Overfitting/Underfitting with Data set size
So, the underfitting means that you still have capacity for improving your learning while overfitting means that you have used a capacity more than needed for learning.

Green area is where testing error is rising i.e. you should continue providing capacity (either data points or model complexity) to gain better results. More green line goes, more flat it becomes i.e. you are reaching the point where the provided capacity (which is data) is enough and better to try providing the other type of capacity which is model complexity. 

If it does not improve your test score or even reduce it that means that the combination of Data-Complexity was somehow optimal and you can stop training.