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Tag: artificial intelligence

  • Truth and Timeliness

    Truth and Timeliness

    truth-n-timeliness

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

    Can ChatGPT Fix Bugs? ‘Wolverine’ Dev Says YES

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

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

  • Postman Releases Tool for Building Apps Using APIs

    Postman Releases Tool for Building Apps Using APIs

    Postman today made generally available a visual tool infused with artificial intelligence (AI) that makes it simpler to build software by invoking application programming interfaces (APIs) found on its network.

    Shamasis Bhattacharya, chief software architect for Postman API, said Postman Flows makes it possible to use a low-code tool to build software using tens of thousands of APIs that can be found on the Postman API network.

    The goal is to reduce the amount of toil required to build applications of moderate to medium complexity in a way that doesn’t require developers to have deep programming expertise, he added.

    Instead, developers are presented with “blocks” that they can drag and drop to create an application and deploy the associated workflows. Postman Flows leverages AI to manipulate data returned by APIs using a natural language interface that reduces the level of technical acumen required to create those workflows.

    That approach provides developers with a reactive experience that is embedded within a visual tool, noted Bhattacharya.

    Many developers still have a natural bias toward writing code, but as demand for applications continues to increase, Postman Flows provides a means to significantly increase developer productivity, said Bhattacharya. There may still be instances where writing code is required, but as developers save more time using Postman Flows, many of them will switch over to a visual tool to build software faster by essentially gluing blocks together, he added.

    There’s naturally a lot of focus these days on developer productivity during uncertain economic times. More organizations than ever are dependent on software, but their ability to hire armies of developers is limited. Low-code tools have emerged to provide a means for accelerating application development, but Postman is now looking to apply that concept directly to the network of curated APIs that it provides to application developers.

    The one thing that is certain is that as the pace at which applications are being built increases, the number of them running in production environments will steadily increase. DevOps teams will find themselves maintaining a wide range of applications that were constructed using both procedural code and low-code platforms.

    In the meantime, IT organizations may want to start evaluating which types of applications lend themselves to being built by development teams that may not require as much experience writing actual code. Much of the application development backlog organizations are struggling with today could be reduced using tools that automate a wide range of development tasks. That should free up developers with more advanced skills to work on more complex applications. That approach might also reduce developer turnover as much of the fatigue associated with building more routine applications is reduced.

    Regardless of how applications are built, there will be a need to manage them in production environments. DevOps teams may soon be able to focus more of their time and expertise on optimizing application performance in production environments versus continuing to shepherd massive amounts of code through continuous integration/continuous delivery (CI/CD) pipelines.

  • HPE to Acquire OpsRamp to Gain AIOps Platform

    HPE to Acquire OpsRamp to Gain AIOps Platform

    Hewlett-Packard Enterprise (HPE) this week revealed it is acquiring OpsRamp to gain access to a platform for embedding artificial intelligence in IT operations (AIOps).

    Latha Vishnubhotla, chief platform officer in the office of the CTO for HPE, said the company would embed the machine learning technology developed by OpsRamp across its entire portfolio in addition to leveraging it to automate IT processes the company manages via its GreenLake service for HPE customers.

    Ultimately, HPE will be applying machine learning across the entire IT stack, including applications, to reduce the total cost of IT, she said.

    The acquisition of OpsRamp is expected to close in the third quarter of this year. Terms of the deal were not announced, but it comes at a time when more organizations are getting comfortable with AIOps.

    A recent OpsRamp survey polled 138 IT leaders working for enterprise IT organizations and 127 representing managed service providers (MSPs) that generate at least $25 million in revenue and have more than 500 employees. The survey found the primary reason organizations are embracing AIOps is to improve service and application availability and performance (60%), followed closely by automation of operations (58%) and processes (54%).

    The survey found that the greatest IT operations challenge for enterprises in 2023 was automating as many operations as possible (66%), followed by driving greater efficiency and productivity of IT operations teams and processes (61%). Application-to-infrastructure dependency mapping is the top incident management challenge (64%), the survey found.

    The primary operational benefit cited was reducing open incident tickets (65%), followed by reducing mean-time-to-discovery (MTTD) and mean-time-to-remediation (MTTR), tied at 56%, and automation of tedious tasks (52%).

