Dragon Testing is an AITestOps company focused on software testing. So far, it has obtained several million dollars order and received several million dollars pre-A round of commercial venture capital. Dragon Testing supplies an AI-Robot-Model-Driven software functional testing SaaS platform. It can be applied to different software types such as .EXE, Web, IOS/Android and mixed applications. Dragon Testing's industry-leading AI robot testing technology combined with its model-driven, mobile and computer screen recording capabilities and cross-platform compiling technique provides customers an efficient, low-cost, and maintainable automated End-to-End testing solution.
Dragon-Testing is pleased to invite university faculty and researcher of the institute to respond to a call for research proposals on AI-Robot-Model Testing Framework(ARM-TF). This is Phase One of Dragon-Testing’s funding initiative to support research in AI-Robot-Model Testing Framework. We anticipate awarding a total of 2 gifts in the $5,000 to $10,000 USD range. Larger gifts may become available in future phases of the Dragon-Testing ARM-TF Research Funding Program, subject to the outcomes from Phase One.
Dragon Testing will award the funding to the proposer's host institution as an unrestricted gift. Dragon Testing does not pay for overhead on unrestricted gifts. Indirect costs, administrative costs, and overhead will not be considered in the budgets.
During this proposal cycle, we are interested in soliciting proposals for research that will lead to direct impact on the real world UI-based Software in the technology sector.
Specifially this proposal is related to end-to-end functional testing over UI subjects like Web/iOS/Android/Windows applications. This call is intended to support further development of Dragon-Testing research. Topics of interest of phase one include:
- Robot-driven Testing technologies over different UI applications
- Automatic test case generation with AI technologies like the reinformencement learning, including test steps automatic generation. For example, in a travel domain, AI can learn how to order an air ticket via large number of similar booking procedures in different websites like expedia or priceline, and then for a new tranvel website it can automatically generat steps like puting "Suzhou" in departing-city box and "Aarhus" in destination-city box, clicking an booking button or choosing an airline item.
We aim to make the process light touch to reduce the burden of preparing an application. Applicants should submit a maximum 2-page proposal. Proposals should focus on the two primary aspects of concern for this call: the scientific contribution and routes to eventual deployment, together with a budget overview, outlining how the proposed funding will be used.
- Summary of the project. Provide a maximum 2-page, clear and concise statement of the scientific contribution and routes to eventual deployment.
- Curriculum Vitae for all project participants. In addition to CVs, please include links to DBLP and/or Google Scholar pages for the proposers involved in the proposed work.
- A proposed budget description (1 page)
Please send your proposals to emails：
Successful winners will receive an email response.