Kubernetes security solutions provider Alcide achieved a milestone this week. The Israel-based company has earned the AWS Outposts Ready designation, which is part of the Amazon Web Services (AWS) Service Ready Program.
This designation is a big deal for the young company. It recognizes that Alcide's platform has demonstrated successful integration with AWS Outposts deployments. AWS Outposts is a fully managed service that extends AWS infrastructure, AWS services, APIs, and tools to virtually any datacenter, co-location space, or on-premises facility for a consistent hybrid experience.
The Alcide platform has three main modules: Advisor, kAudit, and Runtime. Advisor is a Kubernetes multi-cluster vulnerability scanner that covers rich Kubernetes and Istio security best practices and compliance checks. kAudit automatically identifies anomalous behavior and suspicious activity based on the Kubernetes audit log. It also allows defining custom rules and alerts when certain Kubernetes actions occur. RunTime (ART) protects the container network with a microservices firewall and a threat detection engine. It also tracks processes running in containers themselves.
"We know the importance of helping customers and organizations more easily identify potential security risks in order to take action," said Joshua Burgin, General Manager for the AWS Outposts group, in a statement. "With Alcide Advisor and Alcide ART available to customers on AWS Outposts, we are able to provide a comprehensive view of (a customer's) security posture on their infrastructure, on AWS Outposts, and in AWS Regions, both on premises and in the cloud, for a truly consistent hybrid experience."
I had an opportunity to talk with Gadi Naor, Alcide's CTO and co-founder earlier this year at the beta launch of the company's sKan, a solution designed to provide "end-to-end continuous security guardrails" for Kubernetes deployments.
"We keep hearing from our customers that they want to bring Kubernetes security insights to developers early on," Naor told me at the time. "sKan stretches our main security platform into the comfort zone of the developers who are building applications running on Kubernetes in the most automated and seamless manner, without interrupting their development workflow."
Posted by John K. Waters on September 17, 2020 at 8:35 AM0 comments
Enterprises are adapting their software security efforts to support DevOps as CI/CD instrumentation and operations orchestration have become standard components of organizations' software security initiatives. That's one of the insights from the latest Building Security In Maturity Model (BSIMM ) report from Synopsis.
First published in 2009, the BSIMM is the result of a multiyear study of real-world software security initiatives (SSIs). It was developed to provide a "fact-based" set of best practices for developing and growing an enterprise-wide software security program. That set of practices was the first maturity model for security initiatives created entirely from real-world data. The latest BSIMM is available for download now.
This edition of the BSIMM report was authored by Sammy Migues, principal scientist at Synopsys, and one of the original developers of the BSIMM, John Steven, founding principal of Aedify Security, and Mike Ware
Sr. director of technology at Synopsys.
"The purpose of the BSIMM is to quantify the activities carried out by various kinds of [software security initiatives] across many organizations," the report's authors explain. "Because these initiatives use different methodologies and different terminology, the BSIMM requires a framework that allows us to describe any initiative in a uniform way. Our software security framework (SSF) and activity descriptions provide a common vocabulary for explaining the salient elements of an SSI, thereby allowing us to compare initiatives that use different terms, operate at different scales, exist in different parts of the organizational chart, operate in different vertical markets, or create different work products."
The 11th BSIMM report was the result of the efforts of more than 8.4k security software security professionals, who guide the efforts of almost 500k developers. This edition examines practices across 130 companies in a range of industries, from financial to health care, to identify and help solve their software security challenges.
The list of emerging trends in this report for DevOps teams includes:
- Software security efforts are matching pace with software delivery: New activities show a shift toward DevSecOps, including: SM3.4 Integrate software-defined lifecycle governance, AM3.3 Monitor automated asset creation, CMVM3.5 Automate verification of operational infrastructure security
- Organizations are "shifting everywhere:" The "shift left" concept has evolved from performing security testing earlier in the development cycle to performing as soon as artifacts are available.
- Security champions are evolving in firms embracing DevOps and DevSecOps: This is to recruit members from cloud and related roles to apply their expertise as code for organizational benefit.
The BSIMM has proved to be a useful reflection of the current state of software security initiatives in the enterprise, and given how hard it can be to get any organization to communicate honestly about its security practices, something of a miracle. As Gary McGraw, co-author of the original BSIMM, likes to say, it was a science that escaped the test tube to become a de facto standard.
