Large-Scale Microtask Programming
| Autor: | Emad Aghayi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
business.industry Computer science Scale (chemistry) 05 social sciences 020207 software engineering 02 engineering and technology Crowdsourcing Data science Software Engineering (cs.SE) Competition (economics) Computer Science - Software Engineering 0202 electrical engineering electronic engineering information engineering Open-source software development 0501 psychology and cognitive sciences business 050107 human factors |
| Zdroj: | VL/HCC |
| Popis: | To make microtask programming more efficient and reduce the potential for conflicts between contributors, I developed a new behavior-driven approach to microtasking programming. In our approach, each microtask asks developers to identify a behavior behavior from a high-level description of a function, implement a unit test for it, implement the behavior, and debug it. It enables developers to work on functions in isolation through high-level function descriptions and stubs. In addition, I developed the first approach for building microservices through microtasks. Building microservices through microtasks is a good match because our approach requires a client to first specify the functionality the crowd will create through an API. This API can then take the form of a microservice description. A traditional project may ask a crowd to implement a new microservice by simply describing the desired behavior in a API and recruiting a crowd. We implemented our approach in a web-based IDE, \textit{Crowd Microservices}. It includes an editor for clients to describe the system requirements through endpoint descriptions as well as a web-based programming environment where crowd workers can identify, test, implement, and debug behaviors. The system automatically creates, manages, assigns microtasks. After the crowd finishes, the system automatically deploys the microservice to a hosting site. 2 page, 1 figure, GC VL/HCC 2020, Graduate Consortium |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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