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Preparing for Confluence 7.10

✓ Planned changes

✓ Implemented changes

Latest milestone

8 December 2020

7.10.0-rc1

Planned changes

In this section you will be provided with an overview of the changes that Atlassian intends to make, so you can start thinking how it might impact your app. We’ll indicate when a change has been implemented, and in which milestone.

Seraph upgrade

Status: COMPLETE

In this release Atlassian is upgrading atlassian-seraph from 4.0.4 to 4.1.0, which removes the ability to use query parameters to log in. This might affect your development environment and automated tests:

If you’re starting Confluence with AMPS in dev mode (atlassian.dev.mode=true) this change won’t affect you.
If you’re relying on the os_username+os_password query parameter for faster logins, you’ll need to add the atlassian.allow.insecure.url.parameter.login=true system property. We’ve used this workaround in several of our builds.
If you’re using the stateless test runner, you can add the use.form.login=true system property to your test config, for slower, but safe builds which more closely match how end users use the product.

XStream upgrade

Status: COMPLETE

Atlassian is also upgrading the version of XStream bundled with Confluence. This is a major upgrade that breaks compatibility with the old format of stored data. To help with this, they are adding a layer of backward compatibility to support existing data in Bandana.

See XStream 1.4 upgrade for full details.

The special milestone is no longer available as this change is now available in 7.10.0-m43.

OAuth 2.0 authentication

Status: DELAYED

The Atlassian team is adding the ability to configure OAuth 2.0 authentication. Within Confluence this will mostly be used for mail server authentication (such as a POP3 incoming mail server), but will also be available to apps.

See OAuth 2.0 integration for developers.

They’re still working on the mail server configuration screens in Confluence, that will allow you to select OAuth 2.0 as the authentication method, so they’ve rolled back the OAuth 2.0 changes for now.

Create and reply by email

Status: DELAYED

Atlassian has been improving the Create by email and Reply by email plugins. These plugins have always been disabled by default, and were quite difficult to use because there was no UI for configuring the feature.

They’re making some improvements to the way the feature works, and adding a new admin screen which administrators can use to enable the features.

They’re still working on this one, and will provide more information at a later date.

Jira Service Desk is now Jira Service Management

Status: COMPLETE

As you may already be aware, from version 4.14 Jira Service Desk becomes Jira Service Management. In this release Atlassian has updated the places in Confluence that refer to Service Desk to now refer to Service Management. This is just a visual change, and has no impact on compatibility.

Removal of editor-v3

Status: ADVANCE NOTICE

When Atlassian upgraded their editor to TinyMCE v4 way back in Confluence 6.14, they introduced an unsupported dark feature that could be used to revert to the v3 editor (frontend.editor.v4 / frontend.editor.v4.disable). They plan to remove this dark feature flag in a future Confluence release, as they are no longer tested or reliable.

Once that happens, the editor-v3 WRM context will no longer be loaded. Any resources used only in this context can be removed completely. The editor and editor-v4 contexts are unchanged, and with this change, resources in editor-v4 can be safely moved to editor.

Implemented changes

In this section we’ll provide details of changes that Atlassian has implemented, organised by the milestone they are first available in. This will help you decide which milestone to use when testing.

Release Candidate – 8 December 2020

No significant changes in this release candidate.

Beta 1 – 30 November 2020

In this beta:

Seraph upgrade. See above for more details.

EAP 2 – 23 November 2020

Milestone 7.10.0-m43

In this milestone:

XStream upgrade. See XStream 1.4 upgrade for details.
Upgraded Spring from 5.1.14 to 5.1.18.

EAP 1 – 16 November 2020

Milestone 7.10.0-m32

In this milestone:

Jira Service Desk is now Jira Service Management in the Confluence UI.

Passing the Ball to Technology: How Innovation is Impacting Sports Broadcasts

Sports always attract a lot of attention. International competitions like the Olympic Games and the FIFA World Cup are broadcast live on national TV channels, and there are many people who discuss players online. Watching your favourite athletes and experiencing tense moments with them is something we all are so used to.

