With the trends of developing software on the Internet, many software crowdsourcing platforms are emerging. They attract a lot of developers to bid for crowdsourced projects and develop software systems collaboratively. In this paper, we present CrowDevBot, a task-oriented conversational bot for software crowdsourcing platform, that aims to assist online users in completing crowdsourcing-related tasks in a more natural manner. The key idea of CrowDevBot is to: (1) combine a rulebased method and an SVM-NaiveBayes-C4.5 integrated learning method to discover users' intention; (2) employ an integrated CRF (conditional random field) method with novel features to improve the performance of slot filling; and (3) leverage a software service knowledge base to unify entity names and predefine the key slots of user query. We implement CrowDevBot and integrate it into JointForce, an IT software crowdsourcing platform in China. To the best of our knowledge, this is the first time that a task-oriented conversational bot is practically used in software crowdsourcing platform(s). We evaluated our approach on real data set from JointForce. The results show that our intention detecting method achieves F1-score of 87% on the limited training data. For the slot filling, the F1-score of our integrated CRF model reaches 82%, 8% higher than that of the normal CRF model. Index Terms-Task-oriented conversational bot, software crowdsourcing platform, integrated statistical learning, user intention understanding