    In general, organizations are choosing one of two AIOps paths, said Vishnubhotla. Some are waiting to evaluate the recommendations made by the AIOps platform before implementing, while others are looking to completely automate processes so they can focus more of their time and effort on application development and deployment, she said. In much the same way a driver of an autonomous vehicle learns to trust the AI over time, there is a similar transition occurring with AIOps, noted Vishnubhotla.

    The challenge is that it takes an AIOps platform a few months to learn an IT environment. The more dynamic that IT environment is, the longer that process can take. At the very least, however, AIOps platforms have the ability to automate many low-level IT tasks today that conspire to increase the amount of toil DevOps teams would otherwise encounter.

    It’s difficult to determine how pervasive AIOps may be within IT environments, but there is no doubt as organizations look for ways to manage IT at scale, there will be increased reliance on automation. Most organizations can’t afford to continue to hire armies of IT personnel, especially as the global economic outlook remains uncertain.

    In fact, one of the benefits of an AIOps platform is that, unlike an employee, it does not take any days off or suddenly decide to take another job. It also, for better or worse, doesn’t forget anything once it’s been learned. As such, it’s now only a question the degree to which AIOps platforms will be trusted over time to manage increasingly more complicated IT tasks.

  • Neural Hashing: The Future of AI-Powered Search

    Neural Hashing: The Future of AI-Powered Search

    In recent years, vector embeddings have emerged as a promising technology to power information retrieval projects. Vectors use mathematics to represent words and encapsulate meaning. Combined with deep learning algorithms, you can build powerful AI search applications. However, recent advances have shown that another kind of technology – neural hashes – can outperform vectors while avoiding the tradeoffs that people experience with vector search.

    Neural hashing’s superiority over vectors will soon make it accessible, affordable and the primary method for delivering AI-powered search for e-commerce, enterprise search and other areas where search data is constantly growing and changing. Even as early as 2023, neural hashing will be the primary method for delivering AI-powered search.

    First Came Keyword Search

    Most of us are familiar with the basics of keyword search technology, which most search engines still use. When someone submits a query, the keyword search engine will perform a simple lookup and display results by relevance. Keyword algorithms employ techniques such as natural language processing (NLP), lemmatization, typo tolerance, synonym libraries and more to perform the lookups.

    Keyword search engines work well for the most part but can fall short when it comes to long-tail queries, i.e., those longer and more specific queries which represent half or more of search queries. Despite its powerful keyword technology, you can never write enough rules or stuff your site with enough keywords to cover every edge case. Being able to improve long-tail query results, however, can be a huge benefit to an organization, which is why more companies are beginning to look for new technologies to complement their keyword search engines.

    Then Came Semantics

    Semantic, or AI-powered search, can understand concepts. Semantic search goes beyond matching to understanding. For example, if you searched “something for a headache” on a website, a keyword search engine would look for items tagged with those specific words, turning up results only if they can match some or all of the text. But a semantic search engine would understand the concepts being described and offer solutions—such as pain medications.

    Semantic search has traditionally been employed using vector embeddings. You can use vectors for all kinds of objects, too—images, videos, audio, etc. Vector-powered search engines can be incredibly smart, but processing and maintenance make them too expensive to run in production for most enterprise use cases. This is true, especially for certain industries where data is dynamic. In retail and e-commerce, for example, that inability to pivot and scale quickly is a real detriment, as brands need to update data that is unpredictable (e.g., user-generated content) and constantly in flux (many online sellers update their search databases hourly).

    The solution is neural hashing, which refers to the process of compressing vectors with neural networks (deep learning algorithms which mimic the human brain) into hashes or binary vectors. These hashes retain 96% or more of the vector information while using a fraction of the storage space. A semantic search engine built using neural hashing is nearly or just as accurate as vector search but about 500 times faster.

    Better Results With Hybrid Search

    When it comes to AI-powered search, neural hashing will eliminate the tradeoffs between speed, accuracy and costs. It can deliver results in single-digit milliseconds with no additional overhead. As good as it is, it’s even better when combined with a keyword search. We can use hybrid search—both technologies—simultaneously; most queries are a combination of keywords and concepts.

    For example, what if rather than looking for a type of product, a shopper is searching for help with getting rid of a pesky symptom? Or, has a question-style query? As search data indicates, people are often myopically focused on a problem that they desperately want to solve, such as:

    ● Leaky faucet
    ● Stress headache
    ● Stained carpet

    They only know they have an urgent problem. Of course, they’re looking for a solution, but they typically have no idea what particular product they need in order to get relief. They’re still using some keywords, however, like “faucet,” “stained” or “carpet.”