However, we still cannot watch semi-professional or non-professional teams live; they are often left behind in the backstage of big sports. This is happening due to the high cost of broadcasting, and due to the complexity of the process and technologies required for high-quality data transmission. Thankfully, we are already seeing evidence that this situation will change dramatically in the near future; broadcasts will become more accessible to matches of any level due to the introduction of new approaches. Let's take a look at such innovations in sports and see how they are changing the industry.

Minimizing the Impact of Quarantine Restrictions

Automatic online broadcasting technologies are becoming even more important with the introduction of COVID-19 restrictions around the world, which have caused the current crisis in sports. Sport teams have been unable to play matches and have not made any revenue from them. Similarly, sports broadcasters lost hundreds of millions of dollars because they could not show ads during games. The fans stayed at home and will not return to the stadiums soon.

The pandemic has increased the need for automatic, high-quality match broadcast solutions that require minimal human management. Football clubs such as Barcelona, Bayern Munich, and Celtic are already considering using an automatic online broadcasting system. Even before the pandemic, analysts predicted in 2019 that technology that attracts fans will have the greatest impact on sports in the future. We see that this is indeed the case (such as the the LA Galaxy fan engagement platform that GlobalLogic helped develop), and COVID-19 has only further influenced this trend.

Information is Changing the Sports Industry

There are two ways to grow and use video content in sports: (1) broadcast personalization and (2) sports analytics.

Sporting event broadcasts are becoming more and more personalized. For example, how about switching between cameras and watching match moments that are of interest to you? During the 2020 NFL season, CBS Sports deployed 26 cameras so that football fans could choose the one to watch — both live and on-demand. For now, such technology is not available to amateur sports, but it is a promising start.

Sports analytics is developing much faster than personalization. By capturing the video footage during a game, coaches and managers can quickly adapt their strategies and set new goals. Sporting matches now require more preparation than before, and the competition has become more intense in both professional and amateur leagues. Athletes can also watch a repeat of a sporting event, analyze their performance, and identify what can be improved. In addition, the entire team can prepare for future matches, noting the strengths and weaknesses of their opponents.

Technologies are Affordable to All

The implementation of advanced technology requires big money. While professional teams can purchase expensive equipment and hire a large team of specialists, amateur clubs cannot afford these luxuries. However, more and more new technologies are emerging that enable online broadcasting at relatively low costs. Some of these technologies can even be used in educational settings. For example, Polk County schools in North Carolina recently joined the National Federation of High Schools (NHFS) Network to stream sporting events online.

The main task of such systems is simple and obvious: to minimize the budget and to make the process of broadcasting games as simple as possible in both technical and organizational terms (i.e., the solution can be accessible for semi-professional and amateur clubs).

Pixellot AI Camera Solution

One such affordable broadcast system alternative is the Pixellot AI camera solution, which GlobalLogic helped develop and continues to evolve. Pixellot automates the end-to-end video production and data collection for broadcasting and coaching. The solution consists of several cameras in a single camera head unit, which is connected to a computer that controls the shooting mode (i.e., panoramic view or television view). The system (1) analyzes video streams in real-time, (2) automatically recognizes and tracks objects on the field, and (3) generates an authentic broadcast feed that follows the action of the game.

Each broadcast displays not only the total score of the game, but also the actions of each individual player (e.g., goals, assists, interceptions, blocks, fouls, etc.). All this is possible due to machine learning (ML) and artificial intelligence (AI) algorithms. The Pixellot system is able to recognize, monitor, and detect command colours. It can define the boundaries of a playing field, the role of the people who are on it (i.e., players, referees, etc.), and what is happening on it (e.g., pass, goal, kick, etc.).

Pixellot is also making inroads into content personalization, which as I mentioned before, is a highly desirable technology for the sports industry. The system generates all players statistics per match or season in two clicks; creates highlight reels in moments; and allows you to download these videos, edit them, share them on social networks, comment on them, or save them. Whether you are a spectator, coach, talent recruiter, the Pixellot system makes the viewing process more interactive and interesting. It also opens up new horizons for analyzing and evaluating young players' prospects. Through analytics, an athlete can see their strengths and weaknesses and track their progress.

