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Category: Features

  • 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.

  • How To Keep Engineers Happy and Reduce Burnout

    How To Keep Engineers Happy and Reduce Burnout

    Software development can be a mentally taxing profession. It requires intense mental focus, often under heavy workloads, with unclear job expectations and a lack of control. And with recent labor shortages due to layoffs and resignations, teams may also be understaffed, making matters more complicated. These issues can compound and lead to prolonged excessive stress—that’s when burnout hits.

    In fact, one report found that 82% of developers surveyed indicated that they had experienced burnout in the last six to eight months. Some top causes of burnout include increased workload, a poor work culture and personal reasons. But whatever the cause, for 73% of these developers, burnout is negatively impacting their productivity or personal life. So, what can leaders do about it?

    I recently talked with Randall Degges, head of developer relations at developer security company Snyk, to learn how to address ever-present burnout issues in software development teams at scale. Snyk, which provides popular automated vulnerability detection tools, recently closed a staggering $196.5 million series G funding at a $7.4 billion valuation. With that kind of momentum, they must be doing something right. According to Degges, there are a handful of tactics to consider to prevent burnout, like ensuring developers are heard, not assigning tasks without context and making improvements to the DevOps workflow.

    Understanding Burnout

    Burnout is an ever-present issue, explained Degges. It’s common within any high-stress job where you’re on the hook for constantly delivering new features to support the business. Specifically, in software development, burnout can quickly arise when developers don’t feel they have a voice. Or worse, when they’re tirelessly producing software they know won’t ever see the light of day.

    A paper entitled Burnout in software engineering: A systematic mapping study took an interesting systematic approach and analyzed decades of research into burnout in software engineering. It found the following qualitative work-related factors to be most correlated with burnout. (Interestingly, the paper suggested machine learning might soon help produce more quantitative indicators of burnout.)

    causes of burnout diagram
    Causal-relationship of burnout, taken from ‘Burnout in software engineering: A systematic mapping study.’ (Deciphering this diagram gives me burnout!)

    7 Ways to Fight Burnout

    Since we know burnout is a pervasive problem, how can engineering leaders minimize the issue? Below, we’ll outline some specific strategies to consider and how they might help.

    1. Give Developers a Voice

    First, Degges encouraged leaders to ensure developers have a say in what they’re building. He recalled working within a large enterprise, sitting in a cubicle and having absolutely no say in the features his team was working on. This often included code they all knew would never make it into production.

    Slaving away on irrelevant work is never motivating. Instead, a good internal culture provides a comfortable atmosphere where developers can raise issues early on, said Degges, hopefully before leadership puts targets destined to fail on the roadmap.

    2. Provide More Context

    On that note, when decisions are made, don’t throw orders over the wall in a vacuum. Instead, Degges recommended providing more context into what you’re all creating. Giving visibility into the reason behind decisions will align everyone on project roadmaps and create a shared purpose. This helps foster a culture of empathy and listening to others, said Degges. (David Shackelford affectionally calls this attitude HugOps.)

    3. Optimize Communication Patterns

    Optimizing communication patterns for your working environment is also vital to encourage a healthy culture, said Degges. For example, this could equate to optimizing for the physical reality of distributed teams—no one should have to clock into daily standup at 4:00 a.m. If you are working under timezone constraints, Degges recommended adopting more asynchronous communication.

    For instance, his team relies heavily on Slack and provides dedicated channels for sharing ideas. They’ve also incorporated automation to summarize verbose Slack communications with a recap of conversations. In general, removing unnecessary meetings and optimizing communication patterns could greatly reduce the baggage an engineer might carry in their day-to-day work.

    4. Practice Internal Developer Relations

    As they say, practice what you preach. Many tech-driven companies have adopted a developer relations (DevRel) division to build a community with external developer consumers. But are you practicing DevRel internally? According to Degges, DevRel teams should act as a bridge between external and internal teams.

    “Our goal is to not only to think and empathize with customers but also to encourage internal people to be authentic community members themselves,” said Degges. This can equate to empowering internal employees with resources like mentoring, blog writing or speaking opportunities, he said.

    Additionally, other groups are emerging to help increase internal developer satisfaction. For example, platform engineering and developer productivity engineering (DPE) are being applied by many organizations to improve developer experience around internal tools and processes. Naturally, this will require working closely with internal developers to source feedback and make quality improvements.

    Another method to upkeep engineering morale is through automating away toil. And security vulnerability discovery is one area full of painstaking manual tasks. Half of applications have high-risk vulnerabilities, and new exploits in open source libraries are being discovered almost daily.

