“It’s a brand new protocol, and it’s taking all the best ideas that have been out there for a while and trying to standardize it through the W3C,” said David Burns, head of open source at BrowserStack (a browser testing company that is on the WebDriver BiDi working group) and chair of the Browser Testing and Tools Working Group at W3C, which is the group responsible for the WebDriver and WebDriver BiDi specifications. 

The original WebDriver protocol, or WebDriver Classic, is a “remote control interface that enables introspection and control of user agents,” according to its W3C definition. Essentially, it provides a way to remotely control the behavior of web browsers so that applications can be tested in them. 

However, this protocol only offers one-way communication, meaning that the client sends a request to the server, and the server can reply only to that one request, explained Puja Jagani, team lead at BrowserStack and a key code committer for the WebDriver BiDi project.

“The server cannot initiate communication with the client but can only respond. So if something of interest happens in the browsers it cannot communicate back to the client unless the client asks for it,” explained Jagani.

The BiDi in WebDriver BiDi stands for bidirectional communication, meaning that it actually allows events in the browser to stream back to the controlling software.

According to Jagani, because browsers are event-driven, it’s helpful for the browser to be able to share events back to the client when something interesting happens. 

For instance, with this new protocol, users can subscribe to the events created when a network request is sent to or from the browser, which enables them to monitor (or modify) all outgoing requests and incoming responses.

An example of this in action involves an application that is pointing to a production database in the cloud. When testing that application, WebDriver BiDi could be used to modify outgoing requests to point to a test database so that the production database isn’t flooded with test data.

“This is only possible with bidirectional communication. It is not possible without the W3C BiDi protocol,” said Jagani.

CDP vs WebDriver

The Chrome DevTools Protocol (CDP) and WebDriver Classic have historically been often compared because they are both low-level tools — tools that execute remote commands outside of the browser, such as opening multiple tabs or simulating device mode, Jecelyn Yeen, senior developer relations engineer for Chrome, and Maksim Sadym, software engineer at Google, explained in a blog post. 

High-level tools, by contrast, are those that execute commands within the browser. Examples of these include Puppeteer, Cypress, and TestCafe.

CDP does enable bidirectional communication, but it’s limited for testing purposes because it only works for Chromium-based browsers, like Google Chrome, and wouldn’t work in Firefox or Safari. According to Yeen and Sadym, “WebDriver BiDi aims to combine the best aspects of WebDriver ‘Classic’ and CDP.”

However, BrowserStack’s Burns emphasized that this new protocol isn’t intended to replace CDP, but rather it’s a new testing and automation protocol entirely. “CDP is always going to be there on Chromium browsers,” he said.

It already has browser support 

CDP’s creator, Google, is heavily involved in developing and supporting WebDriver BiDi, as is Mozilla. “We are glad that Mozilla and Google have come and helped us get it to that point where it’s standardized and now everyone can benefit from it,” Burns said. He added that Apple isn’t quite there yet, and it’s not clear at the moment when support for WebDriver BiDi will be available in WebKit-based browsers. 

“Sometimes standards can move at a glacial pace, and part of that is for good reason. It involves creating the collaboration points and getting consensus — and sometimes consensus can be really hard, especially where Google, Mozilla, and Apple, they have their own ideas of what makes something better, and so getting that can be really, really slow to implement,” Burns explained. 

Testing automation tools and testing companies have also started supporting it

In addition to the browsers needing to support it, another piece of the puzzle is getting the testing automation tools and testing providers on board. Fortunately, the automation tools Selenium and WebDriverIO, as well as the testing companies BrowserStack, SauceLabs, and LambdaTest, are all part of the WebDriver BiDi Working Group. 

WebdriverIO and Selenium already have some support for the new protocol, and BrowserStack supports it too. Selenium itself is also updating its entire implementation from WebDriver to WebDriver BiDi. Burns explained that retrofitting the classic version of WebDriver to BiDi is the last major piece of the process, and is expected to be complete within the next year. 

