Testing context-aware software systems from the voices of the automotive industry

Santiago Matalonga, Domenico Amalfitano, Martin Solari, Jean Carlo Rossa Hauck, Guilherme Horta Travassos

Research output: Contribution to journalArticlepeer-review

20 Downloads (Pure)

Abstract

As automotive software systems evolve towards high and full driving automation, evaluating their quality becomes increasingly challenging, especially concerning emerging behaviors. Context-awareness is the capability to sense the environment and adapt behavior. Automotive software systems are Context-Aware Software Systems (CASS). Previous secondary studies in technical literature indicate a need for testing techniques for CASS. However, these studies should have investigated the information provided by the industry. Therefore, this research undertakes a Gray Literature Study to uncover evidence of CASS testing using 20 reports from 16 automotive companies as primary sources. Our findings show that industry practices exhibit quality assurance best practices, but CASS abstraction adoption still needs to be completed. Industry reports emphasize testing challenges but lack technical resolutions, relying on amassing diverse datasets for testing. This research has the potential to impact the quality assurance of automotive software systems significantly and lead industry professionals to enhance their testing process.
Original languageEnglish
JournalIEEE Transactions on Industrial Informatics
Publication statusAccepted/In press - 3 Jan 2025

Keywords

  • automotive engineering
  • autonomous vehicles
  • software testing
  • context-aware software systems
  • quality assurance
  • automotive software quality

Fingerprint

Dive into the research topics of 'Testing context-aware software systems from the voices of the automotive industry'. Together they form a unique fingerprint.

Cite this