Interactive ChatBots for Software Engineering: A Case Study of Code Reviewer Recommendation

Abstract

Recommendation systems have played a large role in the Software Engineering research landscape. Applications have ranged from source code elements, APIs and reviewer recommendations, with techniques borrowed from the Information Retrieval, and Machine Learning domains. In recent times, there has been work into a new method of interaction, which is ChatBots, especially for Software Engineering. Early work has been aimed at using bots for mining software repositories, providing task-oriented feedback for the software developer. In this work, we would like to take the ChatBots one step forward, but using them inconjunction with recommendation systems to provide an interactive experience for recommendations. As a case study, we focus on the existing reviewer recommendation systems, and propose how using a ChatBot may enhance the solution, to provide a more accurate and realistic recommendation for the practitioner. In the end, we highlight the potential and next steps to utilize ChatBots into existing Software Engineering recommendation systems.