    Each query can be scored by both the hashing and keyword engines to deliver a superior result for every query. Hybrid search can be combined with traditional search engine features like natural language processing (NLP), autocomplete, learning-to-rank algorithms, filters and more to optimize the end-user experience.

    When it comes to search, speedy, accurate results are the name of the game. In order to make searches as intuitive, fast, and scalable as possible while still remaining cost-effective, search and discovery platforms should explore what hybrid search powered by neural hashing has to offer.

    It has been shown that neural hashing can make highly intelligent AI fast and cost-effective at scale and without the need to bring on highly specialized engineers for every project. We appear to be at another turning point in the world of artificial intelligence, and neural hashing is going to be the driving force behind many future innovations.

  • Before ChatGPT, AIOps Powered Enterprises for Years

    Before ChatGPT, AIOps Powered Enterprises for Years

    Led by OpenAI and its ChatGPT software, a new era of consumer use cases for artificial intelligence is here. These exciting new tools have made AI more accessible to more users than ever before. No longer is this technology the exclusive domain of wealthy corporations.

    However, while the world is now focused on the potential use cases for AI in consumer applications, it’s important to remember that this technology has been part of the business world for years. Behind the scenes, AI has quietly been powering solutions that companies of all sizes have harnessed to improve business outcomes. These technologies have specifically been used by IT teams in the form of artificial intelligence for IT operations (AIOps) to help modernize their companies and operate more efficiently.

    By leveraging AIOps-powered solutions in use cases like observability, overworked and understaffed technology teams are improving their ability to manage complicated, hybrid and multi-cloud IT environments, automate processes and optimize their businesses. As companies prepare for a potential economic downturn and a reduction in resources, these solutions and AIOps are more important than ever.

    Here are four ways that AIOps is helping businesses overcome today’s IT challenges.

    Providing Unified Visibility Into Complicated Networks

    Companies have accelerated their digital transformation efforts in recent years. This has included migrating workflows to public and private clouds, implementing new productivity tools and adopting next-generation technologies. The average business now relies on geographically dispersed workforces and IT environments that use 2.6 clouds and leverage more than 200 apps!

    The result is that organizations lack visibility into these environments. A recent SolarWinds survey found that 54% of technology professional respondents stated they only have visibility into about half or less of their apps and infrastructure. This lack of visibility can lead to problems that negatively impact the return on investment for projects.

    AIOps helps overcome this challenge by providing comprehensive visibility into an organization’s systems, regardless of whether those environments are hosted in on-premises, hybrid or multi-cloud environments. AIOps automatically collects and integrates insights and information across geographies, apps and infrastructures into one dashboard. This provides technology teams with single-pane-of-glass visibility into the state of IT environments.

    Automating Processes & Remediating Issues

    While the visibility provided by AIOps and observability is integral to managing today’s complex IT environments, the technology goes a step further.

    With AIOps, teams can automate processes including the identification and remediation of issues. Given the stakes, this is critical. Companies must ensure their environments are always available and that their performance meets and exceeds customer demands. Even short service outages can lead to frustrated users and a damaged customer experience.

    This is challenging, as an IT anomaly often results in many alerts and a flood of log messages, making the problem difficult for a human to understand and address. AIOps quickly ingests massive amounts of information, provides rapid root cause analysis, data/event correlation and incident management which allows for fast and automated remediation of issues as they arise.

    Additionally, since this technology is always observing and gathering information on potential incidents, it can predict and prevent future issues proactively. By implementing AIOps, companies are taking a step toward autonomous operations or environments that can manage themselves without human involvement.

    Protecting Revenue

    In the face of an uncertain economy, every organization is taking steps to protect its revenue. With AIOps, enterprise technology companies have a powerful tool in this fight.

    By implementing AIOps and observability, companies can quickly resolve and prevent IT issues before they arise. This ensures that IT environments are operating at maximum efficiency.

    With AIOps, organizations can reduce downtime and prevent outages. This ensures that business-critical applications and infrastructure are available and functioning. These capabilities are more important as even short service outages have reportedly been capable of causing millions of dollars in damages.

    Freeing Up Employee Time for Innovation and Efficiency

    Over the last few years, companies have experienced severe staffing challenges, with many organizations finding it difficult to fill critical technology roles. This has led to smaller teams with more responsibilities and less free time. The result has been overworked technology professionals who spend far too much time on manual and menial tasks that do little to support the business’s bottom line.