“It’s a volunteer-driven project, so this happens when everyone’s bandwidth and time matches, so it gets done in like spurts or chugs of work, right? But I think that’s how it is for open source development in general,” said Jagani, who is also a member of the Selenium Technical Leadership Committee.

She noted that by Selenium 5 (the current version is 4.24), the goal is to have at least the high-level APIs done, which cover a number of use cases, like giving the user the ability to listen to console logs and the ability to do basic authentication for their website, to name a couple.

Once Selenium 5 is out, the next goal will be to start transitioning commands one by one from WebDriver Classic to WebDriver BiDi. “Hopefully, by Selenium 6, we are BiDi only,” she said. She did go on to explain that it’s a long process with many external variables. Browsers are still in the process of implementing it, and once BiDi is in the stable version of the browser, that’s when Selenium comes in and can start implementing it. After that, there’s still a period where users will need to use it and give feedback so that Selenium can ensure its implementation is resilient.

Jagani said that the user experience should remain the same once Selenium is switched over to BiDi, and there won’t be a big breaking change. 

“That’s what Selenium tries to do — even from Selenium 3 to 4 — we try to make sure it’s a seamless integration with minimal breaking changes,” she said. “Selenium is very big on backwards compatibility as much as possible, or at least ensuring that we’re deprecating things as required so you know we are going to be removing it and giving sufficient warnings. That experience for users using WebDriver Classic would remain the same, because eventually it’ll be the same APIs, just using BiDi under the hood.”

To take advantage of the new advanced capabilities that BiDi brings, there will be newer APIs available, which will be similar to the ones users are already familiar with. 

The post WebDriver BiDi offers the best of both worlds in browser automation tools appeared first on SD Times.

This is according to GitLab’s State of AI in Software Development report, which surveyed over 1,000 DevSecOps professionals in June 2023.  

Despite low adoption now, when you add in the number of teams planning to use AI, that number climbs to 90%. Forty-one percent say they plan to use AI in the next two years and 26% say they plan to use it but don’t know when. Only 9% said they weren’t using or planning to use AI. 

Of those respondents who are planning to use AI, at least a quarter of their DevSecOps team members do already have access to AI tools. 

Most of the respondents did agree that in order to adopt AI in their work, they’ll need further training. “A lack of the appropriate skill set to use AI or interpret AI output was a clear theme in the concerns identified by respondents. DevSecOps professionals want to grow and maintain their AI skills to stay ahead,” GitLab wrote in the report. 

The top resources for learning included books, articles, and online videos (49%), educational courses (49%), practicing with open-source projects (47%), and learning from peers and mentors (47%). 

According to GitLab, 65% of the respondents plan on hiring new talent to manage AI in the software development life cycle in order to address the lack of in-house skills. 

A majority of the respondents (83%) also agreed that implementing AI will be important in order to stay competitive.

For those 23% who are already using AI, 49% use it multiple times a day, 11% use it once a day, 22% use it several times a week, 7% use it once a week, 8% use it several times a month, and 1% use it just once a month. 

According to GitLab, developers only spend 25% of their time writing code and the rest of the time is spent on other tasks. This is an indication that code generation isn’t the only area where AI could potentially add value. 

Other use cases for AI that companies are investing in are forecasting productivity metrics, suggestions for who can review code changes, summaries of code changes or issue comments, automated test generation, and explanations of how a vulnerability could be exploited, among others. 

Currently, the most popular use case for AI in practice is using chatbots to ask questions in documentation (41% of respondents), automated test generation (41%), summarizing code changes (39%). While not doing it currently, 55% of respondents are interested in code generation and code suggestion, which ranked as the number one interest among developers. 

Many developers also worry about job security when thinking about the impact of AI. Fifty-seven percent of respondents fear AI will “replace their role within the next five years.”

Job replacement wasn’t the only worry; Forty-eight percent also worry that AI-generated code won’t be subject to the same copyright protections and 39% worry that this code may introduce security vulnerabilities. 

There are also concerns around privacy and intellectual property. Seventy-two percent worry that AI having access to private data could result in exposure of sensitive information, 48% worry about exposure of trade secrets, 48% worry about how it’s unclear where and how the data is stored, and 43% worry because it’s unclear how the data will be used. 