"That's very gratifying, personally," McGraw told me in a 2015 interview, "but the important thing is the emphasis here on real data, and the use of facts in computer security. I think we've finally moved past the witchdoctor days in software security."
Posted by John K. Waters on September 15, 2020 at 8:53 AM0 comments
We finally dipped our quarantined toes into the ever-widening podcast ocean last week, because we just didn't have enough to do around here. But seriously, after more than two decades on this beat, it really seemed like the right time to start sharing some of the amazing conversations I get to have on a daily basis with the brilliant and inventive people driving high tech.
We were lucky to have as our first guest Ashique KhudaBukhsh, a project scientist in the School of Computer Science at Carnegie Mellon University's Language Technologies Institute (LTI). I met Ashique in January, when he was still a post-doctoral researcher. I stumbled upon one of his team's published papers, and I called him to talk about what they were up to. That conversation led to two stories in ADTmag's sister publication, Pure AI.
Ashique and his team are engaged in a unique and compelling line of research. He and his colleagues are using artificial intelligence (AI) to analyze online comments in social media and pick out those that defend or are sympathetic to disenfranchised groups. That research led to the development of machine learning classifiers that effectively sort the "hopeful" and "helpful" from the hateful on social media.
The LTI researchers focused initially on finding supporting content about the Rohingya people, who began fleeing Myanmar in 2017 to avoid ethnic cleansing. Ashique explains why that group was chosen, and how his team used the fastText text representation and classification library with polyglot embedding. He also explains how they developed an original strategy they call "active sampling," which used the nearest neighbors in the comment-embedding space to construct a classifier able to detect comments defending the Rohingyas among larger numbers of disparaging and neutral comments.
He also talks about how Facebook, Twitter, YouTube, and other social media platforms, which are employing strategies to identify hate speech and misinformation on their platforms, could use his team's machine learning classifiers to complement that effort.
Ashique is teaching now, but his research continues, and he came to the podcast ready to share his story. It's a great story. You should check it out.
The WatersWorks Podcast will be available soon on iTunes and other podcast apps. But you can listen to it now on the Pure AI website. While you're there, feel free to read the two stories about Ashique's team's work ("Carnegie Mellon Uses AI To Counter Hate Speech with 'Hope Speech'" and "Carnegie Mellon Continues its Research on "Hostility-Diffusing, Peace-Seeking Hope Speech"). They include links to his group's research papers, which you also might want to read.
We'll be podcasting twice a month. I'll let you know when we finish the next one.
Posted by John K. Waters on September 3, 2020 at 7:46 AM0 comments
GitHub's upgrade this year to Ruby 2.7 was a massive, months-long undertaking that required a serious investment in engineering resources and time. The team maintaining the popular Microsoft-owned code-hosting-and-collaboration platform recently shared some of the details of that transition, which, among other things, required that they fix more than 11,000 warnings.
"Fixing that many warnings, some of which were coming from external libraries, takes a lot of coordination and teamwork," observed Eileen M. Uchitelle, a staff software engineer at GitHub and core Rails team member, in a blog post. "In order to be successful we needed a solid strategy for sharing the work."
Ruby 2.7 was released last December; the GitHub team completed the upgrade this summer and deployed to production in July. The team completed a major Rails upgrade almost exactly two years ago.
"Upgrading Rails on an application as large and as trafficked as GitHub is no small task," Uchitelle wrote in an earlier post. "It takes careful planning, good organization, and patience. The upgrade started out as kind of a hobby; engineers would work on it when they had free time. There was no dedicated team. As we made progress and gained traction it became not only something we hoped we could do, but a priority."
The team learned a lot from that Rails upgrade, Uchitelle said, and they used that knowledge on the Ruby upgrade, which was a bit more focused undertaking. They set up the application to be dual-bootable in both Ruby 2.6 and Ruby 2.7 by using an environment variable, she said. "This made it easy for us to make backwards compatible changes, merge those to the main branch, and avoid maintaining a long running branch for our upgrade," she said. "It also made it easier for other engineering teams who needed to make changes to get their system running with the new Ruby version."
GitHub was built with Ruby on Rails and launched in February 2008, and it's now one of the largest source code hosting service in the world, with an estimated 40 million users and more than 100 million repositories. The app itself is huge: more than 400,000 lines of code. And it gets 100s of pull requests daily.
