devopsy.kinsta.cloud

Category: Features

  • GitKraken App Correlates Activity Across Jira Cloud and Git Repositories

    GitKraken App Correlates Activity Across Jira Cloud and Git Repositories

    GitKraken today made available a preview of a free application that aggregates activity across Atlassian’s Jira Cloud and any Git repository to provide a unified view across all the activities of a software development team.

    Adam Wride, general manager for planning solutions of GitKraken, said Team Insights for Jira (TIJ) will make it easier for DevOps teams, project managers and application stakeholders to collaborate.

    TIJ accomplishes that goal by providing an overlay using a familiar Gannt chart view of epics and issues found in project management applications to correlate, for example, user activity such as status changes and comments made in Jira Cloud with updates to commits and pull requests made in a Git repository, he noted.

    Team members can then filter those views by project, sprint, contributors, issue type and other criteria to drill down on the exact list of issues that need to be addressed, added Wride.

    Most application development efforts rarely follow the original DevOps workflow plan, so TIJ provides a means for organizations to track real-time activity data to visualize how their efforts are progressing against the plan. That makes it possible to make data-driven decisions about how to reallocate resources as needed, said Wride.

    At the core of TIJ is a Git Integration tool that GitKraken previously made available. TIJ is the second offering from the company based on that tool following the launch of an application that integrates Jira with continuous integration/continuous delivery (CI/CD) platforms. GitKraken claimed there are already more than 10,000 organizations employing its Git integration tool.

    As more organizations become dependent on software to drive a wide range of processes, the need for increased visibility into DevOps workflows has become more crucial. Revenue is now directly tied to the pace at which software is being deployed and updated. Any change to the pace at which an application is being developed needs to be communicated across all the stakeholders that might be impacted. The challenge is that given all the dependencies that exist within modern applications, it’s not always easy to discern how a delay in the development of one module or component might impact multiple application development projects.

    Of course, project managers have been using project management applications to bring some order to the chaos of software development for decades. The challenge has always been that manual updates to projects have not always been provided in a timely manner. Developers don’t always remember to update project managers, who must manually enter updates in a project management application. GitKraken is making a case for an approach that automatically updates Jira Cloud by collecting data from DevOps tools and platforms rather than waiting for software engineers to remember to share that information with a project manager.

    Arguably, there’s now no excuse for everyone in an organization—if they care to, they can find out the status of any application development initiative. The challenge is making sure the data that DevOps teams—and their tools and platforms—already generate seamlessly updates the project management software that everyone else relies on to keep track of those projects.

  • Pulumi Brings Advanced Analytics and AI to IaC Platform

    Pulumi Brings Advanced Analytics and AI to IaC Platform

    Pulumi today launched a platform that leverages analytics and artificial intelligence (AI) to generate infrastructure-as-code (IaC) from natural language prompts in addition to automating infrastructure management.

    Pulumi CEO Joe Duffy said Pulumi Insights will substantially improve the productivity of DevOps teams in addition to ultimately reducing the total cost of cloud computing.

    At the core of Pulumi Insights is an analytics engine automation application programming interface (API) that enables DevOps teams to launch queries using a search interface or natural language prompts across more than 100 cloud computing platforms. Results can then be organized to highlight the most-used clouds and resources broken down by project and environment using built-in dashboards. There’s also a REST API that makes it possible to export that data to a data warehouse such as Snowflake, Amazon Redshift, Google BigQuery and Azure Synapse.

    In addition, Pulumi has created a companion website and command line tool to make it possible to create code for provisioning infrastructure. That uses large language models like those at the core of generative AI platforms such as GitHub CoPilot or ChatGPT that promise to eliminate much of the toil associated with writing code.

    Pulumi has been making a case for an alternative approach to IaC tools, such as Terraform, that gives enterprise IT organizations more control over how IT environments are provisioned. Too many developers with limited cybersecurity expertise are provisioning infrastructure themselves, resulting in misconfigurations that cybercriminals can then easily exploit.

    The Pulumi approach also makes it simpler for IT teams to create platform engineering teams that can centrally apply guardrails that prevent those issues from arising without slowing down the rate at which applications are being built and deployed, noted Duffy.

