white paper update (#581)

wp/sections/conclusion.tex

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+Aibolit is a recommender system that helps improve the quality of Java classes. 
+The recommendations are learned from OSS Java projects using ML methods.
+Aibolit provides ranked recommendations for each specific Java class, 
+which differs Aibolit from others style checkers and makes it unique.
+
+Aibolit is an extendable system, allowing anyone to add new patterns and to 
+increase the training dataset and thus improve the precision and usefulness 
+of recommendations. Aibolit can also be used as a framework for analysis of 
+patterns and to decide whether any pattern, however subjective it is, is an anti- or a pro-pattern
+with respect to a particular quality metric. As a complementary result, 
+we contribute a 100K+ dataset of patterns and metrics calculated for Java classes.
+
+The first version of Aibolit is relatively simple and there is room
+for improvement. If the anti-pattern has found, we recommend to fix all instances 
+of the pattern in the code. Instead, we may consider each specific occurrence of the pattern. 
+We may exploit its relative position in the structure of the code, rather than just count 
+the frequency. Moreover, Aibolit inspects each Java class independently. But 
+we might consider the relations between classes in the future. Furthermore, 
+Aibolit's prediction model relies on patterns only. In order to improve the model, 
+we have to think about additional features, for example, information about 
+project domain or used frameworks.
+
+Aibolit is a firm step toward the next generation of tools to control
+and improve software quality. It is a complementary tool for 
+product owners who already use tools to manage software quality.
+
+
+