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Unveiling Anomalies and Their Impact on Software Quality in Model-Based Automotive Software Revisions with Software Metrics and Domain Experts

Conference paper
Authors Jan Schröder
Christian Berger
Miroslaw Staron
Thomas Herpel
Alessia Knauss
Published in Proceedings of the 25th International Symposium on Software Testing and Analysis (ISSTA 2016)
ISBN 978-1-4503-4390-9
Publication year 2016
Published at Department of Computer Science and Engineering (GU)
Language en
Links dl.acm.org/citation.cfm?doid=293103...
Keywords Metrics; Outliers; Model-based; Empirical Case Study
Subject categories Software Engineering

Abstract

The validation of simulation models (e.g., of electronic control units for vehicles) in industry is becoming increasingly challenging due to their growing complexity. To systematically assess the quality of such models, software metrics seem to be promising. In this paper we explore the use of software metrics and outlier analysis as a means to assess the quality of model-based software. More specifically, we investigate how results from regression analysis applied to measurement data received from size and complexity metrics can be mapped to software quality. Using the moving averages approach, models were fit to data received from over 65,000 software revisions for 71 simulation models that represent different electronic control units of real premium vehicles. Consecutive investigations using studentized deleted residuals and Cook’s Distance revealed outliers among the measurements. From these outliers we identified a subset, which provides meaningful information (anomalies) by comparing outlier scores with expert opinions. Eight engineers were interviewed separately for outlier impact on software quality. Findings were validated in consecutive workshops. The results show correlations between outliers and their impact on four of the considered quality characteristics. They also demonstrate the applicability of this approach in industry.

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