Is it possible to optimize software product testing using machine learning and data science techniques ?

Given that you have access to software test cases, test case logs and code coverage data, what machine learning techniques you can use to find insights. What types of insights can be drawn and how ? Is there a way to predict bugs before shipping the product to customer or better help QA team to analyze the product. This is an interesting domain and it seems Data Science hasn't touched it yet. Links to project or research papers are highly appreciated.