A Systematic Mapping Study on the Debugging of Autonomous Driving Systems
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914239358173184 |
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| author | Shaw, Nathan Pennada, Sanjeetha Hierons, Robert M Shin, Donghwan |
| author_facet | Shaw, Nathan Pennada, Sanjeetha Hierons, Robert M Shin, Donghwan |
| contents | As Autonomous Driving Systems (ADS) progress towards commercial deployment, there is an increasing focus on ensuring their safety and reliability. While considerable research has been conducted on testing methods for detecting faults in ADS, very little attention has been paid to debugging in ADS. Debugging is an essential process that follows test failures to localise and repair the faults in the systems to maintain their safety and reliability. This Systematic Mapping Study (SMS) aims to provide a detailed overview of the current landscape of ADS debugging, highlighting existing approaches and identifying gaps in research. The study also proposes directions for future work and standards for problem definition and terminology in the field. Our findings reveal various methods for ADS debugging and highlight the current fragmented yet promising landscape. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_04293 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Systematic Mapping Study on the Debugging of Autonomous Driving Systems Shaw, Nathan Pennada, Sanjeetha Hierons, Robert M Shin, Donghwan Software Engineering As Autonomous Driving Systems (ADS) progress towards commercial deployment, there is an increasing focus on ensuring their safety and reliability. While considerable research has been conducted on testing methods for detecting faults in ADS, very little attention has been paid to debugging in ADS. Debugging is an essential process that follows test failures to localise and repair the faults in the systems to maintain their safety and reliability. This Systematic Mapping Study (SMS) aims to provide a detailed overview of the current landscape of ADS debugging, highlighting existing approaches and identifying gaps in research. The study also proposes directions for future work and standards for problem definition and terminology in the field. Our findings reveal various methods for ADS debugging and highlight the current fragmented yet promising landscape. |
| title | A Systematic Mapping Study on the Debugging of Autonomous Driving Systems |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2601.04293 |