- 206 pages
- English
- PDF
- Available on iOS & Android
About this book
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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Information
Table of contents
- 1. Introduction
- 1.1. Motivation
- 1.2. Problem Statement
- 1.3. Thesis Statement
- 1.4. Thesis Organization
- 2. Field of Study - AutomotiveSoftware Development
- 2.1. Automotive domain
- 2.2. Development process
- 2.3. Testing process
- 3. Review of Related Work
- 3.1. Empirical Evidence upon Automotive TestingMethods and Tools
- 3.2. Software Metrics
- 3.3. Case Studies
- 3.4. Public Available Datasets on SoftwareMetrics
- 3.5. Software Fault Prediction
- 3.6. Cross Project Fault Prediction
- 3.7. Imbalanced Class Distribution
- 3.8. Software Error Analysis
- 4. Development Tools andMethods used within theAutomotive Industry
- 4.1. Questionnaire Survey
- 4.2. Development Workflow
- 5. Analysis of real worldAutomotive Software Projects
- 5.1. Unique Dataset
- 5.2. Creation of the Dataset
- 5.3. Metric data Analysis
- 5.4. Bug Distribution
- 5.5. Bug Analysis and Effects upon PreventiveMeasurements
- 6. Fault prediction and Analysisupon Cross Project Prediction
- training data according to the first release milestone, see Table 6.1 for thedistribution data.6.1. Within Project Prediction
- 6.2. Increasing Performance by Up-samplingTraining Data
- 6.3. Cross Project Fault Prediction
- 7. Conclusion
- 7.1. Summary
- 7.2. Threats to Validity
- 7.3. Further Research
- Appendix
- Appendix A.Publication List
- Appendix B.Questions from the Survey
- Appendix C.Acronyms
- List of Figures
- List of Tables
- Bibliography
- Third Party Tools
- Altingers Publications
- Altingers Work submitted toreview
- Altingers Patents