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According to the Reqtest team, machine learning is a hot trend this year, bringing revolutionary changes in workflows and processes.
In software testing, machine learning can be used for:

• Test suite optimization, to identify redundant and unique test cases.
• Predictive analytics, to predict the key parameters of software testing processes on the basis of historical data.
• Log analytics, to identify the tests cases which need to be executed automatically.
• Traceability, extracting keywords from the Requirements Traceability Matrix (RTM) to achieve test coverage.
• Defect analytics, to identify high-risk areas of the application for the prioritization of regression test cases.

Read nine more recent testing trends from the Reqtest editors.

Analysing the Maven Central Repository during the second half of 2018, a group of scientific researchers led by Benoit Baudry, Professor in Software Technology at the KTH Royal Institute of Technology, reveals that Maven Central contains more than 2.5 million artifacts, a real treasure of extraordinary software development. More than 17% of the libraries have several versions that are actively used by a large number of clients.
However, 1.3 million dependencies declared are actually not used. Also, a vast majority of APIs can be reduced to a small, compact core and still serve most of their clients. 

For a more accurate exploration of the Maven Central ecosystem, read Benoit Baudry's article posted on Medium.com:
A journey at the heart of 2.4 million Maven artifacts. 

On the XWiki project, we've been pursuing a strategy of failing our Maven build automatically whenever the test coverage of each Maven module is below a threshold indicated in the pom.xml of that module. We're using Jacoco to measure this local coverage.

We've been doing this for over 6 years now and we've been generally happy about it. This has allowed us to raise the global test coverage of XWiki by a few percent every year.

More recently, I joined the STAMP European Research Project and one our KPIs is the global coverage, so I got curious and wanted to look at precisely how much we're winning every year. 

I realized that, even though we've been generally increasing our global coverage (computed using Clover), there are times when we actually reduce it or increase very little, even though at the local level all modules increase their local coverage...

Read Vincent Massol, XWiki CTO, full post and learnings