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<p>With numerous regression testing (RT) methods available in the literature, it is challenging to choose the right one for a specific context. Practitioners need support identifying suitable research. To this end, recent work has proposed a taxonomy. By mapping both the RT problem and existing solutions onto the taxonomy, practitioners should be able to determine which solutions are best aligned with their problem. Our work explores the practical relevance of this idea through an industrial case study. The context is the development of R&amp;D projects at a major automotive company, in the domain of connected vehicles. We developed an RT problem solving approach based on the taxonomy. Following the approach, we characterized the RT problem, identified a set of 8 potentially relevant solutions from a set of 52 papers, and empirically evaluated their suitability. Our approach was successful, as we found effective RT methods among those selected using the taxonomy. One method, in particular, demonstrated remarkable robustness across various datasets, making it a strong recommendation for the industrial partner. However, this success came at the cost of difficulties due to unclear taxonomy elements, missing elements, and paper classification errors. We conclude that the taxonomy has practical value but would have to mature for easier applicability.</p>

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Finding the right regression testing method: a taxonomy-based approach

  • 期刊论文

  • 发表日期：

    2025年8月2日

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  • Maria Laura Brzezinski Meyer,
  • Hélène Waeselynck,
  • Fernand Cuesta

摘要

With numerous regression testing (RT) methods available in the literature, it is challenging to choose the right one for a specific context. Practitioners need support identifying suitable research. To this end, recent work has proposed a taxonomy. By mapping both the RT problem and existing solutions onto the taxonomy, practitioners should be able to determine which solutions are best aligned with their problem. Our work explores the practical relevance of this idea through an industrial case study. The context is the development of R&D projects at a major automotive company, in the domain of connected vehicles. We developed an RT problem solving approach based on the taxonomy. Following the approach, we characterized the RT problem, identified a set of 8 potentially relevant solutions from a set of 52 papers, and empirically evaluated their suitability. Our approach was successful, as we found effective RT methods among those selected using the taxonomy. One method, in particular, demonstrated remarkable robustness across various datasets, making it a strong recommendation for the industrial partner. However, this success came at the cost of difficulties due to unclear taxonomy elements, missing elements, and paper classification errors. We conclude that the taxonomy has practical value but would have to mature for easier applicability.