Skip to main content

Mutation Operators for Google Query Language

  • Conference paper
  • First Online:
Intelligent Information and Database Systems (ACIIDS 2020)

Abstract

Nowadays the technology is being created and adapted to satisfy the user necessities. Among them, obtaining information as fast as possible. Google knows how to meet this demand developing and offering new services that provide the requested information quickly. Google technology can be used to develop products using the Google App Engine (GAE). In order to manipulate the data, GAE uses the Google Query Language (GQL), a SQL-like language, that has been designed to provide a solution to the necessity of having super-fast access to data warehouses. The quality of the developed products is essential and therefore, testing them is mandatory. In this paper, we propose the use of mutation testing to detect faults during the development of applications that use GQL. With this goal, we introduce a set of specific mutation operators for GQL.

Paper partially funded by the Spanish MINECO-FEDER (grant number DArDOS, TIN2015-65845-C3-1-R, grant number FAME RTI2018-093608-B-C31 and RTI2018-093608-B-C33); the Region of Madrid (grant number FORTE-CM, S2018/TCS-4314).

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://github.com/lorgut/ACIIDS2020/tree/master/Classes.

  2. 2.

    https://github.com/lorgut/ACIIDS2020/tree/master/Entities.

  3. 3.

    https://github.com/lorgut/ACIIDS2020/tree/master/GQLapp.

  4. 4.

    https://github.com/lorgut/ACIIDS2020/tree/master/Outputs.

References

  1. Barry, P.: Doing IT the app engine way. Linux J. 2010(197), 1 (2010). ISSN 1075–3583

    Google Scholar 

  2. DeMillo, R.A., Lipton, R.J., Sayward, F.G.: Hints on test data selection: help for the practicing programmer. Computer 11(4), 34–41 (1978). ISSN 0018–9162

    Article  Google Scholar 

  3. Gómez-Abajo, P., et al.: A tool for domain-independent model mutation. Sci. Comput. Program. 163, 85–92 (2018)

    Article  Google Scholar 

  4. Google: Google App Engine.. https://cloud.google.com/appengine/docs/. Accessed Oct 2019

  5. Google. Google App Engine: Structuring Data for Strong consistency. https://cloud.google.com/appengine/docs/standard/java/datastore/structuring_for_strong_consistency. Accessed Oct 2019

  6. Google. Google Cloud Patform. https://cloud.google.com/docs/. Accessed Oct 2019

  7. Google. Google Query Language in Google App Engine. https://cloud.google.com/appengine/docs/standard/python/datastore/gqlreference. Accessed Oct 2019

  8. Google. Pick strong consistency. https://cloud.google.com/blog/products/gcp/why-you-should-pick-strong-consistency-whenever-possible. Accessed Oct 2019

  9. Gutiérrez-Madroñal, L., García-Domínguez, A., Medina-Bulo, I.: Evolutionary mutation testing for IoT with recorded and generated events. Softw. Pract. Exper. 49(4), 640–672 (2019)

    Article  Google Scholar 

  10. Gutiérrez-Madroñal, L., Medina-Bulo, I., Domínguez-Jiménez, J.J.: Evaluation of EPL mutation operators with the MuEPL mutation system. Expert Syst. Appl. 116, 78–95 (2019). ISSN 0957–4174

    Article  Google Scholar 

  11. Gutiérrez-Madroñal, L., et al.: Mutation testing of event processing queries. In: 2012 IEEE 23rd International Symposium on Software Reliability Engineering, pp. 21–30 (2012)

    Google Scholar 

  12. Hamlet, R.G.: Testing programs with the aid of a compiler. IEEE Trans. Software Eng. SE 3(4), 279–290 (1977). ISSN 2326–3881

    Article  MathSciNet  Google Scholar 

  13. Hees, J., Bauer, R., Folz, J., Borth, D., Dengel, A.: An evolutionary algorithm to learn SPARQL queries for source-target-pairs. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds.) EKAW 2016. LNCS (LNAI), vol. 10024, pp. 337–352. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49004-5_22

    Chapter  Google Scholar 

  14. Hierons, R.M., Merayo, M.G., Núñez, M.: An extended framework for passive asynchronous testing. J. Log. Algebraic Methods Program. 86(1), 408–424 (2017)

    Article  MathSciNet  Google Scholar 

  15. Jia, Y., Harman, M.: An analysis and survey of the development of mutation testing. IEEE Trans. Software Eng. 37(5), 649–678 (2011). ISSN 2326–3881

    Article  Google Scholar 

  16. Merayo, M.G., Hierons, R.M., Núñez, M.: A tool supported methodology to passively test asynchronous systems with multiple users. Inf. Softw. Technol. 104, 162–178 (2018)

    Article  Google Scholar 

  17. Merayo, M.G., Hierons, R.M., Núñez, M.: Passive testing with asynchronous communications and timestamps. Distrib. Comput. 31(5), 327–342 (2018). ISSN 1432–0452

    Article  MathSciNet  Google Scholar 

  18. Tuya, J., Suárez-Cabal, M.J., la Riva, C.: Mutating database queries. Inf. Softw. Technol. 49(4), 398–417 (2007). ISSN 0950–5849

    Article  Google Scholar 

  19. Vázquez-Ingelmo, A., Cruz-Benito, J., García-Peñalvo, F.J.: Improving the OEEU’s data-driven technological ecosystem’s interoperability with GraphQL. In: Proceedings of the 5th International Conference on Technological Ecosystems for Enhancing Multiculturality, pp. 89:1–89:8. ACM, Cádiz (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lorena Gutiérrez-Madroñal .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gutiérrez-Madroñal, L., Medina-Bulo, I., Merayo, M.G. (2020). Mutation Operators for Google Query Language. In: Sitek, P., Pietranik, M., Krótkiewicz, M., Srinilta, C. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Communications in Computer and Information Science, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-15-3380-8_31

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-3380-8_31

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3379-2

  • Online ISBN: 978-981-15-3380-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics