Software development teams, an essential component of the software ecosystem, frequently face organizational and social anti-patterns known as community smells. The occurrence of these smells leads to technical debt, which affects the entire software ecosystem. Therefore, exploring the nature of these smells and finding ways to refactor them is necessary. Existing studies explored various aspects related to community smells, including their identification, detection, and prediction. However, little is known about how community smells co-occur in development communities. This paper bridges that gap by investigating these issues in the context of software communities in Bangladesh. Using a convenience sample recruitment strategy, 39 local software practitioners were chosen, and an interview-based study was conducted. The interviews were transcribed and analyzed using Straussian Grounded Theory. Data was collected on the twenty-nine community smells defined in the literature. Analyzing the data, we identified the five most prominent community smells in the software industry of Bangladesh, which are: Priggish Members, Informality Excess, Truck Factor, Time Warp and Cognitive Distance. The co-occurrence between the smells was discovered using association rule mining. Twelve association rules were discovered. Besides, an association graph was developed based on the association rules found, which can assist management to prioritize which smells to refactor first. Furthermore, refactoring strategies adopted in the local industry were identified. Finally, the strategies were ranked using the association graph to help practitioners perform efficient community smell refactoring.