One of the key goals of the upgrade was to make it possible to run both Ruby and Rails in deprecation-free mode and not be left behind in the future by a modern upgrade cadence, the code hoster has said. With this release, future versions of Ruby will no longer accept passing an options hash when a method expects keyword arguments. "At GitHub, we're committed to running deprecation-free on both Ruby and Rails to prevent falling behind on future upgrades," Uchitelle said.
Uchitelle left no doubt that the team feels the latest upgrade was worth all this effort, if for the performance improvements alone. " The Ruby Core team is well on their way to fulfilling the promise of Ruby 3.0 being 3x faster, she said. She also pointed to improvements in application boot times in production mode (down from about 90 seconds to about 70 seconds). She also cited a decrease in object allocations, which went from about 780k allocations to about 668k allocations. Object allocations affect available memory, she noted, so it's important to lower these numbers whenever possible.
"For any companies that are wondering if this upgrade is worth it, the answer is: 100%," she said. "Even without the performance improvements, falling behind on Ruby upgrades has drastic negative effects on the stability of your codebase. Upgrading Ruby supports your application health, improves performance, fixes language and framework bugs, and guides the future of the language!"
Both of Uchitelle's blog posts are well worth reading: "Upgrading GitHub to Ruby 2.7," and "Upgrading GitHub from Rails 3.2 to 5.2."
Posted by John K. Waters on September 1, 2020 at 1:42 AM0 comments
Microsoft wants to empower its customers to lift and shift their Java and Spring workloads to Azure, while also helping them to modernize their application stack with best-in-class enterprise messaging in the cloud. Toward that end, Redmond recently announced preview support for Java Message Service (JMS) 2.0 over AMQP in Azure Service Bus premium tier.
The Advanced Message Queuing Protocol (AMQP) is an open standard application layer protocol for passing business messages among apps or organizations. It comprises an efficient wire protocol that separates the network transport from broker architectures and management. AMQP version 1.0 supports a range of broker architectures that may be used to receive, queue, route, and deliver messages, or used peer-to-peer.
Microsoft's Azure Service Bus is a fully managed enterprise integration message broker that can decouple applications and services. It's used to connect applications, devices, and services running in the cloud, and often acts as a messaging backbone for cloud-based apps.
Microsoft program manager Ashish Chhabria announced the support in a blog post.
"The enterprise messaging ecosystem has been largely fragmented compared to the data ecosystem until the recent AMQP 1.0 protocol standardization in 2011 that drove consistent behavior across all enterprise message brokers guaranteed by the protocol implementation," Chhabria wrote. "However, this still did not lead to a standardized API contract, perpetuating the fragmentation in the enterprise messaging space.
"The Java Enterprise community (and by extension, Spring) has made some forward strides with the Java Message Service (JMS 1.1 and 2.0) specification to standardize the API utilized by producer and consumer applications when interacting with an enterprise messaging broker. The Apache QPID community furthered this by its implementation of the JMS API specification over AMQP. QPID-JMS, whether standalone or as part of the Spring JMS package, is the de-facto JMS implementation for most enterprise customers working with a variety of message brokers."
In this preview, Azure Service Bus supports all JMS API contracts, Chhabria said, enabling customers to bring their existing apps to Azure without rewriting them. The list of JMS feature supported today includes:
- Temporary queues.
- Temporary topics.
- Shared durable subscriptions.
- Shared non-durable subscriptions.
- Unshared durable subscriptions.
- Unshared non-durable subscriptions.
- Auto-creation of all the above entities (if they don't already exist).
- Message selectors.
- Sending messages with delivery delay (scheduled messages).
To connect an existing JMS based application with Azure Service Bus, Chhabria explained, simply add the Azure Service Bus JMS Maven package or the Azure Service Bus starter for Spring boot to the application's pom.xml and add the Azure Service Bus connection string to the configuration parameters.
Posted by John K. Waters on August 26, 2020 at 12:12 PM0 comments
Google introduced the beta version of its open-source Jib tool for containerizing Java applications in July 2018 with relatively little fanfare. Two years later, the tool has put on some serious muscle in the form of new features and plug-ins, and quietly become a developer favorite.
Jib is an open-source Java tool maintained by Google for building Docker images of Java applications. Jib 1.0.0, released to general availability last year, was designed to eliminate the need for deep Docker mastery. It effectively circumvented the need to install Docker, run a Docker daemon, and/or write a Dockerfile.