Future Development

This new broadcast technology will only evolve and spread in the future. We can already see this, especially when there are about 40,000 online broadcasts of 130 sports leagues on 6,000 fields and stadiums every month. Every day, more and more people are joining different platforms to watch online broadcasts. This applies even to school competitions. For example, the Jupiter Christian School in Florida has installed equipment for broadcasting sports games so that they can be watched at home. This is also important when talking about reducing the number of contacts during the quarantine.

To improve the user experience when watching broadcasts online, integration with OTT media like Netflix or Megogo — which according to Forbes is already displacing traditional television — will be strengthened. The user will have an entire OTT online platform for their local or amateur club, which is currently nowhere to be found. In addition, there may also be integration with sports bookmakers, which are a large market.

Also in the future, viewers will be able to watch the game in a more interactive form using augmented or virtual reality. For example, with Pixellot, GlobalLogic plans to expand the system features and allow each user to freely choose what to look at: a ball, goal, player, or any other object. Our developers are also working on creating (1) a heat map of the field to track where a player was most active, as well as (2) an attack map that shows to which side of the field a player is most likely to pass, etc.

So as online broadcasts gain momentum and become more commonplace and familiar to fans, new technologies will become integral to improving and expanding the current sports industry.

Jira Software 8.12.x release notes

Improved user selection

We have optimized the behavior of single user selection to make it easier to identify users. Now the selection shows the full name and avatar of the user. This way, the custom field user selection has the same appearance as the user system fields such as Reporter and Agent.

Filtered by category

Monitoring log events are divided into categories. With a Jira Data Center license, you can now filter the log by one or more categories. This is especially useful if, for example, you only want to view security and authentication events.

Filter by summary

Get even more specific by filtering by summary. This allows you to drill down to specific events, such as created groups or created projects.

Change log file retention settings

In Jira Data Center, we write events to a monitoring log file in your local outgoing directory. This file can be used as an additional record and when integrated with third-party logging aggregation tools.

Previously, the number of log files stored was limited to 100. This is now configurable, allowing you to store more or less as needed. The maximum size of each file is 100 MB. Therefore, make sure that you have enough storage space available on each node.

New Events

We’ve added new events to track priority, secure administrator login (Websudo), problem export, and OAuth 2.0 integration to give you better visibility into your application.

Support for MySQL 8.0

We are adding another database to the list of supported platforms – MySQL 8.0.

Reduced impact of the app on Jira indexing of (Data Center)

Worried about your indexing times? Read on, as this feature may change that. The document-based replication feature we introduced reduces the impact of apps on indexing time and prevents index inconsistencies in the Jira Data Center. This is independent of the time it takes apps to index data. When DBR is enabled, the Jira Data Center is much more horizontally scalable. The more nodes in the cluster, the better the overall throughput while maintaining search consistency. For more information, see Document-Based Replication in the Jira Data Center.

Users created automatically with just-in-time user provisioning (Data Center)

Just-in-time user provisioning (JIT provisioning) allows users to be automatically created and updated when they log in to Atlassian data center applications such as Jira, Confluence or Bitbucket via SAML SSO or OpenID Connect (OIDC) SSO.

Without JIT, SSO login fails if the user is not present in any of the user directories (be it a remote LDAP or the internal directory). If JIT is enabled, a user can be created just in time. This allows an immediate logon without having to create the user manually in the product first. The data required to provision the user is taken from the SSO response after user authentication, which must be configured in the Identity Provider (IdP) you have selected. Learn More

Small improvements for your day

- Sequence of the statuses
  The order of the statuses displayed in the columns on your card now reflects the order you set in the card configuration. If you do not want Chosen for Development to always lag behind, go to Board> Configure and shuffle the statuses as you wish.

Accessibility: background in subtle keys
We’ve added a new entry helper option that allows you to add a gray background to subtle buttons (which are normally displayed on hover) to make them stand out better. As with all other accessibility features, you can view them under Profile> Accessibility.

Future Undercurrents for Insurance Underwriting

Introduction

Insurance underwriting is a process of analyzing and evaluating the risks involved in insuring people and assets. However, with a surge in technology, almost every aspect of life has become technology-oriented. The advent of software has put individual underwriters and their jobs at great risk. We are observing a diametric shift from risk management to scientific data analysis. In an era of the machine versus the mind, will insurance underwriters be able to sustain themselves?

