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Author: Rick Slade

  • Increasing Software Velocity While Maintaining Quality and Efficiency

    Increasing Software Velocity While Maintaining Quality and Efficiency

    At Compuware, we have been exploring the need for continuous improvement across three key dimensions of software development: velocity, quality and efficiency. Given today’s current market demands and the importance of digital services, this discussion couldn’t be timelier.

    On my previous piece, I focused on quality — how to maintain and improve it as (supposedly) competing demands for velocity and efficiency increase simultaneously. This article will focus on increasing velocity and how it is possible to do so without compromising quality or efficiency. As I discussed in my earlier piece on quality — automation, integration and measurement are once again the key.

    Compuware recently conducted a survey finding the overwhelming majority (97%) of application development managers said IT is under more pressure than ever to deliver software innovation faster. Higher demands for innovation are being imposed universally across platforms, from the cloud all the way back to the mainframe.

    Mainframe software delivery is a particular area of focus, with 95% of respondents noting their IT teams are under pressure to adopt modern development practices on the mainframe in order to accelerate overall software delivery and innovation. Adding to this pressure, organizations reported they expect to increase the frequency of new mainframe application feature deployments by an average of 41% over the next year, compared to 38% for non-mainframe applications. This shouldn’t come as a shock, given the proliferation of modern web-based applications placing unprecedented demands on back-end mainframes.

    Let’s take a closer look at how automation, integration and measurement are equally applicable to the goal of advancing velocity.

    Automation

    Throughout the software lifecycle there are many opportunities for automation — from software design, to development, to build, test, deploy and, ultimately, the production state. The more of these steps that can be automated, the faster developers can work.

    Perhaps the biggest area for potential time savings is testing, because of all the phases of the software lifecycle, testing assumes the most manual labor. The Vanson Bourne survey found test automation to be the single most important factor in accelerating innovation, according to 90% of IT leaders.

    On the mainframe specifically, development teams spend more than half their time on testing during the release of a new mainframe application, feature or functionality. If you can automate more testing, you can roll out applications faster while maintaining (or even enhancing) quality combined with an extremely high level of developer efficiency.

    Integration

    Integration helps software development technologies interoperate with each other. An integrated pipeline leverages existing investments and enables developers to build, test and deploy faster while providing the additional benefit of reducing errors resulting from human intervention (furthering quality). Integration also decreases the manual labor needed to execute and manage workflow within software delivery processes. When these technologies integrate out of the box, setup time is saved and productivity gains can be realized significantly faster. 

    For example, the integration of Compuware Strobe application performance solution with Jenkins, makes it easy for developers to take performance measurements early and automatically as part of a Jenkins CI/CD pipeline.

    Ongoing Measurement

    Measuring velocity is critical. Without measuring, it’s impossible to know how changes are impacting the software development process and what areas need to be improved. Measuring is the key to understanding if there are any activities that can be sped up across the phases of build, test and deploy. This enables development teams to find and address bottlenecks with a great level of granularity. Some common metrics for measurement include mean-time-to-build (MTTB), mean-time-to-delivery (MTTD) and mean-time-to-repair (MTTR).

    Conclusion

    Velocity does not need to exist at the expense of quality or efficiency — on the cloud, the mainframe or any other development platform. Like quality, the key to achieving and improving velocity lies in the disciplines of automation, integration and ongoing measurement. The next piece of this series will explore the keys to maximizing developer efficiency amidst near constant demands for exceptionally fast, high-performing rollouts.

  • Maintaining Exceptional Quality Despite Shrinking Delivery Deadlines

    Maintaining Exceptional Quality Despite Shrinking Delivery Deadlines

    My first article in this series focused on the need for continuous improvement across three key dimensions of software development–velocity, quality and efficiency. While these are all equally important, in this piece we’re going to focus more specifically on quality.

    The past few months—what you might call the summer of outages—have been troublesome for many of the internet’s biggest players. Google, Apple and Cloudflare, as well as Facebook, Twitter and LinkedIn, have all experienced bouts of unplanned downtime, sometimes even multiple bouts in quick succession. Is this all just a matter of coincidence and bad luck? Perhaps, but peeling back the layers reveals an increasingly painful reality of modern DevOps life.

    While the root causes of these outages were varied, several were blamed on software glitches. This suggests software rollouts may be happening before they are comprehensively battle-tested and ready for production-level systems. This is never a good idea, especially when you’re dealing with high-profile services supporting millions of users worldwide. In these cases, software-related stumbles are that much more damaging.

    When software is rolled out prematurely, a cardinal rule of development is broken in that we, the end-users, become the unwilling guinea pig for poor-quality applications. No matter the pace of rollouts, an unwavering focus on quality needs to be maintained, and automation, integration and measurement can be the keys to ensuring this.

    Automation

    Automation refers to a pre-programmed process that executes tasks with minimal human intervention. The more you automate, the higher your quality should be. Avoiding manual intervention decreases the likelihood of less optimal techniques or human mistakes, both of which can lead to poorer application quality and customer satisfaction. Essentially, automation enables the best techniques and practices to be quickly and easily replicated.

    Consider the example of automated unit and functional testing. If you can automate the execution of the best testing scripts incrementally across code units and functions, you can test more accurately, not to mention more quickly. The benefits will increase exponentially with the additional layers of testing you can automate. We recently worked with a customer who realized an overall ROI of 467% from their investment in automated unit testing.

    Integration

    Modern applications are increasingly componentized, spanning multiple platforms. Consider an e-commerce application, where the front-end may reside on a system of engagement (such as a web server) while the back-end transaction ultimately occurs on a system of record, such as a mainframe.

    High-quality, end-to-end digital services ultimately depend on all critical components being universally and automatically included and prioritized in DevOps pipelines. Similar to automation, this is because any time manual effort is introduced into the integration process, you increase the chances for error and consequently, the risk of a particular code segment lagging behind others.

    The end result? More defects making it into production in the sprint to the finish line. It’s like the famous I Love Lucy chocolate assembly line episode, which offers a humorous visual interpretation of the chaos that erupts once human intervention is introduced into a process.  Ideally, seamless toolchain integrations, across multiple application-leveraged platforms, can be utilized to maximize quality outputs leveraging DevOps technologies you’re already using, such as JIRA, Jenkins and SonarQube.

    Measurement

    Improving quality depends on development teams constantly improving their methods. This can be achieved through ongoing measurement against key performance indicators (KPIs).

    The challenge is establishing relevant KPIs, and we have seen many development teams fail at this. The most effective KPIs are those that translate to real business impact. For example, “number of bugs released into production” is a commonly used metric, but it does nothing to convey the bottom-line cost of those bugs. A more effective metric would be the cost of resolving defects, with success being declared when the organization reduces the amount of money spent on fixing bugs.

    The cost of fixing a bug is much less when it takes place earlier in the software development lifecycle (for example, in the coding and unit testing phases) than when the software is near or already in a production state. When a development team can consistently show it is reducing costs through earlier identification of bugs, this puts them in a stronger position to justify investments in better testing and analysis tools.

    Conclusion

    We’ve long known about the challenges DevOps teams face to balance velocity and efficiency with a high degree of software quality. It’s like asking a circus performer to sprint across a tight rope. It may seem unfair, but the need to release high quality software faster and faster, while staying within budget, is not going to abate any time soon.

    The recent spate of outages has taught us that even the biggest and the strongest in the industry are not immune to immense speed pressures, and the quality stumbles that often result. Automation, integration and measurement can help development teams in their ongoing quest to meet the seemingly conflicting requirements of improving quality, velocity and efficiency—at the same time.

    — Rick Slade

  • Focusing on the True DevOps North Star: Continuous Improvement

    Focusing on the True DevOps North Star: Continuous Improvement

    When we look beyond the fundamentals of tearing down silos and establishing greater collaboration, communication and transparency, DevOps is a label assigned to a framework of best practices and technologies that organizations leverage to deliver systems faster, more efficiently and without sacrificing quality. The best practice within that framework is continuous improvement, which provides stakeholders with data and feedback to constantly improve their ability to deliver products that advance strategic business objectives. This is done through key performance indicators (KPIs) that align with the goals of the business.

    Continuous improvement thrives on a robust program of KPIs that measure key attributes of a successful development team in the context of what is important to your organization. These KPIs provide you with the opportunity to continuously evaluate your system delivery processes and supporting technologies, and identify opportunities to make changes that produce better outcomes. In general, all organizations should be continuously improving their system development and delivery against three primary KPIs:

    • Quality: The value and performance of software you deliver to customers.
    • Velocity: The speed at which you develop and deliver software to customers.
    • Efficiency: The ease (low resource consumption, minimal latency) with which you develop and deliver software to customers.

    For now, let’s focus on what’s required to continuously measure and improve against them.

    Continuous Improvement KPIs vs. Metrics

    KPIs provide baselines for minimally acceptable performance levels across critical areas that impact the success of your business. Metrics measured against these KPIs help development teams improve what they’re doing to support the performance of the business in light of its goals.

    There are hundreds of metrics available (escaped versus trapped defects, features deployed and mean time to X, to name a few). The problem is most organizations focus too much on non-business-related metrics and data points and end up measuring the wrong things, such as how many lines of code have been written. This data may be interesting, but it tells you nothing of how well or within what time frame the code was written, or how that affects the delivery of value to customers.

    Your metrics should align with the goal-oriented KPIs of your business. For example, if a goal is to increase the quality of software after an influx of customer support issues related to defects, you should measure how many defects are escaping and how many are being trapped in testing. However, the remaining challenge for most organizations is actually capturing this metrics data to accurately measure progress and continuous improvement against, for example, a quality baseline.

    Collecting Data for Continuous Improvement

    A large reason organizations struggle to capture the right metrics data is they rely on outdated project management tools and manual effort. Developers and other team members painstakingly capture and contribute metrics data through status reports and meetings. Project managers then spend the bulk of their time analyzing and producing even more reports to provide the necessary feedback to support continuous improvement.

    In a digital economy where fast beats slow, this slow approach of manual data collection constrains and negates the business objectives of increasing quality, velocity and efficiency simultaneously. Core to Agile is the production of working software and minimizing the amount of work not directly related to that endeavor. If you want to move to an agile, iterative world, you want to minimize activity or any work that’s not directly related to the software delivery effort.

    That’s why, for true continuous improvement, you want your tools to automatically produce the right metrics data to measure and continuously improve upon KPIs, rather than relying on developers to attend status meetings and feed project managers information used to manually build reports.

    Once you’re able to visualize this data with a modern dashboard-driven analytics tool, you can use that information to constantly assess and evaluate system development and delivery productivity, and help developers make behavioral changes. You will have the necessary information to tweak processes and tooling and better educate team members.

    For example, if your software quality drops below its KPI-defined baseline and your metrics show increased efforts utilized to resolve break/fix issues, you can make a valuable correlation leading to smarter actions to improve quality, such as tweaking or changing testing methods and possibly using more effective testing tools.

    In this way, continuous improvement becomes embedded in your software delivery architecture, as you’re no longer making gut-based decisions that require you to wait for an outcome and guess again. Instead, you’re using data to drive strategic, fact-based shifts in behavior as you develop software, with the knowledge that these changes will have a real impact on the business.

    Making Continuous Improvement Across Platforms

    Ultimately, continuous improvement must reach across platforms in your organization—from the cloud to the mainframe. Development teams should work to continuously improve system delivery across all development platforms.

    The KPIs mentioned must apply to the entire development team and business, not just developers on one particular platform or skill set. You want to identify KPIs that are critical to the successful delivery of systems that support your customers, employees and business partners.

    Now that we have covered the importance of continuous improvement through the use of system delivery KPIs, stay tuned as we explore the individual topics of quality, velocity and efficiency at a deeper level in subsequent articles.

    — Rick Slade