Ninety percent of the respondents said that they would have to evaluate the privacy features of an AI tool before buying into it. 

“Leveraging the experience of human team members alongside AI is the best — and perhaps only — way organizations can fully address the concerns around security and intellectual 

property that emerged repeatedly in our survey data. AI may be able to generate code more quickly than a human developer, but a human team member needs to verify that the AI-generated code is free of errors, security vulnerabilities, or copyright issues before it goes to  production. As AI comes to the forefront of software development, organizations should focus on optimizing this balance between driving efficiency with AI and ensuring integrity through human review,” GitLab concluded.

The post Report: Only 23% of development teams have implemented AI already appeared first on SD Times.

At face value, the numbers sound threatening. AI could replace 300 million jobs by 2030, according to Goldman Sachs. McKinsey estimates that “half of today’s work activities could be automated between 2030 and 2060,” including those in technology development.

But doomsday predictions about new technologies are nothing new — the introduction of steam power during the Industrial Revolution, assembly line production in the early 20th century, and even personal computers and the Internet in the late 20th century all sparked concerns about labor disruptions. Is this just history repeating itself, or are developers hastening their own demise?

AI is more of a friend than a foe

While each new wave of technological innovation has yielded new jobs that couldn’t have existed before, we often cannot foresee the new jobs that are created by technology. Innovation has often been met with fear and uncertainty — or as Joe Lonsdale said during the last AI hype cycle in 2017, “Neo-Luddite fears about technological unemployment are limited in the same way as our ancestors’ worldviews.”

Disruptive technologies tend to automate away routine, repeatable tasks. But through this process of ‘creative destruction,’ a new class of jobs to be done take their place. The same is true with AI.

While making certain jobs and skills obsolete, AI opens up new opportunities to use it to developers’ advantage. When we consider both sides, the big picture looks far less bleak, making a strong case for why — and how — developers should embrace AI.

Most developers are, in fact, already embracing AI. According to a recent GitHub report, as many as 92% of developers are already using or experimenting with AI tools. They are increasingly aware of how AI can relieve the drudgery of repetitive, manual tasks. A new stack of AI-powered development tools can write basic tests, autocomplete simple functions, and generate documentation. They can even bootstrap entire new projects, complete with boilerplate code and instructions to get everything up and running.

The impact so far has been both significant and quantifiable. Developers using GitHub Copilot write up to 46% of their code with it, by GitHub’s estimate. 

AI doesn’t merely write more code; perhaps one of the biggest unsung benefits to AI is opening the door to faster learning and new skills. In a 2023 Stack Overflow survey, when asked about the benefits of AI tools, about 25% of developers responded with “speed up learning.” Nearly a third of developers said they already use AI to better understand codebases. 

True to its name, Copilot (and similar tools) work alongside developers as they code, similar to pair programming. That allows developers to ‘chat’ with their code, making it easier for them to learn new tools, frameworks, and languages. AI pair programming lowers the entry barrier for junior engineers and opens the door to a new class of engineers.

A new class of engineers

What does the next version of the software developer look like? 

The next era of software development is one that will be defined by AI. Developers that spend time honing their AI skills will automate away repeatable tasks and set themselves free to work on more creative projects. 

Data from GitHub shows that developers get better at using GitHub Copilot over time. The acceptance rate of Copilot’s suggestions grows from 29 to 34% over the first six months of use. When these new AI skills are combined with ongoing improvements to the underlying models, they serve as a productivity multiplier. “As AI technology continues to advance, it is likely that these coding tools will have an even greater impact on developer performance and upskilling,” says Inbal Shani, Chief Product Officer at GitHub. 

As with any new developer tool, each has its own learning curve. Although developers can interact with them in plain English, they can also be manipulated with better, more intricate prompts. For instance, GitHub recommends adding high-level goals to prompts and allowing Copilot to generate code after each step, rather than asking all at once. Over time, developers can learn the types of problems they solve best and ways to guide them to more accurate solutions. 