Jib accomplishes this by separating the Java application into multiple layers for more granular incremental builds. (Traditionally, a Java app is built as a single image layer with the application JAR.) "When you change your code, only your changes are rebuilt, not your entire application," the GitHub page explains. "These layers, by default, are layered on top of a distro-less base image."
"Jib has come a long way since it went GA," wrote Google software engineers Chanseok Oh and Appu Goundan in a blog post, "and now has a sizable community around it. The core Jib team has been working hard to expand the ecosystem, and we're confident that the community will only grow larger."
For example, Google publishes Jib as both a Maven and a Gradle plugin. The GitHub repository of Jib extensions to those plugins--the Jib Extension Framework, published in June-- enables users to easily extend and tailor the Jib plugins behavior. Jib extensions are supported from Jib Maven 2.3.0 and Jib Gradle 2.4.0.
"We think that the extension framework opens up a lot of possibilities, from fine-tuning image layers to containerizing GraalVM native images for fast startup or jlink images for small footprint," Oh and Goundan, said.
Google published first-party Jib Maven and Gradle extensions to cover the Quarkus framework's "special containerization needs." (It was already possible to direct Quarkus to create an optimized image with the core Jib engine without applying the Jib build plugin.) Using the Jib build plugins enables finer-grained control over how to build and configure an image compared with Quarkus' built-in Jib engine-powered containerization.
Google has also put some effort into supporting the implementation of first-party integration for Spring Boot in Jib. For example, Jib's packaged containerizing-mode now works out of the box for Spring Boot, containerizing the original thin JAR rather than the fat Spring Boot JAR that's unsuitable for containerization.
Finally, Google has made sure that Jib works out of the box with Skaffold File Sync. Skaffold is a command line tool that facilitates continuous development for Kubernetes-native applications. Using the keyword auto, developers can take advantage of remote file synchronization to a running container with zero sync configuration.
Posted by John K. Waters on August 25, 2020 at 10:41 AM0 comments
So much Red Hat news has been coming out of the KubeCon + CloudNativeCon EU 2020 Virtual event this week that it has been hard to keep up. We reported earlier on the spotlight announcements around its dev tools for Kubernetes. But that was just the tip of the iceberg. The IBM subsidiary has had a busy week!
Here's a roundup of Red Hat's other big revelations from the show:
OpenShift 4.5 Gets Virtualization Platform
Red Hat's enormously popular packaged distribution of the open-source Kubernetes container management and orchestration system gets an upgrade that includes a new virtualization platform.
OpenShift 4.5, announced this week, includes OpenShift Virtualization, a new platform feature that enables IT organizations to bring standard VM-based workloads to Kubernetes, helping eliminate the workflow and development silos that typically exist between traditional and cloud-native application stacks. Virtual machines can now coexist side-by-side with cloud-native services and containers on Kubernetes simultaneously, either for to be rebuilt as a container image or to simply make workflows more efficient, Red Hat said in a statement. OpenShift 4.5 also introduces full-stack automation for VMware vSphere deployments, making it "push-button" easy to deploy OpenShift on top of all currently supported vSphere environments.
Advanced Cluster Management for Kubernetes Goes GA
The advanced cluster management capability, now generally available, was designed to help organizations more effectively scale OpenShift deployments via unified Kubernetes management.
Built specifically for a cloud-native world, the cluster management toolset supports containerized application deployments across multiple clusters, whether an organization is just beginning to explore cloud-native computing or they are running next-generation workloads in production, Red Hat says. Advanced Cluster Management for Kubernetes "meets organizations where they are on their containerization journey," from container proofs-of-concepts to containerized production deployments, with tools to more effectively manage multiple Kubernetes clusters and enforce security policies and governance controls. The toolset also provides a single control plane, which aims to eliminate the fragmented tools that can be required to manage Kubernetes across the hybrid cloud.
New Edge-Computing Support
Red Hat also announced the addition of new capabilities and technologies to its hybrid cloud portfolio designed to support enterprise-grade edge computing, starting with OpenShift.
Red Hat OpenShift now supports three-node clusters, scaling down the size of Kubernetes deployments without compromising on capabilities, and making it better suited for space-constrained edge sites. The Advanced Cluster Management for Kubernetes tools provides management for thousands of edge sites along with core locations via a single, consistent view across the hybrid cloud, which managing scaled out-edge architectures as straightforward as traditional datacenters, Red Hat says.
