Advances in the use of cognitive and machine learning (ML) enabled systems\nfuel the quest for novel approaches and tools to support software developers in\nexecuting their tasks. First, as software development is a complex and dynamic\nactivity, these tasks are highly dependent on the characteristics of the\nsoftware project and its context, and developers need comprehensive support in\nterms of information and guidance based on the task context. Second, there is a\nlack of methods based on conversational-guided agents that consider cognitive\naspects such as paying attention and remembering. Third, there is also a lack\nof techniques that make use of historical implicit or tacit data to infer new\nknowledge about the project tasks such as related tasks, task experts, relevant\ninformation needed for task completion and warnings, and navigation aspects of\nthe process such as what tasks to perform next and optimal task sequencing.\nBased on these challenges, this paper introduces a novel paradigm for\nhuman-machine software support based on context, cognitive assistance, and\nmachine learning, and briefly describes ongoing research activities to realize\nthis paradigm. The research takes advantage of the synergy among emergent\nmethods provided in context-aware software processes, cognitive computing such\nas chatbots, and machine learning such as recommendation systems. These novel\nparadigms have the potential to transform the way software development\ncurrently occurs by allowing developers to receive valuable information and\nguidance in real-time while they are participating in projects.\n