New jobs like Prompt Engineer and AI Engineer tug at our imagination. It is too early to call these the roles of the future — there is much left to build. Yet, these small seeds of change reaffirm software developers’ excitement to continuously reinvent themselves and adapt to a changing world.

AI will almost certainly replace developer tasks over time as tools get better and faster. It seems unlikely, however, that engineers themselves will disappear; their ability to learn new skills is exactly what’s needed to push AI to its full potential. Our world needs these builders now more than ever.  

The post The dichotomy of AI for the software developer appeared first on SD Times.

In a blog post the company called its new AR/VR headset “a spatial computer that seamlessly blends digital content with the physical world while allowing users to stay present and connected to others.” 

Also announced was its corresponding operating system, visionOS, which supports the low-latency requirements of spatial computing. This OS is built upon years of engineering advancements in macOS, iOS, and iPadOS and its primary focus is on delivering immersive spatial experiences that leverage the surrounding space, presenting users with new possibilities in both professional and personal contexts.

One of the key features of visionOS is its new three-dimensional interface, which creates the illusion of digital content being physically present in the user’s environment. This interface dynamically adapts to natural light conditions and incorporates shadow effects, enhancing the user’s understanding of scale and distance within the virtual space.

iOS 17 gets updates to communications apps and improves AirDrop

Apple unveiled iOS 17, a significant update aimed at enhancing communication capabilities across iPhone, FaceTime, and Messages. This new release simplifies the process of sharing content through AirDrop and introduces improved input features to enhance typing speed and accuracy. 

Apple introduced new sharing functionalities for AirDrop. One of these features, called NameDrop, enables users to effortlessly share contact information by bringing their iPhones together or by bringing an iPhone and Apple Watch close to each other. This gesture not only facilitates contact sharing but also allows users to share content or initiate SharePlay, which enables them to listen to music, watch movies, or play games together when their iPhones are in close proximity.

Additionally, iOS 17 introduces two new experiences: Journal, an application that facilitates the practice of gratitude, and StandBy, a feature that provides easily accessible information when the iPhone is placed on charge.

Additional details on updates to iOS 17 are available here. 

macOS Sonoma comes with new customization, conferencing, and improved gameplay

The Mac experience has been enhanced with numerous improvements, providing users with additional options for personalization through widgets and captivating new screen savers. Notably, Safari and video conferencing have received significant updates, offering enhanced functionality and features. 

Furthermore, the gaming experience on Mac has been optimized, ensuring a smoother and more enjoyable gameplay experience.

On Mac, widgets have gained enhanced functionality and a more personalized touch. Users now have the ability to directly place widgets on the desktop and explore the widget gallery to discover their preferred ones. 

These widgets blend into the desktop wallpaper, allowing users to maintain their focus on the current task. Thanks to Continuity, users can also enjoy the wide range of iPhone widgets available on their Mac, expanding the ecosystem of possibilities. Moreover, widgets have become interactive, enabling users to accomplish tasks such as checking off reminders, controlling media playback, accessing home controls, and performing various actions directly from the desktop.

The introduction of Presenter Overlay introduces a new video effect that enhances a user’s presence during content sharing. This effect displays the user on top of the shared content, giving them greater visibility. Furthermore, the Reactions feature enables users to express their emotions during video calls by seamlessly incorporating visual elements like balloons, confetti, and hearts into the video. These reactions can even be triggered with a hand gesture, adding a fun and interactive aspect to the communication experience.

Additional details on macOS Sonoma are available here. 

WatchOS 10 and iPadOS 17 are announced

The new WatchOS offers redesigned apps, a new Smart Stack, additional watch faces, new cycling and hiking features, and tools to support mental health.

iPadOS 17 Features a redesigned Lock Screen and interactive widgets; intelligent new features in PDFs and Notes; updates to Messages, FaceTime, and Safari; and the all-new Health app. 

Apple also updated Apple TV 4k with insights that focus on mental health, new privacy and security features, FaceTime, and video conferencing, among new hardware announcements.

The post Apple Vision Pro and operating systems across the Apple ecosystem are announced at WWDC23 appeared first on SD Times.

