Abstract Feature complexity in modern vehicles is continuously rising, requiring significant additional effort to assure the quality of the embedded software. An efficient and effective quality assurance strategy is central for deployment of a successful software product. However, test strategy decisions are usually made experience-based with poor documentation and scalability to large development teams. Hence, we propose to use the testers experience for quantifying quality characteristics as well as the impact of different tests and resulting bug fixes on the same. Then, optimization algorithms can be applied to derive a nearly optimal test strategy. This leads to an approach where test strategy decisions are transparent, scalable to different users and conserve testing experience. This paper takes the first step by examining the impact of strategical decisions on the resulting software quality. Quantification takes place using adequate software metrics. The impact of the quality assurance strategy on product quality is identified based on data of several industry projects using statistical procedures.
Metrics-based strategies for quality assurance of automotive embedded software
01.01.2017
20 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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