    By implementing AIOps, organizations can streamline workflows by automating repetitive and routine tasks. Doing so can cut down on the amount of overwhelming metrics technology professionals are required to analyze and, instead, empower those individuals and teams to again focus on innovating and completing projects that support key business objectives.

  • Optimizing Cloud Costs for DevOps With AI-Assisted Kubernetes

    Optimizing Cloud Costs for DevOps With AI-Assisted Kubernetes

    As indicated in my prior blog, Optimizing Cloud Costs for DevOps With AI-Assisted Orchestration, an AI-assisted Kubernetes orchestrator is needed to optimize cloud costs for DevOps, DevSecOps and SRE. This blog describes the landscape for cloud-native optimization approaches and a features requirement blueprint for an AI-assisted Kubernetes orchestrator. The blog also describes a roadmap for implementing an AI-assisted Kubernetes orchestrator and the benefits of a solution using an AI-assisted Kubernetes orchestrator.

    Cloud-Native Optimization Approaches Landscape

    As indicated in the article Tricks for Cloud Cost Optimization, “Cloud cost optimization (CCO) is a technique of running cloud-native applications effectively, at minimal possible cost, without affecting the health of the services.” The most typical approaches are variants of the following:

    • Rightsizing pre-paid, fixed and reserved instances: Cloud service pricing models are intended to fit your application and business needs with the hundreds of choices available from cloud service providers. This approach can be cost-efficient when application and business needs are constant over time. However, this approach is not sufficiently cost-efficient for many applications and business needs that are constantly changing, such as use cases for DevOps, DevSecOps and SRE.

    • Spot instances can offer low cost-per-instance, but reliability is not guaranteed because the cloud provider can interrupt these services anytime. Unless the use of spot instances is limited to workloads that do not require a high level of reliability, this approach is limited. This approach does have the potential to have a broader application if a smart management layer is implemented to orchestrate the use of the spot instances.

    • Pay-as-you-go offerings by cloud service providers, used together with infrastructure-as-code (IaC) and auto-scaling, can help save on cloud costs. This approach facilitates the automatic spin-up of resources when needed and automatic shutdown when not needed. However, to realize the benefits of this approach requires constant supervision of cloud usage and manual adjustments to realize the benefits. Businesses may not even be aware of the resource scaling until the monthly bill arrives.

    • Cloud auto-stopping of entire applications and clusters can help to manage cloud resources automatically to ensure that they run only when needed, never when idle. This approach is not granular enough to sufficiently reduce costs for rapidly fluctuating resource needs for DevOps, DevSecOps and SRE use cases.

    • Manual cloud costs management, in which team members are taught to understand their own cloud spend and cloud cost management practices, can help reduce costs. However, this approach is subject to human involvement, is slow and generally does not provide granular controls needed to sufficiently deliver cloud cost savings 24/7.

    None of the traditional cloud cost optimization approaches described above have the requisite smarts, speed or granularity to sufficiently optimize the highly dynamic requirements for DevOps, DevSecOps and SRE use cases. An AI-assisted orchestration approach, when supported with the right features, can make up for the deficiencies of the typical optimization approaches.

    Features for AI-assisted Kubernetes Orchestrator

    As indicated by CAST AI an AI-Assisted Kubernetes orchestrator needs the following features:

    • Easy to start – It should take less than five minutes to fully onboard a cluster.
    • Easy to use – The orchestrator should abstract layers of complexity, so little knowledge is needed to use it other than a basic knowledge of running Kubernetes clusters in a public cloud. For example, a working knowledge of the kubectl command line utility for creating and managing Kubernetes clusters.
    • SaaS configuration – Avoids the need to host and manage the orchestrator itself.
    • Autoscaler policies – Define threshold values and upper bounds so instances are automatically added or removed as per the policy.
    • Cloud Support for AWS EKS, GCS GKS, Azure AKS and kOps clusters
    • Reports to monitor and track cost savings for each cluster.
    • Features to optimize each cluster.
    • High availability to ensure the orchestrator itself is not a failure point.
    • Collect and analyze each cluster configuration to provide the most optimal setup along with a savings estimation for the current cloud environment.
    • Security – Detect and monitor K8s vulnerabilities and configuration issues.