The answer lies in a familiar tactic: automation. Development teams are increasingly adept at automating huge chunks of their work, from setting up the necessary infrastructure environments to building, integrating, testing, and releasing software. Call it DevOps or CI/CD, the tactic is the same: ruthlessly automate mundane or repetitive tasks. To ensure compliance requirements don’t hinder development, IT leaders must similarly prioritize automating data profiling and protection as a normal part of their development pipelines. 

The growing impact of data privacy on software development

The regulatory landscape for data privacy and protection continues to grow, resulting in ever-increasing, and increasingly complex, compliance requirements. In fact, McKinsey found three quarters of all countries have adopted data localization rules, which have “major implications for the IT footprints, data governance, and data architectures of companies, as well as their interactions with local regulators.” 

Existing data privacy regulations such GDPR in the EU and HIPAA in the US, updates to older mandates (e.g., the recently updated Federal Trade Commission (FTC) Safeguards Rule mandated by the Gramm-Leach-Bliley Act), and new and emerging laws (e.g., Virginia Consumer Data Protection Act, Canada’s Consumer Privacy Protection Act) all threaten to slow down software development and innovation by adding layers of security requirements onto the development process. 

Even without the introduction of new privacy mandates, the impact of data privacy and security requirements on development is almost certain to grow. For one thing, development and testing environments have proven to be rich attack targets for threat actors. From source code management systems to infrastructure such as virtual test servers to the test data itself, all are attractive targets for bad actors seeking to compromise systems and data. Add in the many different cloud development platforms like Salesforce and SAP, and it’s clear there is plenty of opportunity for a hungry hacker with nefarious intentions. 

Therefore teams must ensure the entire application lifecycle is secure, including development and test environments, whether on-prem or in the cloud. How do IT and security accomplish this without slowing development and release cycles? The answer lies in test data automation.

Test data management meets DevOps

The software development process is reliant on access to fresh test data. Traditional methods for managing and provisioning test data are typically manual and tremendously slow – think ticketing systems and siloed, request-fulfill models that can take days or even weeks. These processes are very much at odds with modern development methods such as DevOps and CI/CD, which demand fast, iterative release cycles. 

This is where application innovation often grinds to a halt. DevOps and DevSecOps processes have automated quality assurance testing and security and compliance testing throughout the CI/CD pipeline. But data provisioning and governance has remained a manual and time-consuming practice. Enter DevOps test data management (TDM) which automates the “last mile” of DevOps and provides fast delivery of lightweight, protected data in minutes instead of days, weeks or months. With DevOps TDM, organizations can accelerate development and testing, and in turn, can increase compliance and innovation.

Just how much can DevOps TDM accelerate software innovation? Consider one example from Dell Technologies. The technology giant’s developers needed quick access to fresh test data, but, like many other organizations, manually provisioning the data was a slow, tedious process. 

By automating DevOps test data management, Dell significantly increased the speed and efficiency of its test data provisioning and governance. Now, 92% of Dell’s ~160 global, non-production database environments are refreshed automatically on a bi-weekly basis. Developers can now initiate releases through their CI/CD pipelines in just 17 minutes. This has allowed the Dell team to run 6 million pipelines the first quarter of 2022, and more than 50 million since they implemented this standardized, automated approach. 

Shrink the surface area of private data

Antiquated approaches to test data management often rely on scripts or otherwise poorly integrated processes that result in the proliferation of sensitive data throughout the enterprise. It’s not uncommon for each development environment to have its own copy of sensitive production data for testing purposes. And often developers maintain their own copies for coding and unit testing. Many enterprises end up with hundreds or even thousands of uncontrolled copies of sensitive data.

Privacy mandates and security policies treat these copies of sensitive data no differently than the production databases from which they were spawned. Sensitive data such as personally identifiable information (PII) or cardholder data must be secured to the same degree, whether or not it’s in production. This often translates into requiring encryption both at rest and in transit, as well as carefully managed access controls and other protections. And then there are the near-universal requirements for the right to be forgotten. Privacy mandates regularly require businesses to destroy personal data upon request. It does not matter where that data lives.