    As more software supply chain risks emerge, it’s challenging to keep up with the pace of vulnerability discovery and necessary software patching. Therefore, implementing real-time automated vulnerability detection, said Degges, can help remove many manual processes and free up energy to devote to other areas.

    6. Hire Based on the Work, Not the Interview

    Before developers even get the job, they can be put through high-stress on-the-spot tests during the interview process. Experiences here can set the tone for the entire engagement with the company. But activities like white-boarding algorithms are pretty disconnected from day-to-day work, said Degges. In his opinion, engineers should instead be hired based on the quality of their past performances and the work they’ve produced. This could be artifacts like their personal blog or contributions to open source projects.

    7. Apply Artificial Intelligence (AI)

    Lastly, what’s a tech blog post in 2023 without a mention of AI? Generative AI like ChatGPT has rocked the programming world and is being integrated into more development environments and engineering workflows. The future of AI looks very bright and is poised to help developers produce more with less time and resources.

    Of course, automated code generation will require sanity checks, but these tools can certainly help many ancillary functions. For example, Degges’ team is using ChatGPT to spin up content outlines and automate keyword research, helping support their educational content production.

    Soothing the Burnout

    Even with new tools emerging and a newfound emphasis on developer experience, burnout is still prevalent. A full 58% of developers and security professionals said they are currently experiencing burnout, according to a 2022 Cobalt study. This is, perhaps, due to the rapid release cadences of today’s software delivery.

    “In the past, it took months or years for new software updates to roll out. Now, developers only have a matter of weeks,” wrote Bill Mann, CEO of Styra, on Forbes. “Suffice it to say the stakes are high for today’s software teams.”

    Burnout is psychologically taxing and can negatively affect employee happiness and retention. Therefore, it’s an excellent idea for engineering leaders to take it seriously and consider what positive cultural changes they can make.

  • CodeSee Extends Reach of Code Visualization Platform

    CodeSee Extends Reach of Code Visualization Platform

    CodeSee updated its code visibility platform to enable instant mapping of application services, directories, file dependencies and code changes.

    CodeSee CEO Shanea Leven said CodeSee Enterprise 2.0 should extend the appeal of the platform beyond developers to include software engineers managing DevOps workflows.

    CodeSee Enterprise 2.0 provides real-time visibility across multiple repositories regardless of whether the application is a monolith or based on a microservices architecture, she noted.

    In addition, a Code Automations module enables developers to set granular conditions and actions down to the function level, requiring checklists to be complete before allowing developers to move on.

    CodeSee Maps now includes the ability to click to view code directly, while a streamlined Review Maps module has decreased code review time by 30%. Developers can easily share maps to discuss proposed changes or keep necessary code changes as automatic permanent artifacts.

    Additional languages including C#, VB.NET, ASP.NET, Blazor, Java, JavaScript, Go, Python and TypeScript are now supported.

    Finally, CodeSee is making available a dedicated solutions engineering team that provides guidance to connect an organization’s Datadog or OpenTelemetry trace data to CodeSee in less than an hour without storing code or having to first dive deep into traces, noted Leven.

    The core open source CodeSee tool was created because it takes too long today for most developers to pore over a codebase to understand where they might contribute. The larger a codebase becomes, the longer it takes for a developer or software engineer to be onboarded to a new project.

    The overall goal is to improve developer productivity, noted Leven. That’s become a critical issue because many organizations are hesitant to hire additional developers during uncertain economic times; they first want to ensure they maximize the utilization of the teams they already have.

    Application development, of course, is now a team sport. The days when a lone developer cared for an entire application are long over. The issue is finding a way for teams of developers to collaborate more easily in an era where many of them may never have met in person.

    In the longer term, Leven said she also expects that generative artificial intelligence (AI) platforms such as ChatGPT will supplement code visualization tools. Many developers are already starting to use these tools to help generate code faster. That code should, in theory, have fewer errors because it has essentially been copied from other previously validated sources. CodeSee provides a means to visualize that code before deploying it to ensure it will function as promised in increasingly complex application environments.

    One way or another, the tools being provided to developers to boost productivity are advancing. It’s not clear what metrics should be employed to track developer productivity, but the amount of code simultaneously moving through DevOps workflows is going to increase. The challenge DevOps teams face now is keeping track of all the dependencies that exist in that code as application environments are continuously updated.

  • Managing Engineering Teams During Economic Downturns

    Managing Engineering Teams During Economic Downturns

    The tech industry has faced strong economic headwinds over the past year or so. We’ve seen thousands of layoffs at public tech companies, along with weak stock performances and a decline in venture capital funding. Although some analysts see light at the end of the tunnel, the current slump doesn’t seem like it’s quite over yet. As such, some IT leaders are learning to cope with a slashed budget and a leaner engineering team.