Red Hat Joins Intuit on Argo Project
The two company's announced that they will be collaborating on Argo CD, a declarative continuous delivery tool for Kubernetes deployments. If it works as planned, the tool will make it easier to manage configurations, definitions, and environments for both Kubernetes itself and the applications it hosts using Git as the source of truth.
Argo CD, which was open sourced by Intuit in January 2018, is also an incubation-level project within the Cloud Native Computing Foundation (CNCF) and is currently deployed in production by many companies, including Electronic Arts, Major League Baseball, Tesla, and Ticketmaster.
Red Hat, a long-time leader in the open-source community, will help to drive the contributor base and engage with a broader open source ecosystem, the company's said. Red Hat also intends to work to integrate Argo's GitOps capabilities into future versions of OpenShift, which would provide a more developer-centric way of controlling Kubernetes infrastructure and applications.
Posted by John K. Waters on August 20, 2020 at 12:37 AM0 comments
Facial recognition technology has been taking it on the chin lately (pardon the pun). Earlier this week, the BBC reported that a UK court ruled the use of the technology by British police violated human rights and data protection laws in that country. A week before that, a team of researchers at the University of Chicago unveiled Fawkes, an algorithm and software tool that makes pixel-level changes to your image that are invisible to the human eye, but effectively mask you from the current crop of facial recognition applications. And back in July, Amazon, Google, and Microsoft were sued over claims they used photos of individuals to train their facial recognition software without getting prior consent, which violated an Illinois biometric privacy statute. (Facebook had already settled a class-action claim that it also violated that law.)
Remember when we were all thrilled that we could open our phones with our faces?
One of the most important things to keep in mind as you're thinking about the future of the facial recognition technology industry, Gartner analyst Nick Ingelbrecht told me, is that it's not a single monolithic market today, and it never has been.
"It's made up of lots of different segments and use cases," he said, "ranging from 1:1 verification of customers' identities to border control screening; mobile payment verification to password replacement; one-to-many face matching for building access control to the many-to-many systems the police use."
Ingelbrecht is a research director with Gartner's Technology and Service Provider Research group. He focuses on computer vision, emerging trends and technologies, video and image analytics, and physical security. I asked him about recent developments in the facial recognition technology market, and announcements by Amazon, Microsoft, and IBM that they would be curtailing their efforts around this technology.
"The large US technology companies have been backing away from marketing facial recognition products for some years now," he said. "The recent announcements are just the latest in a series of retrenchments by large US tech firms to avoid the reputational risks and potential legal exposures associated with misapplication of biometric technologies. These decisions tend to slow commercial adoption generally, as well as the investment return on research. That said, facial recognition is not going away. Technologies will continue to mature, and research continues outside the US, especially in the People's Republic of China, where there is a very large internal market for facial recognition products."
Amazon has said it will stop selling facial recognition technology to police forces for a year. Microsoft, which doesn't currently sell facial rec tech to U.S. law enforcement, said it won't do so until the federal government passes a law regulating its use. And in a letter sent to Congress in June, IBM's CEO Arvind Krishna said his company has sunset its general-purpose facial recognition and analysis software products, and he called on Congress to regulate the use of the controversial technology by police.
Ironically, the most recent lawsuit against the three tech giants cited their use of IBM's Diversity in Faces Dataset, which the company developed to reduce racial and gender inaccuracies and biases in the technology. Big Blue released the dataset in January 2019 to the global research community "to advance the study of fairness and accuracy in facial recognition technology."
That effort was a response, at least in part, to conclusions by researchers from MIT and Stanford University in 2018, who found that commercially available facial-analysis programs from major technology companies demonstrated both skin-type and gender bias. Facial recognition is a type of image recognition technology that detects faces in captured images, and then quantifies the features of the image to match against a templates stored in a database. In the researchers' experiments, facial recognition algorithm errors in three leading solutions were 49 times more likely for dark-skinned women than white men. These results raised serious questions about how neural networks, which learn to perform computational tasks by looking for patterns in huge data sets, are trained and evaluated.