    There’s also a much greater focus on improving the productivity of DevOps teams during uncertain economic times as organizations look to either limit or reduce the number of full-time employees they hire or retain.

    For one reason or another, DevOps teams are going to be required to find ways to become more productive as the number of applications being built and deployed continues to increase. AI clearly has a major role to play in enabling organizations to achieve that goal. However, before organizations can reliably benefit from those advancements, they will need to ensure the accuracy of the data those AI models use to surface recommendations and generate code. Otherwise, no matter how advanced these platforms are, the results will be suboptimal.

    In the meantime, IT organizations will need to revisit how IaC tools are being employed in an era where the focus on securing software supply chains has dramatically increased after some high-profile breaches. The challenge is that, in addition to all the applications currently being developed, there are thousands more that were deployed by developers without the same emphasis on cybersecurity.

  • Can ChatGPT Fix Bugs? ‘Wolverine’ Dev Says YES

    Can ChatGPT Fix Bugs? ‘Wolverine’ Dev Says YES

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

    This week: Instead of AI aids for programming, what about for debugging? The pseudonymous “BioBootloader” says he’s persuaded GPT-4 to make his code self heal. (more…)

  • Fiberplane Adds Open Source Libraries to Automatically Collect Metrics

    Fiberplane Adds Open Source Libraries to Automatically Collect Metrics

    Fiberplane today added an ability to automatically collect metrics in real-time and make them accessible either via its namesake notebook software for debugging infrastructure or an open source Grafana dashboard.

    Fiberplane CEO Micha Hernandez van Leuffen said Autometrics is an open source set of libraries the company created to make it simpler for developers to see platform performance metrics using a set of tools embedded within their integrated development environment (IDE).

    The goal is to reduce the cognitive load on developers; today, developers need to first determine what metrics to track, find a way to collect the appropriate data and then launch queries against those metrics to determine the root cause of an issue, said Hernandez van Leuffen.

    Autometrics, in contrast, provides access to a set of common queries using the Prometheus programming language (PromQL) against data that has been collected using open source OpenTelemetry agent software, he noted.

    That approach, for example, makes it simpler to track error rates, response time and latency of any function. Links to live charts based on real-time data can also be inserted into the comments section of any document a development team creates.

    The Fiberplane notebook uses the same operational transformation algorithm embedded within Google Docs to enable teams to easily collaborate around a document or spreadsheet. The core platform itself is built in Rust and WebAssembly (Wasm) to make it simpler to integrate Fiberplane as a plug-in into an existing DevOps workflow.

    Autometrics extends that capability in a way that enables DevOps teams to provide developers with instant access to observability data. They can then see how their code is performing on any given platform, which is critical as more responsibility for running applications continues to shift left, said Hernandez van Leuffen. Today, many developers need to wait for an IT operations team to share data collected via an observability platform that was designed to be used by ITOps rather than a developer, he added.

    As observability capabilities become more federated across the entire software development life cycle, it’s critical to make sure developers and IT operations teams can see metrics being generated in real-time. Otherwise, confusion arises because teams are not looking at the same set of metrics.

    Fiberplane address that issue by taking the concept of notebooks—often used by data science teams to collaborate—and applying it to DevOps workflows to reduce the amount of time it takes to resolve any issue. As IT environments become more distributed in the age of the cloud and microservices, troubleshooting issues invariably spans the responsibilities of multiple members of an IT team. In effect, a notebook makes it possible to spin up a virtual war room on demand that enables the members of an IT organization to collaborate—regardless of where they are physically located or which organization they may be employed by.

  • Harnessing the Power of AI Chatbots for Continuous Testing

    Harnessing the Power of AI Chatbots for Continuous Testing

    Artificial intelligence (AI) is revolutionizing the way we develop and test software, especially with the advent of AI chatbots. These intelligent agents have the potential to streamline and automate continuous testing while improving software quality. However, software and QA managers must understand the urgency, opportunities, pitfalls and best practices associated with integrating AI chatbots into continuous testing processes. This blog explains crucial aspects to help your team harness the full potential of AI-driven continuous testing.