    Simultaneously, engineers commonly desire more flexibility from their employers, both in terms of remote working opportunities and flexible hours. In fact, only 15% of developers are working 100% on-site, according to The State of Tech Hiring 2023 survey, which surveyed 14,000 professionals. In addition to being open to flexible working conditions, some organizations are examining how their internal tools could be improved to increase developer experience and productivity. Amid a downturn, leaders might need to consider these new strategies and many others to maintain steady progress.

    I recently met with Amanda Richardson, CEO of CoderPad, to discuss tips to lead your engineering team during this downturn. According to Richardson, finding the right blend of contractors, remote work opportunities, and helpful tooling is necessary amid the current climate. Leaders can also adopt many creative methods to encourage productivity and motivate developers. Below, we’ll overview four ways technical leaders can build and sustain their teams.

    1. Consider Hiring Contractors Vs. Full-Time Developers

    Recruiters often struggle to fill highly technical positions like full-stack developers, backend developers or DevOps professionals. If a company can’t fill full-time roles, contractors are another valid option. According to Richardson, contractors provide companies with more flexibility because they are easier to hire and cut. These mercenaries could swoop in to pick up the slack by solving common tedious problems, like Salesforce integrations or less-sexy administrative work, she said.

    Of course, contractors don’t solve every need, and they’re not bought into the company’s vision. That being said, more and more companies are leaning toward contractors to help fill technical gaps. In fact, 63% of recruiters now say their company hires contingent workers for tech needs, up from 42% last year, the report found.

    2. Target Remote Work

    Work-life balance has become a top requirement among today’s engineering workforce. For example, remote working options rank as the third-highest priority when looking for work, right behind salary and flexible working hours. Many engineers work better without the interruptions and noise of a traditional office space, explained Richardson.

    Yet, the desire to work remotely is at odds with the policies of some large tech companies, who are attempting to push developers back into the office. Interestingly, this may present an opportunity for small-to-medium enterprises to embrace flexibility and get a leg up on the competition. “Give the people what they want,” said Richardson. “If you want to retain talent, remote work is what you must do.”

    3. Use Tools to Fill in the Gaps

    Another method for filling technical gaps amid a downturn is improving efficiency with new tools. For example, platform engineering is a strategy that aims to develop reusable, self-service platforms to manage internal infrastructure. Centralizing common automation and builds could bring more consistency to deployments and reduce grunt work, freeing up engineers and making them more productive.

    Of course, we’ve also seen a lot of interest in new generative AI technologies, as well as development automation using low-code/no-code platforms. According to Richardson, leveraging AI and new automations presents a newfound opportunity to empower the workforce. For example, ChatGPT can solve interesting challenges in the prototyping phase, like generating a mock database or co-authoring support materials. Technical experimentation is a critical aspect of any team, and new AI capabilities could help bolster and support the operational sides.

    4. Retain a Strong Culture

    Lastly, maintaining a vibrant culture is another strategy to help retain talent. Richardson suggested internal hackathons and coding challenges to bring teammates together in an organic way. An internal hackathon could also be organized to rethink internal processes, providing an outlet for engineers to suggest innovative solutions. There’s also the basic stuff, like daily standups or brown bag lunches, that can create a space to share ideas. “It takes a good leader to figure out what’s going to engage their team,” said Richardson. (One business owner I interviewed actually condoned cooperative gaming during work hours to build community and has seen moderate success with this approach).

    Final Thoughts

    Of course, the above suggestions are easier said than done, and it will take a leader with a keen understanding of their unique engineering team’s attitudes and requirements to find their own version of success. On that note, talent comes in many shapes and sizes and traditional education isn’t everything when assembling a development team. In fact, a full 59% of developers do not have a university degree in computer science, the CoderPad report found, indicating that leaders may have to be more flexible when it comes to recruiting.

    In general, following the above tips could be helpful when managing technical talent. Enabling the company to hire contractors and remote workers increases your potential talent pool, and investing in new tools and company culture could help retain workers. Considering half of today’s developers are considering a job change in the next year, leaders should act now to avoid high turnover before it negatively impacts their team’s output and morale.

  • 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:

    Dice.com

    NerdRabbit
    New York, New York
    DevOps jobs
    $120,000 to $150,000

    LinkedIn

    Tata Consulting Services
    Sunnyvale, California
    AWS DevOps Engineer
    $105,000 to $130,000

    Monster.com

    VANET, Inc.
    Irvine, California
    Software Engineer
    $100,000 to $300,000

    SimplyHired

    Renaissance
    Remote
    DevOps Engineer 2
    $100,000 to $125,000

    Indeed.com

    GEICO
    Remote
    Senior DevOps Engineer
    $82,000 to $185,000