Skip to main content
Springer Nature Link
Log in

Enhancing POI Testing Through the Use of Additional Information

  • Conference paper
  • First Online:
Functional and Constraint Logic Programming (WFLP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11285))

Included in the following conference series:

  • 378 Accesses

Abstract

Recently, a new approach to perform regression testing has been defined: the point of interest (POI) testing. A POI, in this context, is any expression of a program. The approach receives as input a set of relations between POIs from a version of a program and POIs from another version, and also a sequence of entry points, i.e. test cases. Then, a program instrumentation, an input test case generation and different comparison functions are used to obtain the final report which indicates whether the alternative version of the program behaves as expected, e.g. it produces the same outputs or it uses less CPU/memory. In this paper, we present a method to improve POI testing by including additional context information for a certain type of POIs. Concretely, we use this method to obtain an enhanced tracing of calls. Additionally, it enables new comparison modes and a categorization of unexpected behaviours.

This work has been partially supported by MINECO/AEI/FEDER (EU) under grant TIN2016-76843-C4-1-R, and by the Generalitat Valenciana under grant PROMETEO-II/2015/013 (SmartLogic). Salvador Tamarit was partially supported by the Conselleria de Educación, Investigación, Cultura y Deporte de la Generalitat Valenciana under grant APOSTD/2016/036.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Although current implementation of POI testing does not still support auto-generation of observation points, it is a key feature that will be part of next release.

  2. 2.

    The occurrence argument (2 in the example) can be omitted, selecting in that case the first occurrence of the variable in the indicated line.

  3. 3.

    More information about Erlang and how we implemented a POI tester for this language are further discussed in Sect. 4.

  4. 4.

    Steps (ii) and (iii) could also be executed in parallel. Here, for the sake of simplicity, we only consider the sequential execution of these steps.

  5. 5.

    Note that a trace consists of a sequence of values since a variable can be evaluated several times. Each evaluation is represented by an element of the sequence.

  6. 6.

    The observed UnBs are represented and identified using literals, e.g. the atom greater could be used to represent an UnB that occurs when an expression is evaluated to a greater value in the new version than in the old one. The UnB representations are defined during the comparison process as it is then when the UnBs are found.

  7. 7.

    All functions presented in Sect. 3 are written in pseudocode. In a particular implementation, all functions should be implemented in the target language.

  8. 8.

    We use the notation ai(key) to refer to access some specific information previously stored in the ai mapping. In this case ai(args) represent the arguments of a POI placed in a function call.

  9. 9.

    The function presented in Fig. 5, does not make use of parameters TEO, TEN and History to define the message content. However, more complex functions that treat the information stored in these parameters can be defined to obtain a more elaborated message, like the one shown in Fig. 7.

  10. 10.

    Function get_ai is defined as .

  11. 11.

    All free variables used in the rule are represented as \(\mathtt {fv_{*}}\). Each one of these free variables is unique and different to all the original variables of the module.

  12. 12.

    In our implementation, the Erlang module secer_api provides a list of implemented functions to easily select any execution mode.

References

  1. Erlang (1986). http://www.erlang.org/

  2. Anand, S., et al.: An orchestrated survey of methodologies for automated software test case generation. J. Syst. Softw. 86(8), 1978–2001 (2013)

    Article  Google Scholar 

  3. Caballero, R., Martin-Martin, E., Riesco, A., Tamarit, S.: EDD: a declarative debugger for sequential erlang programs. In: Ábrahám, E., Havelund, K. (eds.) TACAS 2014. LNCS, vol. 8413, pp. 581–586. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-54862-8_49

    Chapter  Google Scholar 

  4. M. Cronqvist. redbug (2017). https://github.com/massemanet/redbug

  5. Danglot, B., Vera-Perez, O., Yu, Z., Monperrus, M., Baudry, B.: The emerging field of test amplification: a survey. CoRR, abs/1705.10692 (2017)

    Google Scholar 

  6. Engström, E., Runeson, P.: A qualitative survey of regression testing practices. In: Ali Babar, M., Vierimaa, M., Oivo, M. (eds.) PROFES 2010. LNCS, vol. 6156, pp. 3–16. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13792-1_3

    Chapter  Google Scholar 

  7. Ericsson AB. dbg (2017). http://erlang.org/doc/man/dbg.html

  8. Ericsson AB. Trace tool builder (2017). http://erlang.org/doc/apps/observer/ttb_ug.html

  9. Insa, D., Pérez, S., Silva, J., Tamarit, S.: Behaviour preservation across code versions in Erlang. Sci. Program. 2018, 1–42 (2018)

    Google Scholar 

  10. Jumpertz, E.: Using QuickCheck and semantic analysis to verify correctness of Erlang refactoring transformations. Master’s thesis, Radboud University Nijmegen (2010)

    Google Scholar 

  11. Reps, T., Ball, T., Das, M., Larus, J.: The use of program profiling for software maintenance with applications to the year 2000 problem. In: Jazayeri, M., Schauer, H. (eds.) ESEC/SIGSOFT FSE -1997. LNCS, vol. 1301, pp. 432–449. Springer, Heidelberg (1997). https://doi.org/10.1007/3-540-63531-9_29

    Chapter  Google Scholar 

  12. Shapiro, E.Y.: Algorithmic Program Debugging. MIT Press, Cambridge (1982)

    MATH  Google Scholar 

  13. Taylor, R., Hall, M., Bogdanov, K., Derrick, J.: Using behaviour inference to optimise regression test sets. In: Nielsen, B., Weise, C. (eds.) ICTSS 2012. LNCS, vol. 7641, pp. 184–199. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34691-0_14

    Chapter  Google Scholar 

  14. Till, A.: erlyberly (2017). https://github.com/andytill/erlyberly

  15. Tóth, I.B.M., Horváth, Z.: Reduction of regression tests for Erlang based on impact analysis (2013)

    Google Scholar 

  16. Xie, T., Notkin, D.: Checking inside the black box: regression testing by comparing value spectra. IEEE Trans. Softw. Eng. 31(10), 869–883 (2005)

    Article  Google Scholar 

  17. Yoo, S., Harman, M.: Regression testing minimization, selection and prioritization: a survey. Softw. Test. Verif. Reliab. 22(2), 67–120 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Departament de Sistemes Informàtics i Computació, Universitat Politècnica de València, Camí de Vera s/n, 46022, València, Spain

    Sergio Pérez & Salvador Tamarit

Authors
  1. Sergio Pérez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Salvador Tamarit
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sergio Pérez .

Editor information

Editors and Affiliations

  1. Universitat Politècnica de València, Valencia, Spain

    Josep Silva

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Pérez, S., Tamarit, S. (2019). Enhancing POI Testing Through the Use of Additional Information. In: Silva, J. (eds) Functional and Constraint Logic Programming. WFLP 2018. Lecture Notes in Computer Science(), vol 11285. Springer, Cham. https://doi.org/10.1007/978-3-030-16202-3_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-16202-3_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-16201-6

  • Online ISBN: 978-3-030-16202-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics