun-bo, CHEN Lu. Aerial Violence Recognition Based on Spatial-Temporal Graph Convolutional Networks and Attention Model [J]. Computer Science, 2022, 49(6): 254-261.
[11] SHAO Yu, CHEN Ling, LIU Wei. Maximum Likelihood-based Method for Locating Source of Negative Influence Spreading Under Independent Cascade Model [J]. Computer Science, 2022, 49(2): 204-215.
[12] ZHENG Jian-hua, LI Xiao-min, LIU Shuang-yin, LI Di. Improved Random Forest Imbalance Data Classification Algorithm Combining Cascaded Up-sampling and Down-sampling [J]. Computer Science, 2021, 48(7): 145-154.
[13] YANG Zhi-bin, YANG Yong-qiang, YUAN Sheng-hao, ZHOU Yong, XUE Lei, CHENG Gao-hui. Terminology Recommendation and Requirement Classification Method for Safety-critical Software [J]. Computer Science, 2021, 48(5): 32-44.
[14] TAN Qi, ZHANG Feng-li, ZHANG Zhi-yang, CHEN Xue-qin. Modeling Methods of Social Network User Influence [J]. Computer Science, 2021, 48(2): 76-86.
[15] RAN Dan, CHEN Zhe, SUN Yi, YANG Zhi-bin. SCADE Model Checking Based on Program Transformation [J]. Computer Science, 2021, 48(12): 125-130.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.