    The Urgency of Integrating AI Chatbots Into Continuous Testing

    • Increasing complexity of software applications: Modern software applications are becoming increasingly complex, which makes manual testing time-consuming, error-prone and costly. AI chatbots can analyze vast amounts of data in real-time, identify patterns and learn from the testing process, making them invaluable for continuous testing.
    • Fast-paced development and market demands: Today’s competitive market demands faster release cycles and shorter time-to-market. AI chatbots can help you accelerate testing and development cycles, allowing your team to focus on delivering high-quality software promptly.
    • The growing importance of customer experience: AI chatbots can simulate user interactions and predict user behavior, helping to improve user experiences by identifying potential issues before they become real problems.

    Opportunities for AI Chatbots in Continuous Testing

    • Improved test coverage and accuracy: AI chatbots can execute a large number of tests simultaneously, leading to better test coverage and increased accuracy.
    • Enhanced defect detection: AI chatbots can identify defects early in the development process, reducing the overall cost and time spent fixing them.
    • Efficient resource allocation: By automating repetitive tasks and reducing human intervention, AI chatbots enable teams to allocate resources more effectively, leading to increased productivity.

    Pitfalls to Avoid When Implementing AI Chatbots

    • Over-reliance on AI: AI chatbots are powerful tools, but they should not replace human testers completely. Human intuition, creativity and critical thinking are essential for effective testing.
    • Insufficient training data: To work effectively, AI chatbots require quality training data. Ensure that your chatbot is trained with diverse and representative datasets to minimize the risk of biased or inaccurate results.
    • Integration challenges: Integrating AI chatbots with existing systems and processes can be challenging. Ensure that your team has a clear understanding of the integration requirements and potential issues that may arise.

    Best Practices for Applying AI Chatbots to Continuous Testing

    • Define clear objectives: Establish clear goals and expectations for your AI chatbot to ensure that it aligns with your team’s overall testing strategy.
    • Choose the right AI chatbot platform: Consider factors like cost, scalability, ease of integration and support for programming languages when selecting an AI chatbot platform.
    • Continuously monitor and refine: Continuously monitor your AI chatbot’s performance and make necessary adjustments to improve its effectiveness and efficiency.
    • Foster collaboration: Encourage collaboration between human testers and AI chatbots to leverage the strengths of both.

    Benefits of Applying AI Chatbots to Continuous Testing

    • Improved test coverage and accuracy: AI chatbots can execute a large number of tests simultaneously, leading to better test coverage and increased accuracy. By automating repetitive and complex tasks, AI chatbots can effectively handle edge cases and efficiently test various scenarios, thereby reducing the likelihood of undetected issues in the software.
    • Enhanced defect detection and early issue identification: AI chatbots can quickly identify defects and potential issues early in the development process, reducing the overall cost and time spent on fixing them. By leveraging machine learning and pattern recognition, AI chatbots can predict potential problems and bring them to the attention of developers, allowing for proactive resolution and improved software quality.
    • Efficient resource allocation and increased productivity: By automating repetitive tasks and reducing human intervention, AI chatbots enable teams to allocate resources more effectively. Human testers can focus on higher-level tasks that require creativity, critical thinking, and domain expertise. This leads to increased productivity, as the team can concentrate on delivering high-quality software and improving user experiences.

    Conclusion

    AI chatbots are transforming the landscape of continuous testing, offering unprecedented opportunities to improve software quality and speed up development cycles. By understanding the urgency, opportunities, pitfalls and best practices associated with integrating AI chatbots into continuous testing processes, software managers can effectively harness the potential of these intelligent agents and drive their teams toward success.

  • Five Great DevOps Job Opportunities

    Five Great DevOps Job Opportunities

    DevOps.com is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted to better serve our audience.

    Our goal in these challenging economic times is to make it easier for DevOps professionals to advance their careers.

    Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role it tends to create an opportunity for others.

    The five job postings shared this week are selected based on the company looking to hire, the vertical industry segment and, naturally, the pay scale being offered.