The solution is eliminating the replication of sensitive data through the use of data masking. To provide production-quality data to your teams and non-production environments without multiplying the burden of security and privacy protections, DevOps TDM approaches — when implemented properly — automate the masking of sensitive data. In effect, this step shrinks the surface area that you must protect. This reduces your compliance and security risks as well as the impact on your budget.

Quite simply, having less sensitive data strewn about your business means less you to protect. Automation can make that possible.

Starting small but thinking big

Automating with DevOps TDM may appear overwhelming at first. Where do you start? But this is one change where it is very easy to start small, automating test data delivery and masking for just one or a handful of applications. Many businesses begin by addressing their most sensitive environments and where CI/CD pipelines already exist, such as customer-facing apps. Here, the need for protection and the underlying automation framework (i.e., the DevOps toolchains) already exist.

But businesses should also think big as they evaluate solutions. The number of distinct data sources is likely to expand over time. You might have a SQL database on AWS today, but then add your Salesforce platform and mainframe DB2 into the mix in the future. Masking these data sources while preserving referential integrity across them may prove challenging but is essential to effective integration and user acceptance testing.

Ultimately, businesses centralize DevOps TDM while giving their development teams autonomy over the acquisition and use of test data. Centralization means you can apply policies for the masking of sensitive fields and use database virtualization to cost-effectively provision data. 

The benefits of DevOps TDM are substantial. Not only do businesses improve compliance and mitigate risks, they also speed up development and reduce costs. It represents one of those rare instances where a tradeoff between faster, better (safer) and cheaper is no longer required.

The post Don’t let data compliance block software innovation; automation is the key appeared first on SD Times.

Developer experience refers to all aspects of interactions between developers and the tools, platforms, processes and people they work with, to develop and deliver software products and services. In order to create a superior developer experience, software engineering leaders must provide an environment in which developers can do their best work with minimal friction and maximum flow.

Software engineering leaders working towards improving their team’s developer experience should follow these three actions.

 Improve Developer Journeys

Developer experience extends beyond developer tools and technologies. Building and retaining a high-performance development team starts with a positive onboarding experience. A streamlined onboarding process enables developers to make meaningful contributions much faster, which in turn makes the entire team more productive.

Creating a frictionless developer onboarding experience will improve overall developer experience. For software engineering leaders, it is important to ensure that developers are equipped to get started on day one. Be sure to provide a fail-safe environment that is immune to accidental errors. Create a sense of belonging and camaraderie.

Developer self-service can also improve developer journeys by reducing process inefficiencies and, in some cases, eliminating unnecessary processes entirely. Self-service development workflows can be streamlined through the use of internal developer portals. Developers benefit from accelerated feedback loops, as they enable developers to continually improve code quality and understand what is working and what is not. By establishing feedback loops, developers are able to experiment, measure progress and continuously improve. The feedback helps improve their deliverables, as well as their ways of working. This shortens the time to value, thus providing quicker insight on value delivered from the user’s perspective. 

Optimize for Creative Work

To improve developer experience, software engineering leaders must go beyond optimizing development workflows and provide focus time for deep, creative work along with the freedom to fail and experiment.

A collaborative work environment is a crucial ingredient of developer experience, since software engineering is a team sport and involves multiple team members and teams. Teamwork and collaboration amplify original ideas and shorten the cycle time from idea to production. Collaboration between team members lends emergent properties to the team.

Fostering communities of practice can help create an open and collaborative work environment. Practitioner-led communities are fundamental to open, collaborative and effective learning. People own what they help create and work together to address challenges. Software engineering leaders must encourage their teams to create communities of practice through active engagement, regular activities, member focus, collaborative problem solving and a powerful strategic vision.

Be a connector manager who enables cross-pollination of ideas and skills. Connector Managers create a trusting and transparent team environment that supports peer-to-peer coaching. 

Finally, leverage automation for repetitive tasks to free up time for creative work. Automating away the routine and repetitive aspects of software engineering enables developers to focus on applying their creativity to solving problems. Developers should indulge in ideation, build new solutions, collaborate and communicate with their peers, partners and customers rather than maximizing the time available for writing code. 