What’s Coming Your Way

According to Forbes, insurance underwriting was listed as one of the “10 most endangered jobs in 2015.” Citing data from the Bureau of Labor Statistics (BLS), U.S., it forecasts that employment in this role is expected to fall by 6 percent — from 106,300 insurance underwriters in 2012 to fewer than 99,800 in 2022. The partnerships between several tech giants and insurers have resulted in the development of products such as IBM’s Watson and the SymbioSys underwriting engine, which are fueling the fact that underwriters might be replaced by off-the-shelf software.

This article is not just limited to the role of underwriters and the possibilities in technological advancements. It will also touch upon various technologies that affect the insurance industry and whether it will be apt to believe that humans will be replaced by the computer algorithms.

What Does an Underwriter Do?

Underwriting is a job that has existed for as long as there has been a need for insurance. It’s one of the critical steps involved in the process of issuing insurance policies. Underwriters examine the amount they are going to write in relation to the premium that is being paid for that risk. They decide whether or not to write that risk followed by policy issuance for an individual or a business entity. Any wrong conclusion can actually affect the insurer’s solvency ratio, further impacting the balance sheet as millions of dollars are at stake.

Traditional vs Automated Approach

To quote insurance policy, the traditional ways of dealing with linear processing has rapidly changed from month(s) to days, and further, to a few clicks. Emerging technologies like the Internet of Things, Artificial Intelligence, and Machine Learning are revamping the overall underwriting process with the help of readily available customer data. The growth in these large amounts of data has initiated this predictive risk management approach.

Companies are moving to a more robust analysis of risk attributes and are applying a dynamic approach to their evaluations by gaining insights from the data. This allows them to be aware of the changing aspects of risk ahead of the market. It also ensures that they are competitive and aligned with the needs of their prospective customers. A trend that looks promising for the future is that tomorrow’s top performers in the industry will have underwriters who play considerably varied and diverse roles, such as sales executive or decision scientist, customer advocate or innovator. This will further empower business process innovation and also provide cross-functional support.

According to the industry analyst firms, supercomputers’ cognitive capability is one of the most prominent reasons behind this cultural change. A lot of research and development is happening, and enterprises are making huge investments to take the first mover advantage. For instance, IBM has invested billions of dollars creating a Watson supercomputer. The main USP of Watson is just like that of a human: it learns from whatever data is fed into it. It is then able to analyze, do research, and give suggestions in a simple and understandable language.

Productivity Measurement

With the evolving technologies, traditional ways of measuring underwriters' productivity on performance indicators like percentage of applications approved, turnaround time, etc., would no longer be relevant. As the industry is showing signs of maturity for embracing the cultural shift, the performance indicators have to match the new age underwriting process. Some of the key performance indicators include fine-tuning the AI algorithms for analyzing the data, identifying upfront which risk class is favorable, preparing the better risk assessment models leading to lower expense ratio, etc.

Though it is just a thought based on the fact that industry is evolving, over a period of time, these will get more refined and in-tune with the prevailing market theme.

A Note Before We Part

On one hand, the future of insurance will be determined by the advent of powerful new technologies. There will be challenges to find the right talent to capitalize on this data-driven transformation. On the other hand, due to practical and strategic reasons, underwriters have a clear and urgent opportunity to diversify their skills moving forward, which can be in line with the changing landscape of the industry as a whole.

Also, we may say that although the underwriting role may be at the brink of closure, there will still be a need for these individuals to spend a good portion of their time interacting with potential clients and brokers, proving they are excellent “people persons.” They also need to keep themselves updated with the latest market trends and technologies.

With their experience and good judgement, underwriters can further fine-tune these emerging AI solutions rather than just define the workflow or play with BPM tools. This shift from prognostic risk measurements to highly data scientific roles will be one of the key factors that will break the status quo and will decide the future path for underwriters down the line.