    Roadmap for Implementing AI-Assisted Kubernetes Orchestrator

    According to the article Roadmap To Cloud Cost Optimization, organizations need to understand that they need to deploy cloud computing the right way. This not only paves way for the actual digital transformation but is even instrumental to cloud cost management.”

    The article Top 3 Issues in Kubernetes Cost Management indicates, “Teams cannot manage and monitor Kubernetes’ costs without having the right tools in hand. No doubt, you can collect all the data yourself and create the right solutions. It wouldn’t be easy to modify these solutions if changes are required.” The speed of change with DevOps, DevSecOps and SRE is not compatible with the typical tools.

    A good example AI tool, CAST AI, indicates how easy it can be to onboard a Kubernetes cluster. First, connect your Kubernetes cluster to CAST AI and see how much you can save by optimizing cluster configuration. After exploring the available savings and cost reports, you can onboard the cluster into CAST AI and set the Autoscaler policies, which will manage the cluster for you. It takes less than five minutes to fully onboard a cluster.

    Benefits of Solutions Using AI-Assisted Kubernetes Orchestrator

    An AI-assisted cloud orchestrator can provide the following benefits.

    • Spot instance automation
    • Spot instance fallback
    • Real-time autoscaling
    • Instant rebalancing
    • Visibility into cloud usage and costs
    • Avoids over-provisioning
    • Cloud resources are not left idle
    • A consistent approach between teams and departments
    • Reduce monthly average cloud spend by up to 60%

    Summary

    The prior blog Optimizing Cloud Costs for DevOps With AI-Assisted Orchestration explained why an AI-assisted Kubernetes orchestrator is needed to optimize cloud costs for DevOps, DevSecOps and SRE. In this blog, specific requirements for such an AI-assisted Kubernetes orchestrator was explained using CAST AI as an example. The steps to onboard a cluster to the AI tool was explained.

  • The Time for Automated Testing is Now

    The Time for Automated Testing is Now

    Skilled software testers continue to be in high demand, and it appears that trend won’t change any time soon. The current labor shortage and a looming recession is giving software development leaders good cause to think about how to attract and keep qualified personnel and, more importantly, enable them to work as efficiently as possible. Added to this scenario is a trend in the software development cycle toward shift left testing, which has put increased pressure on developers to take on testing responsibilities that often require taking on new perspectives and using new tools. Automated testing solves these problems by making it easier to learn how to build a consistent, reliable testing program in less time, requiring less maintenance.

    Add to this the future promise of artificial intelligence or AI-augmented testing platforms and current machine learning (ML) capabilities, such as self-repairing of test scripts, and you have a platform that both levels the learning curve and accelerates efficiency. The sooner organizations can transition from manual to automated testing, the better they can withstand internal and external market fluctuations.

    A Developer and a Tester Walk Into a Bar …

    Traditional software testing is a manual process in which testers are employed to replicate users and are expected to bring that perspective to their work. As such, software testers have a very different mindset than software developers. Where developers are protective of their creative output, software testers are driven by a curiosity to probe the limitations of a system. It stands to reason that the qualities that make someone excel at either of these roles is, likewise, not the same. And yet, with the trend toward shift left testing and continuous integration/continuous development (CI/CD), the industry has blurred the distinction between coder and tester to the dismay of both sides.

    Automated testing removes much of the monotonous work associated with repetitious test plans. It allows testers to focus on writing better scripts, extending the scope of their testing plans and scheduling more powerful load tests. From the organization’s perspective, once you have established a library of reusable test scripts, your testing program will be protected from future turbulence in the employment market, including recession and attrition.

    Low-Code/No-Code

    Regardless of the tool you use, code will be created if you are creating software — even test scripts. Ideally, however, a low-code/no-code tool can hide some of the complexity and promote best practices. Using a low-code/no-code tool can relieve a tester from creating test scripts from scratch by recording manual interactions and offering suggestions. In the case of shift left testing, a low-code tool can eliminate the need to learn entire testing frameworks while still enabling a developer to access code when desired. Automating testing also eliminates the human error inherent in repetitive manual tests. In short, test automation does not diminish the value of skilled software testers and developers; it increases it.

    Placed in the hands of skilled testers, automated testing will only become more powerful, more effective and more critical to your development cycle. It elevates the skills of testers and makes them more productive, accurate and thorough. Test automation also makes for a smoother transition to shift left testing, allowing developers to spend less time testing their software and more time doing the creative work that makes them thrive. In a market where attracting and keeping top talent is only going to get more difficult, anything you can do to lighten the burden on your employees and make them more productive will be a big win for your business. So, the question is not whether you should transition from manual to automated testing but when.