Make a Meaningful Impact

Although most organizations focus on improving developer journey, developer experience in the long term goes beyond software development workflows. It involves giving developers the opportunity to make a meaningful impact. 

Foster a culture where developers don’t feel embarrassed to admit a mistake, ask a question, or offer a new idea. Psychological safety is the top predictor of high performance in software engineering teams. A number of organizations have reported that psychological safety  is a key characteristic of a high performing team.

The post 3 key actions to improve developer experience appeared first on SD Times.

These include: 

1. Workflow durations are between 5-10 minutes on average 
2. Recovery from any failed run is fixed or reverted in under an hour
3. Success rates are above 90% for the default branch of the application
4. They deploy as often as their business requires and at least 1+ times per day

Additionally, the report found that holiday season downtime brings increased risk to a business. According to the report, this can be combated by setting up a robust test suite to back up teams and enable innovation even when members are out of the office.

The report also showed that organizations that prioritized an optimal team structure had higher success levels. Meaning that making simple changes to day-to-day business practices in order to ensure that meetings don’t conflict with peak productivity hours can be highly beneficial to a business’ overall success.

It was also revealed that the size of a team is not nearly as important as the testing tools in place. Teams that prioritize test-driven development can rely on their tooling to perform well during market swings, seasonal fluctuations, and times of uncertainty, regardless of the size of the team itself. 

The report also showed that the three most elite open-source projects on CircleCI have pipelines between ten and eleven minutes. Achieving high performance and product maturity is made possible with code that is well tested in the cloud, meaning that pipelines will take longer to complete. 

“One of the most important ways to improve an organization’s application development efficiency is to systematically understand which behaviors are beneficial and which lead to inefficiencies,” said Stephen O’Grady, principal analyst, RedMonk. “By analyzing years of data across tens of thousands of organizations, CircleCI is doing just that.”

The full report can be found here. It examines two years of data from over a quarter billion different workflows and almost 50,000 organizations globally on the CircleCI platform.

The post Report: 4 critical benchmarks of successful development teams appeared first on SD Times.

Forge has been in beta since the beginning of 2020 and is designed to handle many of the maintenance aspects of app creation such as compliance, data management practices, scaling performance and security. 

“Forge is the culmination of over 2 years of work, during which we re-envisioned what modern cloud extensibility should look like in the next decade and beyond. We believe customers will increasingly ask for higher standards from app developers: everything from compliance and data management practices to scaling performance for tens of thousands of users,” Mike Tria, head of platform engineering, wrote in a post.

The solution is made up of three main components: a serverless Functions-as-a-Service (FaaS) hosted platform, a declarative UI language, and a DevOps toolchain, all of which serve three main pillars. 

The first pillar is that Forge allows developers to build Atlassian-wide applications that have all the power of the Atlassian platform including data residency for customers that want their data in a particular place, encryption, audit locking, and audit trails, high scale and performance and is built on Atlassian’s own cloud infrastructure. According to the company, more than 60% of Atlassian customers use at least one app or integration from the Marketplace to solve their specific needs.

“When you build an app for some for a third party, you’re hosting it yourself and you’re spinning up resources on AWS, Azure or GCP. You’re solving your own forms of compliance, you’re dealing with data residency and you’re dealing with running data,” Tria told SD Times. “In Forge, you write serverless code, and then Atlassian takes it from there, we host it and run it so you basically get the benefits of the platform.”

The second pillar is around security and enterprise, building enterprise capabilities such that every Forge app is an enterprise by default and can serve enterprise customers  in a more explicit way than Connect, Atlassian’s previous framework for extending Atlassian cloud products and an option for building apps on Jira Cloud since 2014.

Atlassian said that Connect will still have some use cases – especially in a transition to Forge that takes place over time. Atlassian will keep some elements of Connect (such as the ability to have remote storage instead of Forge-hosted storage), and gradually bring Connect and Forge together as part of a single cloud app development platform.