Clearly, facial recognition technology has come a long way since Woodrow Wilson Bledsoe began his pioneering work in a field he all but created back in the 1960s. It's now part of a class of biometric tech in increasingly widespread use, primarily in security applications, that includes fingerprint, iris, speech, and gate recognition. It's also worth a lot of money--billions, according to industry watchers. The global facial recognition market was valued at $3.4 billion in 2019 (according to a Grand View Research report) and is anticipated to expand at a CAGR of 14.5% from 2020 to 2027.
Biometrics continue to be used extensively across a range of security applications--primarily access control and attendance tracking. And recent headlines notwithstanding, the technology also continues to improve, evolve, and expand at an explosive rate. Advancements in artificial intelligence and machine learning have been applied to biometric technology, leading to increased accuracy and accessibility.
Despite recent controversies and what could be called growing pains, facial recognition technology isn't going away any time soon, Ingelbrecht said. However, the market is already changing.
"The current arguments about facial recognition in the US will put further pressure on small vendors and accelerate consolidation, aggregation, or exit at a time when the industry is suffering from a slump in buying activity, severe cash constraints, and supply chain difficulties," he said. "Outside the US, we expect facial recognition technologies to evolve at a rapid pace. It is ironic that in Europe, the GDPR [General Data Protection Regulation] has made commercial deployments of facial recognition very difficult, while governments and law enforcement there enjoy exemptions. In the US, there is extensive commercial use of facial recognition, especially in the retail and hospitality sector, but constraints are largely targeted at law enforcement."
Ingelbrecht's advice to the facial rec tech vendors during this volatile time: "They need to be very focused on their target market segments and differentiate themselves clearly via the business value they deliver to customers."
Posted by John K. Waters on August 13, 2020 at 11:02 AM0 comments
Software development toolmaker JetBrains, has been on a bit of a product-release binge that started on July 28 with the release of IntelliJ IDEA 2020.2, which was followed by the releases of the IntelliJ Scala Plugin 2020.2, PyCharm 2020.2, CLion 2020.2, PhpStorm 2020.2, the EduTools Plugin 3.9, GoLand 2020.2, IntelliJ Rust 2020.2, the Space Beta, and TeamCity 2020.1.3.
Leading this pack of products promulgations, of course, is the venerable code-centric Java IDE, IntelliJ IDEA. The company released version 2020.1, the first major update of the year, in April with support for the latest Java 14 release, as well as new features for several Web and test frameworks, an upgrade of the debugger with dataflow analysis assistance, and a new LightEdit mode. The company's newest product is Space, an all-in-one team collaboration environment.
IntelliJ IDEA 2020.2 comes with numerous updates, including the ability to review and merge GitHub pull requests from inside the IDE, navigate between warnings and errors in a file with the Inspections widget, view the full list of issues in a current file with the Problems tool window, and get notified if code changes would break other files. This release also provides new features for Jakarta EE, Quarkus, Micronaut, Amazon SQS API, and OpenAPI.
But the marquee feature in this release is probably support for Java 15, which is due in September. IntelliJ IDEA 2020.2 is fully ready for that release, said Zlata Kalyuzhnaya, JetBrains marketing manager, in a blog post. "We've updated our support for the Records feature, which is now in its second preview, added basic support for Sealed Classes, and provided full support for Text Blocks, which are a full-fledged feature in Java 15," she wrote.
The list of features Kalyuzhnaya highlighted in this release includes:
- Inlay hint: If changes you make to a Java method or field will cause errors in other files, the IDE will notify you about it with an inlay hint. Click on the hint and the IDE will provide a list of the errors to fix.
- Pinpointing runtime exception causes: In this release, JetBrains has supplemented exception stack trace analysis with dataflow analysis. Clicking on the stack trace takes you to the exact place in the code where the exception appears.
- Improved autocompletion for Stream API methods: This release of the IDE is designed to work better with the Stream API. I allows you to start typing the stream method name within the collection itself, which will trigger IntelliJ IDEA to insert 'stream()'automatically. Also, the IDE now suggests chained calls of expected type in the autocompletion.
- New Variable refactorings: This release introduces this feature, which allows you to replace occurrences of a variable in intermediate scopes, as opposed to only replacing one or all occurrences.
- Regrouped Java Live Templates: This release groups the Java live templates under the Java node in Settings/Preferences to make it easier for developers to locate them among the live templates for all the other languages.
To learn more, visit the Java section of the JetBrains' what's new page.
Posted by John K. Waters on August 12, 2020 at 12:13 PM0 comments