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Experience report on the application of genetic algorithms to reduce costs of the software validation process in the automotive sector during an engine control unit project

Abstract

The number of electronic control units (ECU) installed in vehicles is increasingly high. Manufacturers must improve the software quality and reduce cost by proposing innovative techniques. This research proposes a technique being able to generate not only test-cases in real time but to decide the best means to run them (hardware-in-the-loop simulations or prototype vehicles) to reduce the cost and software testing time. It is focused on the engine ECU software which is one of the most complex software installed in vehicles. This software is coded by using Simulink® models. Two genetic algorithms (GAs) were coded. The first one is in charge of choosing which parts of the Simulink® models should be validated by using hardware-in-the-loop (HIL) simulations and by using prototype vehicles. The second one tunes the inputs of the software module (SM) under validation to cover these parts of the Simulink® models. The usage of dynamic-linked libraries (dlls) is described to deal with the issues linked to SM interactions when running HIL simulations. GAs found at least 7 more bugs than traditional techniques and improved the functional and code coverage by between 3 and 11% for functional coverage and by between 1.4 and 7% for code coverage depending on the SM complexity. The validation time is reduced by 11.9% compared to traditional techniques. GAs perform better than traditional techniques improving software quality and reducing costs and validation time. The usage of dlls allows testing the software in real time as described in this study.

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Notes

  1. Orthogonal array testing is a black box testing technique that is a systematic, statistical way of software testing. It is used when the number of inputs to the system is relatively small, but too large to allow for exhaustive testing of every possible input to the systems. It is particularly effective in finding errors associated with faulty logic within computer software systems” (Delius, 2004).

  2. Wang and Winner present a method, in which the automated driving function is executed passively, in series production vehicles, which is sometimes known as the shadow mode (Riedmaier et al., 2020; Wang & Winner, 2019).

  3. Low system states are functional states at low level. Consequently, the functional state cannot be detected by the driver.

  4. The optimal path is a part of the model (Fig. 2) that is composed of functional states. These states meet the conditions to change states and the path has the lowest cost.

  5. The reader can find a beta version of the code used in this research. See Appendix.

  6. The number of vectors that will be mutated depends on the need. In this research, the authors have chosen 30% of the vectors to be mutated with good empirical results.

  7. Feedback from other projects means bugs found in a project which could impact another project.

  8. Considering the complexity of this case-study, the number of variables used as predictors must be limited. Otherwise, it would be extremely complex to draw conclusions.

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Correspondence to Pedro Miguel Ortega-Cabezas.

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Appendix

Appendix

For confidentiality reasons, only a beta version can be provided. It can be downloaded in the following link:

https://github.com/pedroai1980/ga.git

The version provided by the authors tries to solve the problem shown in Fig. 21. The reader could reuse it with some changes such as adding automation scripts to each transition between states. Adding calls to Simulink® models to assess conditions to go from one state to another one.

Fig. 21
figure 21

Problem to be solved by using the code provided by the authors

The code is composed of the following files:

  1. i.

    utils.py. This file defines the functions necessary to assess conditions for going from one state to another one. The reader can replace and add the functions they want or they can even add calls to Simulink® models.

  2. ii.

    main_v2.py. This file runs the code to solve the problem.

  3. iii.

    g2_func.py and genetic_funcs.py contain the code of the two genetic algorithms necessary to solve the problem.

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Ortega-Cabezas, P.M., Colmenar-Santos, A., Borge-Diez, D. et al. Experience report on the application of genetic algorithms to reduce costs of the software validation process in the automotive sector during an engine control unit project. Software Qual J 30, 687–728 (2022). https://doi.org/10.1007/s11219-021-09582-x

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Keywords

  • Engine control unit software testing
  • Genetic algorithms
  • Model-based testing
  • Black-box testing
  • Cause-effect technique