“In Connect, the way it worked prior is an application would just get access to really any data in your instance. With Forge, that’s really narrowed down so as a customer you can make a choice such as this app can just view issue data for this one API and things like that,” Tria said. “We want the Forge apps to be just as powerful, but we want more power in the hands of admins.”

For security, Forge uses OAuth 2.0 to support more granular scopes and makes sure that apps only access the data they need to perform a customer’s use case. 

Forge also lets developers keep customer data hosted in the Atlassian cloud, making it easier to comply with GDPR and other regulatory requirements and stated that it is working towards SOC2 certification for Forge, the company explained. 

The third pillar for the Forge platform is to enable developers to innovate faster.

“Getting an app up and running using Connect if you’re very good will take a few hours. With Forge it’s minutes,” Tria said. “Very much at the core of Forge is the speed of development. Again, there’s less heavy lifting. It’s all just done through a simple UI and a command line that we give to developers to run and deploy their stuff so there’s a DevOps toolchain that comes with Forge that Connect never had.”

Developers can use Forge to build apps that are publicly available for other people to download and install or they can charge for it on the Marketplace. If the company is an Atlassian customer, developers can build apps just for their own company that don’t get listed on the Marketplace as well. 

Recently, Atlassian also added distribution on the Atlassian Marketplace, and new apps like Link Management for Confluence, Visualize with AWS and Easy Subtask Templates have been listed. 

The post Atlassian releases new cloud app development platform: Forge appeared first on SD Times.

“The new Software Intelligence Hub extends the value we get from the Dynatrace platform to even more technologies and data sources. This enables more teams across our organization to benefit from precise insights and automated workflows and frees critical time for our developers to bring new innovations to market faster and with higher quality,” said Chris Deane, a senior engineering manager of platform services at BT Consumer.  

This includes wide application and infrastructure coverage, extensions that broaden the automatic and intelligent observability of Dynatrace across additional cloud cases, and open APIs and an SDK.

npm 7 released
Npm 7 includes a new feature that automatically installs peer dependencies, whereas in previous versions, peer dependencies conflicts presented a warning that versions were not compatible, but would still install dependencies without an error. 

Other updates in the new version include an increased velocity and tempo to a weekly release cadence, reduced dependencies by almost half, and increased coverage by 17%.

Npm 7 also includes changes to the new lockfile format, which is backwards compatible with npm 6 users. 

Additional details on the new release are available here.

Python accepts pattern matching PEP 634
The Python Steering Council announced that it chose to accept PEP 634, and its companions PEP 635 and 636, for Pattern Matching. 

The developers behind Python aim to have high-quality documentation available on the first release for Python 3.10 and its absence should be considered a release blocker. 

At the same time, the Python Steering Council, PEP 640 and 642 were rejected since 642’s proposed syntax “does not seem like the right way to solve the jagged edges in PEP 634’s syntax,” the council wrote in a post.

Developer week winners
The 2021 DEVIES winners were announced at the developer trade show that spanned over 30 different DevTech categories. 

Winners included Red Hat Integration for API infrastructure, Kong for API services, SmartBear for app analytics and testing, and many more. 

The full list of winners is available here.

The post SD Times news digest: Dynatrace Software Intelligence Hub, npm 7 released, and Python accepts pattern matching PEP 634 appeared first on SD Times.

Version 1.0 includes an improved traffic inspector that enables users to inspect requests and responses with different formats, including Headers, Text, Raw, JSON, and XML. 

Also, with Composer Collections, users can create a collection of requests to keep them all organized. Users can open multiple requests and execute them and share collections with team members. 

The improved Auto Responder UI makes it easier to create and apply rules while inspecting web traffic. The ruleset can then be exported and shared with a team. 

“Fiddler Everywhere is designed to keep your team in mind. You can now seamlessly save sessions, comment, and share them with your team without manually exporting them,” Progress wrote in a blog post.

Each saved session opens as a new tab in the Sessions view so that users can debug these sessions independently. 

Moving forward, the team behind the project said it will continue to focus on improving the user experience. 

Progress added that the Traffic Inspector features will always remain free, including unlimited sessions. Free users can also use the collaboration features within some prescribed limits.

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