    We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration:

    Monster.com

    SnapLogic
    San Mateo, California
    Infrastructure Engineer
    $183,310

    LinkedIn

    Metropolitan Transit Authority – NYC
    New York, New York
    Cybersecurity Officer – DevSecOp/Cloud Infrastructure
    $144,500 to $191,000

    Dice.com

    Flexion
    Remote
    Azure DevOps Engineer
    $140,000 to $170,000

    Indeed.com

    Woven Teams
    Remote
    DevOps Engineer
    $116,000 to $147,000

    SimplyHired.com

    Raytheon Missile and Defense
    Schriever Air Force Base, Colorado
    Senior Software Engineer
    $75,000 to $161,000

  • Android Apps Must Let Users Delete Data ¦ RISC-V in the Data Center

    Android Apps Must Let Users Delete Data ¦ RISC-V in the Data Center

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

    This week: Google forces apps to make deleting users’ data easier, and the RISC-V drumbeat grows louder. (more…)

  • CircleCI Report Surfaces DevOps Productivity Gains

    An analysis of more than 14 million workflows from thousands of organizations conducted by CircleCI, a provider of a continuous integration/continuous delivery (CI/CD) platform, revealed the mean-time-to-recovery (MTTR) decreased by about 13% year-over-year.

    Half of CI workflows (50%) recovered from a failed run in 64 minutes or less. That compares to 73 minutes or more in previous years, the report noted.

    In general, the report found 50% of workflows completed in 3.3 minutes or less. However, the average duration was approximately 11 minutes. The median workflow ran 1.54 times per day.

    Success rates, as defined by the number of passing runs divided by the total number of runs, was, on average, 77% for default branches and 67%, on average, for non-default branches.

    Naturally, the level of productivity any given DevOps team can achieve is impacted by everything from the size of the team to the vertical industry they operate in. CircleCI CTO Rob Zuber also noted there are other crucial developer productivity metrics, such as the amount of time developers are focused on writing code, that need to be tracked as well.

    The goal for any DevOps team is to abstract away as much complexity as possible to enable developers to spend more time on writing business logic, said Zuber. Unfortunately, as more responsibility for infrastructure and security has shifted left toward developers, many of them are spending too much time managing application environments instead of writing code, noted Zuber. He noted that the cognitive load being placed on developers today is too high.

    Developing applications is, of course, as much an art as it is a science. Organizations can set aside time for sprints to write code, but it’s difficult to dictate when inspiration might strike a particular developer. There is always going to be a need to let developers, for example, write code at odd hours of the day and night. The more friction they encounter in terms of spinning up application environments, the less likely it becomes a developer will spend time squarely focused on writing code, said Zuber. The “zone” is when most of the best code is developed, he added.

    Naturally, there is no set of metrics that are equally applicable to all DevOps teams, but they should pay attention to metrics as an indicator of whether teams are improving, added Zuber. Most developers and software engineers are not going to want to work in a “software factory” where every minute of the day needs to be accounted for within a project management application. Rather than employing metrics as a stick, they should instead be used as an educational tool—a carrot—that drives steady improvement. In addition, organizations should take care to make sure the collecting of metrics doesn’t impede the pace at which applications are being developed.

    As the saying goes, things measured are usually things done; but there is a fine line to be observed when it comes to any goal that requires a certain level of creativity to achieve.

  • Synopsys Preps Extensions to Polaris SaaS Platform

    Synopsys Preps Extensions to Polaris SaaS Platform

    Synopsys plans to extend the capabilities of its Polaris Software Integrity Platform for securing application development environments by adding dynamic application security testing (DAST) tools along with the ability to scan code used to provision infrastructure.

    As a software-as-a-service (SaaS) platform, the Polaris Software Integrity Platform was created by combining the static application security testing (SAST) tool Synopsys gained with the acquisition of Coverity in 2014 and the software composition analysis (SCA) tool it added to its portfolio by acquiring Black Duck Software in 2017.

    In 2015, Synopsys acquired the Seeker tool for analyzing the code created using interactive application security testing (IAST) tools from Quotium. The company last year acquired WhiteHat Security from NTT to add a DAST tool.

    Patrick Carey, senior director for marketing strategy for Synopsys, said that the company would add the capabilities of those latter two platforms to create a comprehensive set of best-in-class security tools that are easily accessible via a SaaS platform.

