Recommendations as learning: From discrepancies to software improvement

Abstract

Successful software development requires software engineering skills as well as domain and user knowledge. This knowledge is difficult to master. Increasing complexity and fast evolving technologies cause deficits in development and system behavior. They cause discrepancies between expectations and observations. We propose using discrepancies as a trigger for recommendations to developers. Discrepancies in using a software application are combined with discrepancies between development artifacts. To efficiently support software engineers, recommendations must consider knowledge bases of discrepancies and resolution options. They evolve over time along with evolving experience. Hence, recommendations and organizational learning are intertwined.