Implicit and explicit knowledge mining of Crowdsourced communities: Architectural and technology verdicts

Abstract

The use of social media especially community Q & A Sites by software development community has been increased significantly in past few years. The ever mounting data on these Q & A Sites has open up new horizons for research in multiple dimensions. Stackoverflow is repository of large amount of data related to software engineering. Software architecture and technology selection verdicts in SE have enormous and ultimate influence on overall properties and performance of software system, and pose risks to change if once implemented. Most of the risks in Software Engineering projects are directly or indirectly coupled with Architectural and Technology decisions (ATD). Advance Architectural knowledge availability and its utilization are crucial for decision making. Existing architecture and technology knowledge management approaches using software repositories give a rich insight to support architects by offering a wide spectrum of architecture and technology verdicts. However, they are mostly insourced and still depend on manual generation and maintenance of the architectural knowledge. This paper compares various software development approaches and suggests crowdsourcing as knowledge ripped approach and brings into use the most popular online software development community/Crowdsourced (StackOverflow) as a rich source of knowledge for technology decisions to support architecture knowledge management with a more reliable method of data mining for knowledge capturing. This is an exploratory study that follows a qualitative and qualitative e-content analysis approach. Our proposed framework finds relationships among technology and architecture related posts in this community to identify architecture-relevant and technology-related knowledge through explicit and implicit knowledge mining, and performs classification and clustering for the purpose of knowledge structuring for future work.