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Since 2009, the deep learning revolution, which was triggered by theintroduction of ImageNet, has stimulated the synergy between Machine Learning(ML)/Deep Learning (DL) and Software Engineering (SE). Meanwhile, criticalreviews have emerged that suggest that ML/DL should be used cautiously. Toimprove the quality (especially the applicability and generalizability) ofML/DL-related SE studies, and to stimulate and enhance future collaborationsbetween SE/AI researchers and industry practitioners, we conducted a 10-yearSystematic Literature Review (SLR) on 906 ML/DL-related SE papers publishedbetween 2009 and 2018. Our trend analysis demonstrated the mutual impacts thatML/DL and SE have had on each other. At the same time, however, we alsoobserved a paucity of replicable and reproducible ML/DL-related SE studies andidentified five factors that influence their replicability and reproducibility.To improve the applicability and generalizability of research results, weanalyzed what ingredients in a study would facilitate an understanding of why aML/DL technique was selected for a specific SE problem. In addition, weidentified the unique trends of impacts of DL models on SE tasks, as well asfive unique challenges that needed to be met in order to better leverage DL toimprove the productivity of SE tasks. Finally, we outlined a road-map that webelieve can facilitate the transfer of ML/DL-based SE research results intoreal-world industry practices.

Synergy between Machine/Deep Learning and Software Engineering: How Far Are We?