References

https://riskandinsurance.com/will-technology-replace-underwriters/

https://www.ey.com/Publication/vwLUAssets/EY-the-future-of-underwriting/$FILE/EY-the-future-of-underwriting.pdf

https://www.carriermanagement.com/features/2017/01/11/162831.htm

The 5G Wedlock with OTT: The Journey Begins

In this white paper, GlobalLogic takes a look at how 5G brings with it a host of new possibilities and will expand the scope of growth and revenue generation, especially for the media and entertainment industry.

In just a decade, 5G is estimated to grow by 79%, making video consumption, augmented reality, and virtual reality more efficient. With 5G, the world of gaming will be more competent and collaborative whilst opening up newer avenues for advertisement.

This paper also takes a closer look at the impact of 5G and its five important pillars that will enable immersive media for a mass-market. 5G

Strategies for Digital Transformation with Microservices

Digital transformation is a process that relies not only on technology, but also on other digital transformation driving forces such as people and skills development, alliance development, customer experience digitalization and enhancement, value add across products, and innovation. In all digital transformation initiatives, technology is the primary enabler that provides the necessary building blocks to empower change across an organization.

Microservices have proven to be a bullet-proof approach to digitally transforming a business by attacking existing technical debt, simplifying complex current scenarios, and using a clean and robust microservice architecture. Learn more in this white paper on strategies for using microservices to achieve digital transformation.

GlobalLogic Cafe: How Personalized Ads Work in a Cookie-Less (CTV) Environment

Ever wondered how Google manages to know so much about users likes and dislikes so accurately and how they are able to recommend solutions? The answer is by having access to relevant and accurate user data. In the AD technology space, data plays a critical role in defining the success of a business.

With Google planning to phase out the use of third-party cookies in its Chrome browser, advertisers are increasingly seeking granular audience data from other sources (such as digital content accessed by apps and streamed over smart TVs, mobile, or OTT devices) to preserve the accuracy of their ad targeting.

Increasingly, publishers or product owners are trying to grow their addressable audiences through authentication and registration strategies. They are working to create their own personalized ad targeting capabilities for their user base across platforms rather than relying on third party systems. In Advertising outside the walled gardens, these third party sources collect user information via cookie syncing and other methods to serve targeted advertisements.

Our GlobalLogic team will take you through a solution which leverages a customer data platform (CDP) to unify customer data and personalize ad targeting, all while complying with privacy policies such as GDPR and CCPA.

Bitbucket Server and Data Center 7.8 release notes

✓ Rerun builds from Bitbucket

✓ Database password encryption

✓ Graceful shutdowns (Linux only)

Rerun builds from Bitbucket

With Bitbucket Server 7.4, Atlassian has introduced Integrated CI/CD. Designed to get from idea to production faster, it provides new ways to get continuous feedback on your code directly in Bitbucket, reducing the need to switch tools.

Starting with this release, you can re-run builds from Bitbucket. On the Builds page and the Builds pull request page, there is now an Actions column with a menu button for each build. From here you can start the actions authorisation process, and then you can re-run Bamboo plans and Jenkins jobs from this menu.

Database password encryption

To add an extra layer of security, you can now encrypt the database password, which is stored in the bitbucket.properties file. You can use one of the two encryption methods Atlassian offers in this version or create your own.

Graceful shutdowns (Linux only)

Bitbucket now supports an orderly shutdown through the SIGTERM signal. Performing an orderly shutdown allows Bitbucket to complete all scheduled tasks, ongoing Git requests and running user requests before exiting. This in turn helps prevent users from losing work during maintenance tasks that require a shutdown.

What else is new?

Atlassian has added JMX metrics for Integrated CI/CD build actions
The SSLv2Hello protocol has been removed from the default list of mail SSL/TLS protocols. For more details, see BSERV-11889 – Bitbucket fails to send mail to Office365 when using Java 11 Closed
If you require this protocol, then it can be reinstated by adding the following entry to the bitbucket.properties file:
```
mail.crypto.protocols=SSLv2Hello TLSv1 TLSv1.1 TLSv1.2
```

Powering Up MLOps Production Lines for Frictionless AI

Artificial intelligence (AI) has been hailed as the new electricity due to its potential to transform business. Unfortunately, unleashing the full potential of AI is a challenge for many enterprises.

