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State of the Art Software Development in the Automotive Industry and Analysis upon Applicability of Software Fault Prediction - Softcover
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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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State of the Art Software Development in the Automotive Industry and Analysis upon Applicability of Software Fault Prediction
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State of the Art Software Development in the Automotive Industry and Analysis upon Applicability of Software Fault Prediction
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State of the Art Software Development in the Automotive Industry and Analysis upon Applicability of Software Fault Prediction
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 206 pp. Deutsch. Seller Inventory # 9783736995086
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State of the Art Software Development in the Automotive Industry and Analysis upon Applicability of Software Fault Prediction
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Seller Inventory # 9783736995086
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State of the Art Software Development in the Automotive Industry and Analysis upon Applicability of Software Fault Prediction
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State of the Art Software Development in the Automotive Industry and Analysis upon Applicability of Software Fault Prediction
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Cuvillier, Cuvillier Mär 2017, 2017
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ISBN 13: 9783736995086
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Cuvillier Verlag, Nonnenstieg 8, 37075 Göttingen 206 pp. Deutsch. Seller Inventory # 9783736995086
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State of the Art Software Development in the Automotive Industry and Analysis upon Applicability of Software Fault Prediction
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Taschenbuch. Condition: Neu. State of the Art Software Development in the Automotive Industry and Analysis upon Applicability of Software Fault Prediction | Harald Altinger | Taschenbuch | Audi Dissertationsreihe 119 | 206 S. | Englisch | 2017 | Cuvillier | EAN 9783736995086 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 109009721