    At a time when more organizations than ever are moving to secure software supply chains, Carey said it’s critical to provide developers with more visibility into vulnerabilities across the entire software development life cycle (SDLC). Some organizations are opting to achieve that goal by embedding tools within the SDLC themselves, but many organizations would likely prefer to invoke a SaaS platform that they don’t have to maintain, noted Carey.

    He noted that a SaaS platform makes it simpler to point a collection of integrated tools at a code repository to surface those vulnerabilities.

    As DevSecOps workflows continue to evolve and mature, more responsibility for application security is shifted left toward developers. The challenge is that many developers have limited cybersecurity expertise, so it’s critical that they have access to high-quality security tools.

    In addition, a SaaS platform helps reduce the total cost of achieving that goal by providing multiple capabilities within a single integrated platform, said Carey.

    Naturally, most of the focus today is on reducing the number of vulnerabilities that find their way into production environments as applications are being built. However, there are already large numbers of unaddressed vulnerabilities in applications that have already been deployed. Organizations will need to find ways to address that technical debt in addition to providing developers with tools that help prevent those code vulnerabilities in the first place.

    As such, there is no one central place through which application security can be managed, noted Carey. Instead, application security needs to be addressed across every phase of the SDLC, he added.

    It’s not clear how quickly organizations are adopting DevSecOps best practices, but as calls for increased liability for software development increase—as outlined, for example, in the U.S. National Cybersecurity Strategy— organizations should expect DevSecOps workflows to become a requirement. Otherwise, organizations will be severely penalized for vulnerabilities that find their way into code. In effect, there is now a race to embrace DevSecOps before any proposed liability legislation becomes the law of the land.

  • Orgs Shift Toward Centralizing Automation Initiatives

    Orgs Shift Toward Centralizing Automation Initiatives

    A survey published today found that 75% of respondents recognized that automation silos hamper their ability to unify cloud operations across functions.

    Conducted by CloudBolt Software, the survey polled 350 senior IT and DevOps leaders at organizations with more than 3,000 employees. On a positive note, the survey found 57% of respondents were starting to pursue a more centralized approach to automation, with a total of 88% acknowledging that having all previous and new automation centralized for the entire organization would ensure compliance, bolster security, enable business continuity, encourage reuse and reduce risk.

    Rather than taking a more holistic approach, well over half of respondents (57%) reported automation efforts are limited to specific silos. On average, organizations are employing 27 different automation tools their organizations have deployed, using one or a few as they see fit, with little to no consistency across teams. More than half (55%) of respondents said they have four or more separate automation teams, with 95% noting that each of those teams operates in their own silo.

    CloudBolt CTO Rick Kilcoyne said that lack of continuity clearly creates inefficiencies and redundancies that conspire to raise the total cost of IT. The challenge is that each team tends to favor one approach to automating a silo with little to no regard for a larger overall strategy to create an automation standard, he said.

    That approach also creates issues whenever a member of one of those teams leaves an organization, he noted. In fact, the survey found 77% of respondents reported that continuity is at risk any time a vital member of the team leaves.

    CloudBolt has been making a case for a federated automation platform that can be invoked either via application programming interfaces (APIs) or a graphical user interface (GUI) that provides access to low-code tools. The goal is to make the platform equally accessible to DevOps teams or IT administrators that tend to lack programming skills, and do so in a way that doesn’t require individual teams to replace their automation platform of choice. Instead, each of the automation platforms can be programmatically invoked via a central framework, said Kilcoyne. That approach eliminates any political debate over the potential need to replace one team’s favored automation platform with another, noted Kilcoyne.

    Regardless of who is accessing the platform, most organizations would be well-advised to develop a center of excellence for automation, he added.

    DevOps teams have always been committed to ruthlessly automating as many IT processes as possible, but in many cases, that has resulted in a plethora of automation tools being employed. In effect, automation may have become too much of a good thing. One approach to regaining control over those platforms is to create a platform engineering team that centralizes the management of DevOps workflows.

    Regardless of approach, Kilcoyne said the most important thing for any team to do before automating an IT workflow is to first understand how to manually set it up. Too many IT teams are trying to apply automation to processes they don’t really understand with mixed results, he added.

    The one thing that is certain, especially with the rise of artificial intelligence (AI), is the management of IT is going to become more automated.