STA 2019 Program
STA 1: The 11th IEEE International Workshop on Software Test Automation
Measuring & Analysis
Monday July 15, 1:00 – 2:30
Location: AMU 254
Session Chair: Tiberiu Seceleanu, ABB Corporate Research, Sweden
Mitigating Threats to Validity in Emprical Software Engineering: A Traceability Case Study
Nasser Mustafa, Yvan Labiche and Dave Towey
Short-term Performance Metrics Forecasting for Virtual Machine to Support Anomaly Detection Using Hybrid ARIMA-WNN Model
Juan Qiu, Qingfeng Du, Wei Wang, Kanglin Yin, and Liang Chen
Call for Papers
Test automation aims to reduce the cost and improve the effectiveness of software testing by using various techniques and methods. Other benefits are consistency and accuracy, reduction of overall test cycle time, risk mitigation of manual testing, and increase in overall product quality. Recording test activities as test scripts and playing the test scripts or record and playback are the common methods. More advanced methodologies include data-driven, keyword-driven and hybrid methods as well as framework-based methods. Among tool development, interoperability remains a major challenge.
This year’s theme of COMPSAC is “Data Driven Intelligence for a Smarter World”. In the era of “big data” there is an unprecedented increase in the amount of data collected in data warehouses. Extracting meaning and knowledge from these data is crucial for governments and businesses to support their strategic and tactical decision making. Furthermore, artificial intelligence (AI) and machine learning (ML) makes it possible for machines, processing large amounts of such data, to learn and execute tasks never before accomplished. Advances in big data-related technologies are increasing rapidly. For example, virtual assistants, smart cars, and smart home devices in the emerging Internet of Things world, can, we think, make our lives easier. But despite perceived benefits of these technologies/methodologies, there are many challenges ahead. What will be the social, cultural, and economic challenges arising from these developments? What are the technical issue related, for example, to the privacy and security of data used by AI/ML systems? How might humans interact with, rely on, or even trust AI predictions or decisions emanating from these technologies? How can we prevent such data-driven intelligence from being used to make malicious decisions?
Accordingly, submissions including applications and case studies in these areas will highly be appreciated.
Scope of the Workshop
Topics of interest include, but are not limited to, the following:
- Test automation for large, complex systems
- Model Based Testing
- Keyword Based Testing
- Test automation in the context of different software development lifecycle methodologies
- Design of high-quality, reusable tests
- Product Line Testing
- Test modeling and test methodologies
- Combinatorial testing, Test input generation
- Test-driven development and behavior driven testing
- Operation, maintenance and evolution of test tools and environments
- Application in different domains – digital world, cloud computing, healthcare
- Experiments, empirical studies, experience reports, and case studies.
- Support for testing methods, test framework, test infrastructure.
- Management of distributed test assets and test environments
- Development, operation, integration, and standardization of test tools
- Test metrics to measure test efficiency and test coverage optimization
- Test efficiency, Test effectiveness, and Lean testing approaches.
- Quantitative studies including cost vs. benefit studies.