Predicting and evaluating software model growth in the automotive industry
Paper in proceeding, 2017

The size of a software artifact influences the software quality and impacts the development process. In industry, when software size exceeds certain thresholds, memory errors accumulate and development tools might not be able to cope anymore, resulting in a lengthy program start up times, failing builds, or memory problems at unpredictable times. Thus, foreseeing critical growth in software modules meets a high demand in industrial practice. Predicting the time when the size grows to the level where maintenance is needed prevents unexpected efforts and helps to spot problematic artifacts before they become critical. Although the amount of prediction approaches in literature is vast, it is unclear how well they fit with prerequisites and expectations from practice. In this paper, we perform an industrial case study at an automotive manufacturer to explore applicability and usability of prediction approaches in practice. In a first step, we collect the most relevant prediction approaches from literature, including both, approaches using statistics and machine learning. Furthermore, we elicit expectations towards predictions from practitioners using a survey and stakeholder workshops. At the same time, we measure software size of 48 software artifacts by mining four years of revision history, resulting in 4,547 data points. In the last step, we assess the applicability of state-of-the-art prediction approaches using the collected data by systematically analyzing how well they fulfill the practitioners' expectations. Our main contribution is a comparison of commonly used prediction approaches in a real world industrial setting while considering stakeholder expectations. We show that the approaches provide significantly different results regarding prediction accuracy and that the statistical approaches fit our data best.


Jan Schröder

University of Gothenburg

Christian Berger

University of Gothenburg

Alessia Knauss

Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers)

H. Preenja

University of Gothenburg

Mohammad Ali

Chalmers, Signals and Systems, Systems and control

Miroslaw Staron

University of Gothenburg

Thomas Herpel

Automotive Safety Technologies GmbH

Proceedings - 2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017

584-593 8094464
978-1-5386-0992-7 (ISBN)

2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017
Shanghai, China,

Subject Categories

Reliability and Maintenance

Software Engineering

Computer Systems



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