Band 5
State of the Art Software Development in the Automotive Industry and Analysis upon Applicability of Software Fault Prediction
Aus der Reihe
Künstliche Intelligenz & Digitalisierung
Fr. 91.90
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Paperback
Erscheinungsdatum
08.08.2023
Verlag
Cuvillier VerlagSeitenzahl
202
Maße (L/B/H)
21/14.8/1.1 cm
Gewicht
274 g
Auflage
2. Auflage
Sprache
Englisch
ISBN
978-3-7369-7870-6
https://cuvillier.de/de/shop/publications/8875-state-of-the-art-software-development-in-the-automotive-industry-and-analysis-upon-applicability-of-software-fault-prediction
In recent years the amount of software within automobiles has increased up to 100 Million LOC in modern day premium vehicles. Virtually all innovations in automotive engineering in the last decade include software components. Parallel to this increasing amount, testing becomes more vital. Automotive software development follows restrictive guidelines in terms of coding standard, language limitations and processes. Traditionally testing is a core part of automotive development, but the raising number of features increases the time and money required to perform all tests. Repeating them multiple times due to programming errors might jeopardises a cars introduction on the market. SFP is a new approach to forecast bugs already at time of commit, thus to guide test engineers upon defining testing hotspots. This work reports on the first successful application using model driven and code generated automotive software as a case study and a success prediction rate up to 97% upon a bug or fault free commit. A compiled and published dataset is presented along with analysis upon the used software metrics. Performance data achieved using different machine learning algorithms is given. An indepth analysis upon factors preventing CPFP is conducted. Further usage and practical application areas will conclude the work.
In recent years the amount of software within automobiles has increased up to 100 Million LOC in modern day premium vehicles. Virtually all innovations in automotive engineering in the last decade include software components. Parallel to this increasing amount, testing becomes more vital. Automotive software development follows restrictive guidelines in terms of coding standard, language limitations and processes. Traditionally testing is a core part of automotive development, but the raising number of features increases the time and money required to perform all tests. Repeating them multiple times due to programming errors might jeopardises a cars introduction on the market. SFP is a new approach to forecast bugs already at time of commit, thus to guide test engineers upon defining testing hotspots. This work reports on the first successful application using model driven and code generated automotive software as a case study and a success prediction rate up to 97% upon a bug or fault free commit. A compiled and published dataset is presented along with analysis upon the used software metrics. Performance data achieved using different machine learning algorithms is given. An indepth analysis upon factors preventing CPFP is conducted. Further usage and practical application areas will conclude the work.
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