Cal for Papers -- Insights of Crowdsourcing and Swarm Intelligence of AI Techniques to Software Testing

2024-12-18

The cost of software development will be substantially reduced if the testing is performed utilizing automated testing. A more effective testing process may be enhanced by automating the challenge of constructing test cases, which has been solved using metaheuristic search-based technology and Quality. Mobile Testing as a Service provides on-demand testing service providers for mobile apps and SaaS to uphold software validation and quality technology standards by leveraging a cloud-based, includes measures testing environment. This ensures that predetermined Quality of service and service-level agreements. Most Mobile TaaS is haphazard, with a relatively small set of automated mobile testing tools. A Crowded and Swarm Sourcing-based Search and optimization Based Metaheuristic Technique can simulate the effects of in-the-wild testing without investing in a lab or buying or renting equipment. Still, there is a danger of poor testing quality and an unknown validation timetable.

In crowdsourced and swarm intelligence of software testing, the software is tested by a massive crowd and swarm of testers. The CST operates a network of the micro-tasking paradigm, in which a testing task is distributed to several testers who utilize it independently of one another. Individual employees also tend to recognize the simple faults instead of the complex ones. the consequence of collaborative testers working in pairs to develop a sustainable test report. Swarm intelligence is one of the artificial intelligence techniques used to help solve complicated problems. SI includes the interactions between individual behavior and local population behavior. Nature frequently provides a source of inspiration, particularly for complex systems. There is no centralized control system to forecast the behavior of respective agents, and individuals obey straightforward principles. Therefore, intelligent behavior that is unknowable to individuals is produced by a certain degree of random recurrence between the agents.

 Based on the total number of invalid flaws found, the maximum count of weak faults identified, and the possibility of finding more challenging defects, workers should evaluate the success of this technique.The differences between a single worker and the testers who worked in pairs and attempted to find issues are minimal. However, CST substantially influences the Quality of testing results since it reduces the frequency of invalid errors and enhances the identification of more difficult problems. The effects are higher and indicate that CST platforms might benefit from new technologies that make it simpler to assemble groups of people who can collaborate to complete testing objectives.

This special issue comes from the emergence of interest in testing AI applications and applying AI technologies to software testing. With the help of new concepts, techniques, methodologies, and modeling techniques, we solidify our understanding of the field while also working to improve processes with new tools and resources. This conference offers an international organization for practitioners and researchers to exchange cutting-edge research results. The conference accepts domains of the best practices in the field, the difficulties for both practitioners and researchers, and papers providing original research on AI testing.

 

List of Topics Areas Include, But Are Not Limited to:

  • Quality attributes and Quality models of AI applications
  • Validation of the datasets and Quality evaluation used for building the AI applications
  • Measurements of the adequacy and test data generation of testing AI applications
  • Checking the correctness for test oracle of AI application
  • Multimedia applications for Crowdsourced user interface testing
  • Automated and semi-automated for software testing on test cases
  • Analysis, development, and evolution for the Whole lifecycle of AI applications
  • Collective intelligence for a real-time method of human swarms
  • Parallel distributed intelligence using human swarming real-time method
  • AI applications for the management of testing resources and various testing activities
  • AI technologies and AI applications with specific concerns of software testing
  • Crowdsourced software testing services in supporting coordination
  • Crowdsourcing and human-centered software engineering
  • Threats and challenges of privacy in crowdsourcing

 

Guest Editors:
Dr. Faheem Khan, Department of Computer Engineering, Gachon University, Seongnam, South Korea
Dr. Umme Laila, Computer Engineering Department, Sir Syed University of Engineering & Technology, Karachi, Pakistan
Dr. Muhammad Adnan Khan, Riphah International University, Pakistan

 

Manuscript Submission – Key Dates:
Submission Deadline: 28th April, 2025
Author Notification: 20th June, 2025
Revision and Re-submission Deadline: 25th August, 2025
Paper Acceptance:   28th November, 2025