Skip to main content
SpringerLink
Log in

Perspectives on search strategies in automated test input generation

  • Letter
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Korel B. Automated software test data generation. IEEE Transactions on Software Engineering, 1990, 16(8): 870–879

    Article  Google Scholar 

  2. Miller B P, Koski D, Lee C P, Maganty V, Murthy R, Natarajan A, Steidl J. Fuzz revisited: a re-examination of the reliability of UNIX utilities and services. Technical Report CS-TR-1995-1268, University of Wisconsin, 1995

  3. Cadar C, Godefroid P, Khurshid S, Pasareanu C S, Sen K, Tillmann N, Visser W. Symbolic execution for software testing in practice: preliminary assessment. In: Proceedings of the 33rd International Conference on Software Engineering. 2011, 1066–1071

  4. Cadar C, Sen K. Symbolic execution for software testing: three decades later. Communications of the ACM, 2013, 56(2): 82–90

    Article  Google Scholar 

  5. Sharma C, Sabharwal S, Sibal R. A survey on software testing techniques using genetic algorithm. International Journal of Computer Science Issues, 2013, 10(1): 381

    Google Scholar 

  6. Li Y, Chen B, Chandramohan M, Lin S W, Liu Y, Tiu A. Steelix: program-state based binary fuzzing. In: Proceedings of the 11th Joint Meeting on Foundations of Software Engineering. 2017, 627–637

  7. Peng H, Shoshitaishvili Y, Payer M. T-Fuzz: fuzzing by program transformation. In: Proceedings of 2018 IEEE Symposium on Security and Privacy. 2018, 697–710

Download references

Acknowledgements

We thank the anonymous reviewers for their constructive comments. This work was supported in part by National Key R&D Program (#2017YFB1001801) and the National Natural Science Foundation of China (Grant Nos. #61690204 and #61802165). The authors would also like to thank the support of the Collaborative Innovation Center of Novel Software Technology and Industrialization, Jiangsu, China.

Author information

Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China

    Yang Cao, Yanyan Jiang, Chang Xu, Jun Ma & Xiaoxing Ma

  2. Department of Computer Science and Technology, Nanjing University, Nanjing, 210023, China

    Yang Cao, Yanyan Jiang, Chang Xu, Jun Ma & Xiaoxing Ma

Authors
  1. Yang Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanyan Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jun Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaoxing Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yanyan Jiang or Chang Xu.

Additional information

Supporting information

The supporting information is available online at journal.hep.cn and link.springer.com.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cao, Y., Jiang, Y., Xu, C. et al. Perspectives on search strategies in automated test input generation. Front. Comput. Sci. 14, 143202 (2020). https://doi.org/10.1007/s11704-019-8281-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-019-8281-3

Search

Navigation