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Category: Continuous Delivery

Continuous delivery is a software engineering discipline that allows organizations to consistently develop and release quality software at a rapid pace. It is achieved by automating the software development lifecycle, enabling tasks such as development, testing and release to occur concurrently and continuously.

  • DevOps Flow: Accelerating Velocity With Software Factory Best Practices

    DevOps Flow: Accelerating Velocity With Software Factory Best Practices

    In the cloud era, senior executives are investing in key capabilities like DevOps with the expectation this will accelerate innovation and bring new digital products to market faster. To deliver on these goals, enterprise organizations seek to deploy high-quality code faster and more frequently, but they face a organizational, process and technology complexity that can hinder this objective, slowing throughput and incurring high error rates. Often when DevOps is discussed, the focus is concentrated on the technology layer, particularly the latest tools like Kubernetes, and also on the productivity of each individual developer.

    However, for many organizations, the main challenges will be with team practices and organizational models.

    Writing for DevOps.com, Mike Vizard highlighted the reality of DevOps uptake; that most adoption issues are related to organizational challenges, not technology. The top two barriers to adoption are slow processes and speed of adaptation (29%) followed by budget and funding (21%). Only 18% identified technology limitations as an issue.

    As Mike writes, it is the broader organizational transformation that is more challenging. ‘Addressing bottlenecks’ might be a function of confronting the situation of a department manager who zealously guards their turf and acts politically to resist any changes required.

    Implementing DevOps for one team is a relatively straightforward exercise, but for enterprise organizations, they face a much larger magnitude of complexity, as they typically have multiple teams spanning multiple geographies and suppliers with hundreds of developers all contributing to the same software development process.

    Therefore, a DevOps methodology that also addresses the organizational level is required, one that maps processes end-to-end and identifies the improvements needed to speed production across this entire life cycle.

    Software Factories

    Transformation leaders are turning to a ‘digital factory‘ approach to streamline this complexity into a production line model that yields consistently high throughput and quality using Lean principles derived from the manufacturing world.

    McKinsey explored the dynamics and successes of the digital factory model, a new approach to organizing digital teams. This model involves a self-organizing structure rather than rigid departmental hierarchies, and they described the KPI improvements the digital factory enables: “We see reductions in management overhead of 50% for technology teams in the digital factory, 70% in the number of business analysts needed to write technology requirements, and, as test automation becomes the norm, a drop of 90% in the number of testers. Finally, we see top engineering talent performing at eight times the level of their peers, as measured with metrics such as code commits.”

    In short, McKinsey described a development team that improves quality and finds efficiencies as a direct by-product of one that leverages the latest tools and team methods to produce better digital products faster. The concept of a ‘software factory’ is a very powerful metaphor because much of the insights are drawn literally from the world of manufacturing, where over many decades they have perfected the science of optimizing the efficiency of a production line.

    To close the loop and map these development improvements to business value, management can leverage organizational performance practices, notably Six Sigma and the theory of constraints, to define their ‘DevOps algorithms,’ a systematic formula for quantifying and improving the rate of software development throughput.

    DevOps Flow

    DevOps Flow is a methodology for implementing high-performance, cloud-centric software development based on science developed by pioneers like Toyota to optimize factory lines and apply this to software engineering.

    Organizations need the ability to measure the entire system end-to-end to understand how value flows and where it is constrained and, most importantly, to correlate those metrics with desired business outcomes. This approach allows for continuous optimization in the pursuit of delivering greater and greater value to the organization faster.

    It can be applied to speed the deployment of new software releases, notably identifying and removing constraints, reducing batch sizes and eliminating waste. These are all steps taken within manufacturing to increase production throughput and are improvements that software teams can adopt to achieve equivalent benefits.

  • The Impact of Developer Happiness on Productivity

    The Impact of Developer Happiness on Productivity

    Even in an era of unprecedented tech layoffs, most companies are still eager to find and retain top software developer talent. What’s more, many firms are discovering that they lack direct insight into their development teams’ needs and priorities. As a result, some of them are struggling to keep their developers happy and thus productive. A recent Zenhub report found, for the first time, a quantitative link between developer happiness and productivity, including what keeps developers in the same position instead of moving on to another job at the first sign of perceived trouble.

    Software Development Happiness

    The 2022 Software Developer Happiness report is based on a survey of 380 software professionals from a host of different organization types. In brief, the survey found that two of the biggest contributors to devs being unhappy in their roles were a lack of work/life balance and not working on what’s perceived as “quality work.”

    Even with a looming recession and thousands of layoffs, there remains a dearth of senior software development talent. For more junior talent, there will be more available than in previous, pre-pandemic years. As companies strive to do more with less, they still require development teams to build their products and/or the applications that drive their business. Setting up more junior teams for success via collaboration and productivity tools will be key to navigating this new environment.

    Given these findings, if organizations want to retain developer talent, they still need to make sure developers are working on meaningful projects and that they’re able to achieve that work in a way that balances the needs of the business with the needs of the individual.

    An ideal example of this phenomenon is the developer tool ecosystem companies make available to their teams. While the growth of this ecosystem has been a net positive for developers, it has also created a lot of tool sprawl and significant integration challenges.

    Less Time on Application Development

    Stephen O’Grady, an analyst at RedMonk, an analyst firm focused explicitly on software developers, talked a lot about this through a term he coined called the “developer experience gap.” He is referring to developers spending more time integrating tools than working on application development or application code. That integration work comes at a real cost. It means developers are spending less time on application development, are having to work longer hours or do the integration work off the side of the desk.

    The real bottom line to these survey results is that developers are happiest and stay at their jobs longer when empowered to focus on building code. This means spending less time interacting with management, attending meetings or resolving and integrating multiple solutions. The report’s results also emphasized the importance of work-life balance in encouraging developers to stay at their current job longer, while poor work-life balance accelerated the hunt for another job. These insights into developer retention are especially critical as, according to the report, 71% of developers haven’t decided how long they’ll stay with their current employer. The report also suggested that 33% of developers have stayed in a job longer than usual due to the pandemic, which could mean an increase in developer turnover when the pandemic comes to an end.

    Another key finding is that collaboration challenges are viewed as the biggest impediment to developer productivity. Delays in feedback result in lost productivity; nearly 70% lose a minimum of three hours per week per developer and 75% spend at least an hour per week on reporting instead of writing code.

    Finally, developers said their biggest priority is ensuring their software meets business demands, and most developers want high-performance productivity tools. In fact, 93% of developers believed these tools are critical to their success.

    Increase Developer Happiness

    So what can team leads to alleviate these issues? For both team leads and more senior management, the biggest way to increase developer happiness is simply to let them focus on coding. Developers like to showcase their skills, so encouraging them to work on interesting and impactful projects that challenge their skill sets is a great way to boost morale. Ensuring an appropriate work-life balance and paying the developers competitively are the other two recommendations for improving developer retention.

    Additionally, senior management should take another look at how they are compensating their teams. Financial compensation will always be a critical element, but we should expect to see companies shift their focus to programs and benefits that their employees truly need. Particularly given the somewhat chaotic year in 2022 and continuing in 2023, those in the technology industry will shift to valuing organizational stability over just a paycheck. An improved focus on mental health, conflict resolution and work/life balance will also be a big part of attracting the appropriate talent to an organization.

    The full report provides further detailed analyst recommendations for project managers, product owners, team leads and executives. The entire report can be downloaded for free here.

  • How to Achieve Data Mesh With Four Key Principles

    How to Achieve Data Mesh With Four Key Principles

    Today’s world is becoming increasingly data-driven, which means businesses need to refine their practices and decision-making processes with the use of trustworthy data. However, this has proven challenging as organizations often don’t know what to do with the abundance of data available to them. According to a recent survey from Snow Software, 89% of IT leaders receive actionable data, yet 60% feel overwhelmed by the volume at their fingertips. These companies are both thriving in data collection and are bogged down by the sheer quantity available. While traditional data strategies have not yet adapted to meet modern business needs as the amount of data continues to grow, there’s a strong opportunity for companies to transform their data warehouses and data lakes and optimize the flow of data to consumers through an emergent approach called data mesh. Data mesh forges a path for companies to improve their business strategy and make better, faster decisions by leveraging the insights buried within their data.

    Data Mesh and its Benefits

    At its core, data mesh is an approach to data management that enables organizations to evolve from the pitfalls of legacy, centralized data architecture toward a decentralized, domain-driven design at scale. Breaking that down, data mesh is designed to help companies manage complex data networks by focusing on decentralized data ownership, treating data as a product, and providing a uniform infrastructure capable of sharing data across the company landscape. It improves upon legacy data management, which is often time-consuming, error-prone, unsustainable and unscalable. Unlike these traditional methods, data mesh gives domain-driven ownership to experts who strategize how it can best serve the company, as opposed to just dumping all data in one place and waiting for a centralized function to process it and make it available to the business.

    A data mesh approach also enables organizations to democratize their data by allowing users to acquire trusted data when they need it, all while automating the complexity and being shielded by security and governance guardrails. It removes bottlenecks and helps businesses glean the data-driven insights they need at the scale and speed of their organization. This way, organizations can maximize the value of their data by ensuring it is clean, vetted and readily available to support business initiatives.

    The Four Principles of Implementation

    So how can organizations embrace this approach? The framework of data mesh is centered on four guiding principles to help organizations get the most out of their data. Leveraging these principles will help your organization unlock the benefits of a data mesh.

    ● Domain-driven ownership: As opposed to separating data from your organization’s subject matter experts, the first pillar of data mesh implementation involves putting domain experts in charge of their data ecosystem. This way, data will be cleaned and enriched at the source before it’s made available throughout the organization. Domain owners will maintain the quality of the data and keep all necessary facts and documentation available for audits, effectively combining data with business talent.
    ● Data as a product: The job of the data team is to provide the data that the company needs for whatever purpose, be it making decisions, building personalized products, or detecting fraud. By treating data as a product, the company is giving its data a vision, strategy and product roadmap, ensuring its value is executable.
    ● Self-service data infrastructure: A business is made up of logically autonomous domains; each domain not only supports a business function, product or process but also produces data products and therefore requires a data infrastructure. Self-service infrastructure ensures business domains don’t have to deal with the underlying complexity of networking, security, compute and storage requirements.
    ● Federated computational governance: Standards for data governance are defined centrally, but local domain teams are empowered and equipped to apply to these standards however is most suitable for their specific environment within predefined guardrails. The key to achieving this for policy, security, quality and definition at scale is automation and integration throughout the landscape of the data infrastructure.

    By enacting these principles within your organization, your data mesh architecture is sure to provide the most value for your data.

    Data mesh is an optimal solution for any business currently struggling to find value in its data. Traditional methods are time-consuming, unsustainable, unscalable and error-prone. Data mesh is an approach designed to transcend these barriers, decentralizing much of the IT team’s heavy lifting and putting the responsibility of managing data on individual business domains. With clear ownership, domain experts can develop deep data knowledge and expertise and ensure that it’s trustworthy and valuable to your organization before making it available for consumption. This way, enterprises can prioritize self-service and empower data consumers with access to accurate, high-quality data when needed.

  • Why You Need to Break the Observability Data Silo

    Why You Need to Break the Observability Data Silo

    In my role as a developer advocate for Coralogix, I speak with organizations across almost every conceivable industry, and there is a pattern across almost all of them. In the world of observability, the challenge is battling scale. At scale, observability becomes a complex problem to solve. Surprisingly, the most common pattern for handling observability data is also holding people back from unlocking the true power of their data.

    What is the Data Silo in the World of Observability?

    To understand the data silo, we first need to understand the typical architecture of an observability solution, whether it’s homegrown or SaaS. Observability data can be divided into three types: Logs, metrics and traces. These three types come together to give engineers a complete picture of the health and performance of their system.

    These three data types are typically held in different storage mechanisms. A popular solution for logs is open search, while Prometheus holds the mindshare for metrics, and Jaeger is a very popular tracing solution. This means three solutions, three interfaces and, subsequently, three problem-solving workflows.

    What’s wrong with separating data like this? Even though there are three types of data, there is still only one goal: To understand the health and performance of the system. This is the only use case. The data silo is the dominant trend for observability solutions to provide a fragmented view of the data. It requires engineers to piece together stories as they go, rather than correlating across all of this data at once, to provide a complete picture of system performance.

    This slows down troubleshooting. If there is an issue, an engineer will first review one of the data types. Then, they will run different queries against other data types to confirm. So they may detect a 500 error in the logs, then move to the traces to see the impact on traffic, and finally, review the application metrics. This means three minimum queries when attempting to assess only one problem.

    Breaking the Data Silo

    Breaking the data silo can be a daunting challenge. There are some key points to focus on when a company starts out and some finishing touches that make for an excellent developer experience.

    Unify data storage as much as possible. For example, traces can be stored in Prometheus rather than in a separate solution. This will enable engineers to query, for example, trace latencies alongside CPU metrics. It will also minimize the UI work necessary to join together data. If an organization has the expertise, it’s possible to store everything inside an OpenSearch cluster. This means a single interface, with a single query language, to query all available observability data. Anyone assessing this option should simply decide what their company can maintain and what skills they have in-house.

    Present observability data as one. If storage unification isn’t an option, then focus on the presentation layer. Tools like Grafana can consume most observability solutions as external data sources and render them all on the same dashboard. While an engineer will still have to query them using the specific language of the tool, it means that they can render things alongside one another, making for an insightful dashboard.

    Correlate data in one user interface. This means metrics are rendered in a single dashboard alongside logging and tracing data. This will give users a static view of the data at once, even if there is a limit to how queryable the data is.

    Create universal values. If it’s impossible to make any of the previous recommendations, then this is an easy, quick win that will at least make querying observability data more consistent. An easy way to do this without making your code messy is to use a library like MDC (or your tech stack’s equivalent). Add some tags or values that will exist in all logs, metrics, and traces. For example, the name of the application and the feature it belongs to, i.e. application is credit-card-service and feature is payments. This means that if something goes wrong, there is already a set of key-value pairs that translate across every data type.

    At the very least, this will mean an engineer can issue consistent queries across the three solutions, albeit in different syntaxes. This represents the least value, but also the least rework and effort, and will lower the cognitive load of engineers who are already trying to debug a potentially serious outage.

    The Data Silo Can be Beaten

    A siloed observability strategy drives waste and inefficiency in engineering teams, precisely when they need efficiency the most. By breaking the data silo, observability data can be queried, analyzed, and correlated together to form a single, cohesive and complete picture of your system health.

  • Vercel Adds Observability Tools to Web Application Framework

    Vercel Adds Observability Tools to Web Application Framework

    Vercel today announced it has added a suite of observability tools to its platform for building web applications using Next.js, an instance of the open source React framework based on a JavaScript library.

    Vercel CTO Malte Ubl said the Vercel Monitoring tools make it simpler for developers to troubleshoot applications without relying on a DevOps team to surface data. Instead, developers can take advantage of Vercel Monitoring tools to access metrics that track user interface and application programming interface (API) issues, he added.

    That capability is not intended to replace the need for observability tools employed by DevOps teams. Instead, the goal is to provide metrics that are specific to issues that developers encounter as an application is being built or after it has been deployed so they can be addressed.

    Developers can select from pre-made queries provided by Vercel or create their own by defining what metrics they want the Vercel Monitoring tools to track, including bandwidth and web traffic spikes and requests by bots that are indicative of a cyberattack. In total, there are more than 500 potential errors that developers can track. Logs then make it possible to drill down into errors and issues once they are identified to pinpoint the root cause of an issue.

    As more responsibility for applications shifts left toward developers, there’s a clear need for tools that provide more visibility into the applications they develop. Many of the issues that DevOps teams encounter today could have been resolved if developers had been aware of them earlier.

    The Vercel platform employs caching, routing and a React framework to optimize application performance in a way that reduces the dependency on backend infrastructure to optimize application performance. That approach puts less strain on backend infrastructure as more requests are automatically processed using a framework that spans clients and servers. In effect, developers can take advantage of frameworks such as React to treat the underlying IT infrastructure as if it were serverless whenever possible with tools that have a familiar JavaScript construct. The overall goal is to increase developer productivity at a time when many organizations are much more focused on the issue.

    As a result, DevOps teams will need to make some adjustments as more applications capable of providing richer experiences are built and deployed without relying as much on backend infrastructure. That shift has implications for everything from the amount of network bandwidth consumed to the performance of web applications. Frameworks such as Next.js also tend to increase the overall rate at which web applications can be built and deployed.

    One way or another, it’s clear that the way web applications are being built is fundamentally changing. The only issue now is determining to whether DevOps teams are prepared to absorb that level of change across an ever-increasing portfolio of applications that still need to be deployed, secured, updated and optimized.

  • Why Low-Code/No-Code Tech is Important in a Competitive Job Market

    Why Low-Code/No-Code Tech is Important in a Competitive Job Market

    The demand for tech professionals is increasing exponentially as digital skills become a requirement for businesses. However, the world is facing a serious shortage of tech professionals. According to a forecast by the World Economic Forum, 50% of the world’s labor force will need to upskill in order to keep pace with technological advancements and automation. Similarly, digital transformation will change over one billion jobs. Yet the majority of workers around the world lack the digital skills to meet the demands of their jobs. According to a recent study, three in four employers in the world (data from 40 countries) claim a work shortage. On the other hand, millions of young people find themselves unemployed as they lack the basic digital skills for in-demand jobs. Low-code/no-code software solutions can offer a solution to this labor shortage and close the gap for workers who quickly need to acquire digital skills. Proficiency in low-code/no-code applications will provide candidates with a competitive edge in the job market.

    Low-code/no-code software allows citizen developers and business experts to build business applications without requiring high technical expertise. With citizen development becoming an important part of all organizations’ business strategy, the demand for workers with proficiency in low-code/no-code app development is increasing. Expertise in low-code/no-code app development has become an expected skill set for potential employees.

    Benefits of Low-Code/No-Code App Development

    Citizen Development

    As discussed above, digital skills are becoming a part of almost all technical jobs. Employees should be proficient in using technology to streamline data collection, automate daily processes and leverage mobile technology. To achieve digital transformation, employees should be acquainted with advancements in digital technologies. This is where the skills gap seeps in. Learning code is not an easy task. It requires a lot of time, patience and effort. Add mobile development skills to the list, and there’s an even larger skills gap. Low-code/no-code app development can play a major role in addressing the skills gap. With pre-built apps and components, low-code/no-code development platforms offer an intuitive environment for designing software without the need to be proficient in coding. Such platforms can be easily used by people without programming skills to build flexible interfaces their companies need. However, low code can also be used by IT departments to deliver faster digital solutions while freeing up time to focus on more complex coding tasks.

    Additionally, low-code naturally aligns with the millennial proclivity to be digitally savvy. This generation is more comfortable than ever with using digital technologies in all aspects of their lives. Thus, they are more inclined and open to using low-code/no-code platforms and gaining some expertise in the domain.

    Empowering Users

    Low-code/no-code platforms empower citizen developers or business users to “skill up” in an increasingly digital society. By empowering business users to develop applications without the need for coding, low-code generates jobs directly or indirectly. The rate of technological advancement tells us that it is no longer favorable for companies to take months to develop a single app. Since the software has a life of three to five years, taking several months to develop apps is not conducive to the competitive market. As such, low-code/no-code app development can empower business users to develop applications quickly. However, citizen developers should work with IT management to ensure that the app adheres to all the company’s compliance requirements and governance guidelines. This is because citizen developers may not be proficient in scaling apps.

    Business Experts Understand Business Application Need

    Business needs are continuously evolving, especially since after the pandemic, when so many people are remote or using mobile apps to run businesses. At this rapid pace, it is not always easy for IT departments to continuously craft new apps and immediately update apps to match evolving circumstances and needs.

    Business users who are close to daily tasks, data collection requirements and ongoing processes, are in an excellent position to understand urgent needs for specific business applications. As a result, business users are in a much better position to collect department requirements and propose an application that caters to those specific needs. In such situations, low-code or no-code development solutions equip business users to develop applications directly based on their needs.

    How Can Low-Code/No-Code Help Employees Advance Their Careers?

    As discussed above, low-code has tremendous potential and benefits in the current digitally charged environment. Here are some benefits for candidates learning low-code skills:

    1. Bridge Business and IT

    Low-code/no-code is an easy entry point for someone in their early career phases interested in working in the IT department. By learning low-code solutions for streamlining business processes and automating workflows, employees can take a step towards interfacing with IT departments or understanding the basics of coding. With necessary low-code skills, employees can also become leaders in cross-functional groups or digital transformation teams responsible for introducing low-code/no-code solutions and rapidly introducing new business applications. There is a huge gap in this space, as only 29% of IT leaders feel confident that they have the right training mechanisms for non-developers to build applications.

    2. Increase Your Salary

    As employees grow their low-code skills, their salaries will correspondingly increase to reflect digital proficiency. Since low-code/no-code skills will increase in demand, proficient users will make significant gains in terms of the salary offered by major companies. Glassdoor suggests that the average base salary of a workflow automation specialist in the U.S. is $81,335 per year, while that of a data analyst is just $69,517 per year.

    3. Higher Job Satisfaction

    A full 71% of employees expect their work organizations to provide them with the same level of service using technology that customers’ products provide. Organizations can introduce low-code/no-code applications within the organization to increase the level of employee satisfaction.

    Low-code/no-code skills are becoming highly coveted in the current competitive job market. Offering benefits to both employees and organizations, low-code app development is the key to digital transformation.

  • Six Mainframe DevOps Predictions for 2023

    Six Mainframe DevOps Predictions for 2023

    DevOps, or the automation of application development and hand-off to operations, is more prevalent than ever on the mainframe. As we begin 2023, below are six predictions for what the coming year will bring. The composite themes will undoubtedly be speed, alignment and experience.

    1. Enterprise DevOps Will Break Down Silos Across Core Systems

    DevOps practices will continue to expand beyond cloud and distributed systems to include more core systems than ever before. Core platforms, including the mainframe, are the functional backbone of mission-critical applications that drive the world economy. In a recent IDC survey, 82% of organizations reported taking steps to prioritize using the same application development tools across distributed and mainframe teams. 2023 will continue to see a rise in enterprise DevOps that will bring down the silos between platforms. As core system workloads increase, organizations will see the benefit of aligning platforms such as mainframe and distributed to realize the full benefit of their DevOps initiatives.

    2. Growth in AI/ML as Key Components for Measuring DevOps Performance Metrics

    The value of analytics will increase as organizations emphasize the use of key performance indicator (KPI) data to measure the performance and maturity of DevOps pipelines. In this way, organizations will not only gain more insights into their pipelines, but will also be able to proactively manage the data in real-time. They will be able to adjust loads as needed and create smart pipelines with advanced error handling. Organizations will leverage benchmarks within the analytics to see how they compare with their peers.

    3. Cross-Functional Pipelines Will Create Hybrid Automated Everything

    This will enable the cloud, core systems and data to deliver a more holistic view of “platforms, processes and people.” Silos will finally disappear. Automation will become a key priority in this effort as organizations seek to free up time for developers to innovate and achieve greater velocity, quality and efficiency. A single integrated and connected pipeline will emerge that automates the creation and testing of applications in a single flow. The rationale of prioritizing this alignment between distributed and core systems includes the reuse of best practices, workflow simplification, cost-effectiveness, application integration and streamlined training.

    4. DevOps Will Embrace Platform Engineering and Site Reliability Engineering (SRE)

    Platform engineering is the next evolution of DevOps, taking a DevOps platform and turning it into a self-service product to improve the developer experience. It treats DevOps as a product and allows organizations to create an end-to-end offering that serves developers, operations and platform teams. SRE focuses on the “Ops” side of DevOps, specifically how to automate operations capabilities. This is vital to the DevOps mission since it provides the data that allows organizations to manage what they can measure.

    5. DevSecOps Will Play an Increasingly Central Role

    Security must become a central part of the toolchain and a “first-class citizen” in the pipeline. Static application security testing (SAST), also known as white box testing, along with dynamic application security testing (DAST), also known as black box testing, will be further integrated into processes to enable security teams to scan large loads at rest or in real-time quickly and efficiently.

    6. Git-Centric Enterprise Source Code Management (SCM) Strategy Will Grow

    Git has become the de facto standard for SCM for distributed application workloads. In a recent IDC survey of industry professionals across 11 countries and multiple industries, 87% said they will migrate all or some of their mainframe source code to Git over the next three years. This means Git and Git-native development will become important capabilities for mainframe DevOps companies to support. Organizations will increasingly prefer implementation approaches and tools that offer flexibility in both scope and pace.

    The techniques highlighted in these predictions are, naturally, related. They point to an ever-increasing capacity for efficiency and quality that will enable core and distributed technologies to work better together.

  • WTH? WFH is 6× Pre-Covid ¦ Plus: Agile Sucks (Redux)

    WTH? WFH is 6× Pre-Covid ¦ Plus: Agile Sucks (Redux)

    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: Working from home is here to stay, and Agile is still a failure. (more…)

  • Tricentis Extends Mobile Testing Reach

    Tricentis Extends Mobile Testing Reach

    Tricentis has added the ability to test native iOS and Android applications to its Testim software-as-a-service (SaaS) platform.

    Previously, the Testim platform was designed to test web applications, but is now being extended as organizations build and deploy more native mobile applications. Capabilities of the Testim Mobile platform include codeless authoring of mobile tests to eliminate the need to write and maintain Appium, Espresso or XCUITest scripts and the ability to set up, configure and manage mobile devices in minutes using the Tricentis Mobile Agent.

    Additionally, applications can be uploaded to the cloud to enable collaboration across testing teams as they opt to run tests on real or virtual devices. DevOps team can also customize tests with JavaScript, branch tests, and run tests from a scheduler or continuous integration (CI) build.

    Tricentis CTO Mav Turner said Tricentis is moving to implement a consistent set of mobile testing services across its entire portfolio of testing platforms now that mobile applications are equal, first-class citizens alongside web applications. The primary driver of that shift has been the COVID-19 pandemic, which saw more organizations relying on external-facing mobile applications to engage customers, partners and employees working remotely, he added.

    Testing those applications is more critical than ever because the cost of failure—in terms of impact to the business—is now much higher as more organizations depend on software to drive business processes, noted Turner.

    The goal is to make it simpler to test these applications as they are being developed to ensure quality, he added. The challenge is that many developers don’t have a software engineering mindset. Hence, they need to rely more on testing platforms infused with artificial intelligence to discover issues relating to, for example, scalability and security, said Turner.

    Most organizations are trying to shift more responsibility for testing toward developers as part of an effort to remediate issues earlier in the software development life cycle. It’s more challenging for developers to remediate issues after an application has been developed simply because they have lost context. Testing platforms infused with AI capabilities make it easier for developers to identify issues at the time they are writing code. In the future, Tricentis expects to take advantage of generative AI platforms to make it easier to author tests using a set of best practices that can be easily reused, noted Turner.

    That approach doesn’t necessarily eliminate the need for a dedicated quality assessment review, but it does allow testing teams to focus more of their time and effort on testing the application experience versus identifying lower-level errors that many developers routinely make.

    Over time, the quality of applications should steadily improve as many of the low-level mistakes that developers make are eliminated. That should improve everything from application security to the user experience as applications are continuously updated. In the meantime, organizations that embed testing within DevOps workflows are steadily reducing the number of issues that need to be addressed just before applications are supposed to be deployed.

  • How Will Low-Code and AI Coexist?

    How Will Low-Code and AI Coexist?

    Over the last few years, low-code solutions have become important to help organizations widen programming accessibility to a broader base of users and help fill development gaps. And these technologies are still gathering steam—Gartner estimated the market for low-code development technologies would grow to a total of $26.9 billion in 2023 and that 65% of applications would be developed using low-code by 2024.

    However, AI-based code generation is set to further level the playing field with advanced auto-completion and AI coding assistants. Take GitHub’s Copilot, a helpful AI pair programmer, or OpenAI’s ChatGPT, which can generate complex functions given natural language prompts. Other AI tools trained on UIs, like Galileo, can derive frontends for any project in a matter of seconds. So, are low-code development platforms still relevant in this new paradigm of AI-driven software development? If so, how will low-code solutions and AI coexist?

    Well, generic algorithms can only go so far in creating truly unique and innovative experiences that solve real-world business cases. And since most programmers don’t have the skills to train machine learning or deep learning models from their own datasets, this is one area where low-code solutions could lower the barrier, enabling users to tag unstructured data, generate models, run simulations and share reusable AI across departments.

    I recently chatted with Johanna Pingel, MathWorks AI product marketing manager, to discuss the role of AI in low-code programming. According to Pingel, low-code solutions can lower the barrier to AI functionality, both in quickly integrating AI-driven functionality into applications and seamlessly generating new AI algorithms based on unique datasets. Below, we’ll consider some additional use cases for AI and how low-code platforms may work alongside it.

    Use Cases For AI

    First off, what are application developers using AI for these days? Well, AI has an increasingly wide range of uses. It began with computer image and processing, such as identifying cats and dogs or postal codes. Now, voice recognition, voice-to-text and text-to-voice are commonplace, and AI is being used to create ever more sophisticated applications. One area that Pingel is excited about is applying AI to help with battery life management, predicting the lifespan of batteries for electric vehicles and consumer electronics.

    “Anywhere there’s data, there’s an opportunity to create models and make predictions based on that data,” said Pingel. This especially rings true for scenarios involving data and visualization. For example, consider visual inspection in a pharmaceutical production line to ensure there are no defects with any pills. Another use case is visual inspections of parts in automotive assembly lines. Applied to these scenarios, AI can remove grunt work and make production safer and more efficient.

    ChatGPT is another example of an AI application quickly becoming mainstream, not only for content creators but for scientific research. For example, ChatGPT can spin up MATLAB code and create functions and unit tests, which can be copied and pasted into IDEs. However, code written by ChatGPT should still be checked and verified by engineers and scientists to ensure its accuracy, said Pingel.

    How Low-Code Lowers the Barrier to Bespoke AI

    Low-code solutions can bring many benefits—these platforms help to speed up tedious tasks and level the playing field for those with different coding abilities and desires, said Pingel. Low-code platforms can also be a great tool for engineers looking to incorporate AI into their business systems. For example, having a low-code method could be helpful in generating code for machine learning algorithms and new models, which can help companies create their own algorithms and incorporate them into their own platforms.

    More people are interested in incorporating AI into their applications, but the use cases are growing increasingly more sophisticated. Businesses may start with simple needs, such as identifying faces or identifying text from handwritten letters. These rely on simple datasets, and algorithms already exist to accomplish most of these tasks. Off-the-shelf AI is helpful at the start, but eventually, companies tend to want to use their own data and settings far more specific to their applications, said Pingel.

    For example, a more niche case could be as specific as identifying cracks in a windshield. Or, in a financial industry, it might be analyzing mass amounts of loan applications to determine approval rates. In these situations, using your own internal data becomes necessary to create AI that understands the situation at hand. Yet, the hard part is assembling this data in a way that is accessible for a model to comprehend. Analysts estimate that upwards of 90% of data held within enterprises is unstructured, meaning it requires a heavy amount of tagging to become usable for AI/ML.

    Low-code is a perfect solution to avoid the point-and-click aspects of cleaning, cropping and structuring this data, said Pingel. This can introduce automation into the process of labeling data while still involving some human intervention to fix mistakes. Machine learning models can take a while to train and simulate, underlining the need for a fast way of iterating on models. A common platform could help companies develop custom machine learning algorithms and models and keep track of experiments. This helps to ensure that the most effective model is used for the final application.

    End Benefits of Democratized AI With Low-Code

    Overall, low-code platforms can provide a number of benefits that lower the barrier to unique AI creations. Low-code platforms like MATLAB and Simulink are useful for structuring and labeling data and quickly generating ML models. Low-code also makes it easier to bridge different programming languages and quickly generate code for a variety of different languages, such as C, C++ and Java.

    Additionally, low-code platforms enable users to collaborate easily with other users. But low-code doesn’t mean no-code, Pingel explained. In fact, “you can learn to be a better programming through low-code tools, too,” she said. For example, it could enhance understanding of the structure and syntax of code.

    In general, democratized AI is an important capability for businesses to take advantage of. Although AI-based solutions are coming to market to displace old tools and processes, there is still an argument to be made for low-code—namely in empowering non-technical users and lowering the barrier to creating custom AI-driven functionality.