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Multi-language design smells: a backstage perspective

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Abstract

Multi-language systems became prevalent with technological advances. Developers opt for the combination of programming languages to build a single application. Such combinations of programming languages allow the reuse of existing code and libraries without re-implementing the code from scratch. Software quality is achieved by following good software development practices and avoiding the bad ones. However, most of the practices in the literature apply to mono-language systems only and do not consider the interaction between programming languages. We previously defined a catalog of bad practices i.e., design smells related to multi-language systems. This paper aims to provide empirical evidence on the relevance of multi-language design smells and their perceived impacts on software quality. We analysed eight open source projects to detect occurrences of 15 types of multi-language design smells. We also extracted information about the developers that contributed to those systems. We performed an open and a closed survey targeting developers in general but also developers who contributed to those systems. We surveyed developers about the perceived prevalence of multi-language design smells their severity, and their impact on software quality attributes. We report that most of the studied design smells are perceived as design or implementation problems. Our results suggest that the studied design smells could be introduced mainly during refactoring and maintenance activities, and during regular development tasks. Our results also point that multi-language design smells are perceived as harmful and have negative impacts on software quality. The perceived prevalence of design smells and their impact varies from one specific smell type to the others. We believe that our findings are important for developers and researchers interested in improving the quality of multi-language systems as it can help them prioritize design smells during maintenance activities.

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Notes

  1. https://www.ibm.com/developerworks/library/j-jni/index.html

  2. https://github.com/objectcomputing/OpenDDS/blob/945a0df6f4a2e52be9eb766d7b717d146d1649f1/java/idl2jni/runtime/i2jrt_TAOObject.cpp

  3. https://android.googlesource.com/platform/frameworks/base/+/ba34751/core/jni/android_opengl_GLES10Ext.cpp

  4. https://github.com/oracle/graal/issues/1388

  5. https://github.com/sosy-lab/java-smt/issues/87

  6. https://github.com/zeromq/zmq-jni/commit/6b0d25dd45de42680c965ec8c27f7596661bb6fa

  7. https://github.com/google/conscrypt/blob/23c2ce258afd5a43258b685df4279eaa2ee0e15c/common/src/main/java/org/conscrypt/NativeCrypto.java

  8. https://www.developer.com/java/data/jni-data-type-mapping-to-cc.html

  9. https://github.com/java-native-access/jna/commit/a77f47fb297dd33a8cec4c88ae4777186882f472

  10. https://github.com/google/conscrypt/blob/master/common/src/jni/main/cpp/conscrypt/native_crypto.cc

  11. https://www.linkedin.com/

  12. https://share.polymtl.ca/alfresco/service/api/node/content/workspace/SpacesStore/b7fbaa9e-8055-41cc-b016-dac345f6cb97?a=false&guest=true

  13. https://www.checkmarket.com/

  14. https://s-ca.chkmkt.com/?e=189517&h=B445656A9769E90&l=en

  15. https://spectrum.ieee.org/at-work/innovation/the-2018-top-programming-languages

  16. https://osf.io/6yqv5/?view_only=4cca6dc961b44303833917c236e2d667

  17. https://github.com/ResearchML/Replication_EMSE

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Acknowledgements

Our sincere gratitude goes to the anonymous respondents who answered the open and closed surveys. We also thank the anonymous reviewers for their comments and suggestions that significantly improved this paper.

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Authors and Affiliations

  1. DGIGL, Polytechnique Montreal, Montreal, QC, Canada

    Mouna Abidi, Md Saidur Rahman, Moses Openja & Foutse Khomh

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  1. Mouna Abidi
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  2. Md Saidur Rahman
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  3. Moses Openja
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  4. Foutse Khomh
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Correspondence to Mouna Abidi.

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Communicated by: Neil Ernst

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Appendix

Appendix

We provide in this section the details about the detection results and the smell occurrences detected in our subject systems presented in Table 1. We also highlight the total number of responses sent and received from running the survey.

Table 11 provides the smells occurrences detected when running MLSInspect (Abidi et al. 2021) on the 270 snapshots of our subject systems described in Section 3. The columns reported in Table 11 reflects the subject systems analysed. The first row (#Snap) reports the number of snapshots in each system, while the rest of the rows describe the occurrences of smells detected in each system.

Table 11 Overview of the Detected Smell Occurrences in the Studied Systems
Full size table

Table 12 summarizes the total number of surveys that we sent, the number of answers received, and the number of answers we kept for the analysis of this paper. Note that we did not keep answers that contain information related only to the background section as those participants did not fill any of the sections related to the purpose of this study. Another important point is that in this study, we initially aimed to capture results for nine systems as stated in our initial protocol (Abidi et al. 2020). However, the results we received are only for eight systems as shown in Table 12.

Table 12 Survey Responses Overview
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Abidi, M., Rahman, M.S., Openja, M. et al. Multi-language design smells: a backstage perspective. Empir Software Eng 27, 116 (2022). https://doi.org/10.1007/s10664-022-10161-2

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