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Thus, with a study involving 21 additional developers, we qualitatively assess the feasibility and usefulness of leveraging natural language feedback (automation considered critical/feasible to implement) in supporting developers during MCR activities. We surveyed 14 additional participants (12 developers and 2 researchers), not involved in the previous survey, to qualitatively assess the relevance and completeness of the identified MCR change types as well as assess how critical and feasible to implement are some of the identified techniques to support MCR activities.

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Our participants provided recommendations, specified techniques to employ, and highlighted the data to analyze for building recommender systems able to automate the code review activities composing our taxonomy. Results of our study highlight that the availability of new emerging development technologies (e.g., Cloud-based technologies) and practices (e.g., Continuous delivery) has pushed developers to perform additional activities during MCR and that additional types of feedback are expected by reviewers. The taxonomy was designed by performing three steps: (i) we generated an initial version of the taxonomy by qualitatively and quantitatively analyzing 211 review changes/commits and 648 review comments of ten open-source projects then (ii) we integrated into this initial taxonomy, topics, and MCR change types of an existing taxonomy available from the literature finally, (iii) we surveyed 52 developers to integrate eventually missing change types in the taxonomy.

To that end, we first empirically elicited a taxonomy of recurrent review change types that characterize MCR. The objective of this paper is to investigate the approaches and tools that, from a developerвЂ™s point of view, are still needed to facilitate MCR activities. Recent research has shown that available tools for Modern Code Review (MCR) are still far from meeting the current expectations of developers.

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