A Case Study on the "Jungle" Search for Industry-Relevant Regression Testing - Réseaux, Informatique, Systèmes de Confiance Access content directly
Conference Papers Year : 2023

A Case Study on the "Jungle" Search for Industry-Relevant Regression Testing

Abstract

The optimization of regression testing (RT) has been widely studied in the literature, and numerous methods exist. However, each context is unique. Therefore, how to tell which method is appropriate for a specific industrial context? Recent work has proposed a taxonomy to aid in answering this question. The approach is to map both the RT problem and existing solutions onto the taxonomy, aiming to determine which solutions are best aligned with the problem. This paper presents a case study that evaluates the approach in a real setting. The context is the development of R&D projects at a major automotive company, in the domain of connected vehicles. We used the taxonomy to characterize the RT problem in terms of measurable effects, and to identify the technically feasible solutions from a set of 52 papers. We report on the beneficial aspects but also the difficulties of the approach, due to unclear taxonomy elements, missing ones and paper classification errors.
Fichier principal
Vignette du fichier
QRS_Jungle-7.pdf (254.82 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04294958 , version 1 (20-11-2023)

Licence

Copyright

Identifiers

Cite

Maria Laura Brzezinski Meyer, Hélène Waeselynck, Fernand Cuesta. A Case Study on the "Jungle" Search for Industry-Relevant Regression Testing. 23rd IEEE International Conference on Software Quality, Reliability & Security (QRS 2023), Oct 2023, Chiang Mai, Thailand. ⟨10.1109/QRS60937.2023.00045⟩. ⟨hal-04294958⟩
48 View
37 Download

Altmetric

Share

Gmail Facebook X LinkedIn More