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A Learning Classifier System for Automated Test Case Prioritization and Selection

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A Correction to this article was published on 01 September 2022

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Abstract

For many everyday devices, each newly released model contains more functionality. This technological advance relies heavily on software solutions of increasing complexity which results in novel challenges in the domain of software testing. Most prominently, while an ever higher number of test cases is required to meet quality demands, performing a large number of test cases frequently amounts to a significant increase in development time and costs. In order to overcome this issue, agile development methods such as continuous integration usually only execute a subset of important test cases to meet both time and testing demands. One way of selecting such a subset of important test cases is to assign priorities to all the available test cases and then greedily pick the ones with the highest priority until the available time budget is spent. For this, in a previous work, we presented a new machine learning approach based on a learning classifier system (LCS). In the present article, we summarize our earlier findings (which are spread over several publications) and provide insights about the most recent adaptations we made to the method. We also provide an extended experimental analysis that outlines more in detail how it compares to a state of the art artificial neural network. It can be observed that the performance of our LCS-based approach is often much higher than the one of the network. Since our work has already been deployed by a major company, we give an overview of the resulting product as well as several of its in-production quality attributes.

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(adapted from [29])

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Availability of Data and Material

The used data sets may be found here: https://bitbucket.org/HelgeS/atcs-data/src/master/.

Change history

  • 29 August 2022

    Figures were not placed nearer to its citation. Now, the placment of the figures have been corrected.

  • 01 September 2022

    A Correction to this paper has been published: https://doi.org/10.1007/s42979-022-01352-1

Notes

  1. Note that, despite its name, NAPFD actually measures test case failures and not faults. Faults are system errors caused by bugs etc. and each fault may result in multiple test cases failing.

  2. In those publications we report on the AIS approach performing equal to or better than the NSGA-II of Lachmann et al. [14]. The NSGA-II used by Arrieta et al. [2] differs from the one used by Lachmann et al. only in the employed crossover operator.

  3. Spieker et al. [32] originally formulated the use case as a reinforcement learning one. We intend to provide a more high-level machine learning view.

  4. Of course they are adapted to numbers. Mutating a number translates to drawing a new random number. Crossover consists of first performing an arithmetic crossover (for two numbers x, y, this corresponds to \(\zeta x + (1 - \zeta ) y\) and \(\zeta y + (1 - \zeta ) x\)) and then the same two-point crossover as used for the ternary subconditions.

  5. Note that the normalization by \(\gamma\) and \(\Gamma\) is necessary to ensure that the result is indeed a probability distribution.

  6. Our code is available here: https://github.com/LagLukas/transfer_learning.

  7. The data sets can be downloaded here: https://bitbucket.org/HelgeS/atcs-data/src/master/.

  8. Spieker et al.’s implementation of their NN-based approach can be found here https://bitbucket.org/HelgeS/retecs.

  9. We take the average over three succeeding values. We consider disjoint CI cycle sets with indexes \(\{3k, 3k+1, 3k+2\}\).

  10. We used one-sided Wilcoxon tests to compare each combination of one of the three ER methods with one of the failure count or time ranked value functions with each combination of the three ER methods with the test case failure value function (a total of \((3 \times 2) \times (3 \times 1) = 18\) comparisons) and for each checked the null hypothesis of whether the first performs worse than the second. Since all the p-values are less than \(10^{-21}\), we conclude that the failure count and time-ranked value functions yield significantly better results.

  11. For the failure count value we can observe similar results; the corresponding plots can be found in Appendix A.

  12. We examined null hypotheses of the form: Our transfer learning approach leads to worse results than the raw XCSF-ER on data set x with value function y.

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Authors and Affiliations

  1. BSH Hausgeräte GmbH, Regensburg, Germany

    Lukas Rosenbauer

  2. University of Augsburg, Augsburg, Germany

    David Pätzel & Jörg Hähner

  3. University of Hohenheim, Stuttgart, Germany

    Anthony Stein

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The source code for the ML approaches etc. can be retrieved from here: https://github.com/LagLukas/transfer_learning.

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The original online version of this article was revised due to incorrect given name and family name of the authors in all references. Now, they have been corrected.

This article is part of the topical collection “Computational Intelligence” guest edited by Kurosh Madani, Kevin Warwick, Juan Julian Merelo, Thomas Bäck and Anna Kononova.

Appendices

A Transfer Learning for Failure Count

Figure 9 displays the transfer learning experiments if the failure count value instead of the time ranked value is employed.

Fig. 9
figure 9

Overview of the effects of transfer learning on XCSF-ER (both temporal and in terms of the distribution). Note that XCSF-TL denotes XCSF-ER with transfer learning and the temporal plots are based on [25]. Here we employ the failure count value

Full size image

B Random Testing Comparison using Failure Count

See Fig. 10 and Table 6.

Fig. 10
figure 10

Boxplots displaying the performance of the used LCSs and random selection

Full size image
Table 6 p values for XCSF and XCSF-ER with a pure random selection. For the methods we consider the failure count value
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Rosenbauer, L., Pätzel, D., Stein, A. et al. A Learning Classifier System for Automated Test Case Prioritization and Selection. SN COMPUT. SCI. 3, 373 (2022). https://doi.org/10.1007/s42979-022-01255-1

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