AST 2018

13th IEEE/ACM International Workshop on Automation of Software Test

In Conjunction with ICSE 2018 (

Gothenburg, Sweden. May 28-29, 2018

Topics of interest

The workshop focuses on bridging the gap between the theory and practice of software test automation.
The general theme of the workshop is automation of software test. The topics cover all aspects related to software TA, including but not limited to:

  1. Methodology: Software test automation in the context of various software development methodologies.
  2. Technology: Automation of various test techniques and methods for testing related activities, as well as for testing various types of software applications.
  3. Tools and environments: Issues in the development, operation, maintenance and evolution of software testing tools and environments, and their integration and inter-operation with other types of software development and maintenance tools and runtime support environments and platforms.
  4. Experiments, empirical studies and experience reports: Experiments, empirical studies of software test automation, as well as reports on real experiences in using automated testing techniques, methods and tools in industry.
  5. Identification of problems and visions of the future: The identification of problems that hamper wider adoption of automated test techniques, methods and tools and the analysis and specification of the requirements on automated software testing.

In addition to the general themes and topics, AST 2018 focuses on the special theme of Artificial Intelligence (AI) for TA and TA for AI/Machine Learning (ML) software.
AI/ML has recently gained much attention from both research and practice community with the heated talks on self-driving cars, robot controlled Amazon warehouse, as well as Microsoft’s AI programmer.
TA has the need to catch up by developing technologies to test such human-machine heterogeneous systems, as well as applying such technologies in TA.

To keep up with recent surge in research and practice interests in AI/ML, it is timely to review the current practices and understand the challenges confronting practitioners for the testing of AI/ML software.