It’s proving to be a struggle to get the machine learning (ML) models underpinning new AI applications over the line. Despite widespread ML investment by businesses that understand the potential of this technology, a staggering 60% of ML projects initiated never make it into production. To put this into perspective, imagine a car manufacturer that has a budget to build 100 cars on its production line but only completes 40 cars. This is obviously an undesirable outcome and an unsustainable business model.

Once developed, problems persist. Gartner predicts: “through 2020, 80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organisation.”

One issue is that the growth and evolution of data science tools and technologies and expertise has not been supported with equally mature ML development methods or processes. This has a direct impact on the ability to effectively manage release cycles to deploy ML models in production and manage the continuous retraining and release cycles required. The remains a major barrier to the success of ML projects for most organisations.

If this sounds alarmingly familiar, don’t despair.

The emergence of MLOps – a practice for collaboration and communication between data scientists and operations teams to help manage ML development lifecycles – is a game changer.

At GlobalLogic, our MLOps service blends our deep expertise in enterprise-grade DevOps with our pedigree in IT operations service and support and ML engineering capabilities. Importantly, we help you clear not just the technical hurdles but the cultural ones too.

Our MLOps frameworks are built on Amazon SageMaker to simplify and automate ML workflows and get models into production faster and more cost effectively.

Interestingly, several enhancements were announced this month at AWS re:Invent that will bring even more workflow efficiencies. One is Amazon SageMaker Studio, an integrated development environment (IDE) that allows developers and data scientists to build, code, develop, train and tune machine learning workflows using a single interface.

Another announcement that caught my eye is Amazon SageMaker Model Monitor. It detects deviations such as data drift which degrade model performance over time, so you can take remedial action.

Amazon Sagemaker Operations for Kubernetes is yet another new and exciting capability. It enables organisations to release workflows with much-needed portability and standardisation, supporting security, high availability and regulatory requirements.

Around our MLOps frameworks we wrap our unique, tried and tested pod-style approach to enablement. Our MLOps Enablement Pods provide outcome-focused teams of data scientists and MLOps engineers that remain flexible in their resource profiling so that your day-to-day team is exactly what you need at every stage.

Specifically, our MLOps Enablement Pods operate in sprints, embedding in your teams for short periods to instil new skills and tooling as well as new processes and ways of working into your business, while keeping a tight control on costs. By keeping your pipelines clear, we help you get AI projects into production quickly and cost effectively.

To find out how our MLOps wizardry can help you extract more value from your ML strategy and unblock your AI project pipelines, please get in touch with our team here.

Jira Software 8.13.x release notes

Get the Jira Server app on managed devices

You can now distribute the Jira Server mobile app to your team using your Mobile Device Management (MDM) solution. This is great if you need to restrict the app to company-approved devices, or a particular set of users.

If your MDM solution supports the AppConfig standard, you can also save your users time and prevent mistakes by pre-populating your site URL in the app. Got a lot of sites? No problem, you can pre-populate multiple site URLs. Logging in has never been easier.

Hungry for new features?
A lot has changed since Atlassian released Jira 8.5.x, which also was a Long Term Support release. They introduced issue archiving, improved performance and accessibility, and did other spectacular things to make Jira better for you. See all the changes and fixes that happened between JIRA 7.13 and 8.13 in Atlassian’s change log.

API change log
To help you get a full picture of what has changed since the last Long Term Support releases, we’ve prepared the REST API change log.

Long Term Support releases, performance-wise
For every Jira release, Atlassian runs extensive performance tests to compare the latest version to the previous one. This is to see how the new features affect Jira, and to make sure that any performance regressions are not introduced.

They’re also comparing performance between Jira Long Term Support releases. These are usually a few versions apart, so the improvements are much more visible than between smaller, feature releases.

You can see the results of our Jira 8.5 LTS Jira 8.13 LTS performance comparison in Performance and scale testing.

Always a good time to clean up Jira
If you feel your Jira is slowing down and users can no longer find what they want, we’ve got something for you. Check out our Jira cleanup guide for tips and tricks how to start a cleanup project and enhance team productivity.

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