A Cognitive and Machine Learning-Based Software Development Paradigm Supported by Context

TL;DR

This paper proposes a new paradigm combining cognitive computing, machine learning, and context-awareness to enhance real-time support for software developers, addressing current gaps in task guidance and knowledge inference.

Contribution

It introduces a novel human-machine support paradigm that leverages context, cognitive assistance, and machine learning to improve software development processes.

Findings

Identifies gaps in current ML and cognitive support for developers.

Proposes a paradigm integrating context-aware processes, chatbots, and recommendation systems.

Highlights potential to transform software development practices.

Abstract

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