  • CircleCI Will Use AI to Increase Collective DevOps Intelligence

    CircleCI Will Use AI to Increase Collective DevOps Intelligence

    CircleCI has committed to adding additional collective intelligence capabilities to its continuous integration/continuous delivery (CI/CD) platform that will leverage machine learning and other forms of artificial intelligence (AI) to optimize application development and delivery.

    CircleCI CEO Jim Rose said that as the company’s namesake CI/CD platform continues to collect more telemetry data from multiple customers, it becomes feasible to create AI models that will optimize everything from application testing to reliability and performance.

    As part of that strategy, CircleCI has also added capabilities such as test splitting recommendations that employ machine learning algorithms to estimate the expected time savings of enabling parallelism on test jobs.

    In addition, CircleCI is creating a team of analysts and data scientists to develop additional AI capabilities based on the data collected via the cloud version of the company’s platform.

    The decision to invest more in developing AI capabilities follows the acquisition of Ponicode, a provider of a unit testing platform, earlier this year. That platform uses AI to augment application testers by providing capabilities such as syntax validation, on-hover hints and autocompletion capabilities to improve the accuracy of software developed using Visual Studio Code. The goal is to integrate that AI testing technology within the CircleCI platform to make it possible to apply algorithms to individual developers’ code as well as to code aggregated from multiple developers within a build as it is continuously updated across a DevOps workflow.

    The workflows make up a software supply chain that, in many regards, functions like any other type of supply chain, noted Rose. As such, there is plenty of opportunity to apply AI models to optimize those workflows in the same way AI is being applied to optimize other types of supply chains, he added.

    It’s still early days as far as applying AI to DevOps processes is concerned, but it’s clear there is a need to further automate a wide range of manual processes. It’s not likely that AI is going to replace the need for DevOps engineers any time soon, but it will reduce a lot of the toil that currently hinders productivity. The challenge, of course, is that it takes a lot of telemetry data to train an AI model, which typically can only be efficiently gathered and stored with a cloud platform. Less clear is whether AI capabilities might drive more organizations to shift away from legacy CI/CD platforms deployed in an on-premises IT environment where there might not be enough telemetry data available to effectively train an AI model.

    The one thing that is certain is there is now a race to apply AI to DevOps workflows. Organizations that have adopted DevOps typically are highly committed to automation, but the amount of AI applied to those workflows has been limited thus far. In the coming year, however, chances are good the amount of AI being applied to DevOps workflows will steadily increase, especially as many DevOps teams struggle to keep pace with complex application environments.

  • Adopting Shift Left Testing in Software QA

    Adopting Shift Left Testing in Software QA

    I am often asked to recommend best practices for building software testing programs. The problem is that it depends on your definition of “best.” What works for an innovative startup developing software and deploying agile development sprints is not necessarily going to suit an established public company launching a new web product. The software development industry is so broad and diverse that there is no one-size-fits-all set of practices I can recommend. Put simply, the “best practices” are those that produce consistent results over time, meaning that they can be standardized. There has been a trend toward shift left testing in the past decade, and I have a few suggestions for getting the most out of development teams taking on this new responsibility.

    Shifting Left Testing Supports Agile Software Development

    “Shift left testing” means testing software for functional quality during the development phase, rather than waiting for the QA testing phase within the traditional software development cycle. Back when software was packaged for sale and deployment rather than delivered as a service, there was time in the development cycle for a dedicated QA phase. Developers started with a concept, analyzed their requirements and came up with a design before beginning to write the code. Once written, the software code would be integrated within existing systems (if needed) and then the software would be passed on to the QA team. Any bugs were sent back to development to be fixed and sent through the cycle again. All of these phases represented a conceptual move from left to right.

    Shift left is a departure from this cycle in which developers test their own software in an effort to speed up the process. This kind of development works particularly well for dev teams engaged in agile development. By having developers test their software as they go, bugs can be identified earlier in the development process, which saves time and resources and enables more thorough testing.

    Shift Left is Also Problematic

    There are some significant problems with the Shift left method that can’t be ignored. Specifically, developers are being asked to perform functions they were never properly trained to do—and often have no real interest in doing.

    Software developers are creative people by nature. Whether self-taught or formally trained, the majority of them have come to their positions in pursuit of creating something. Quality assurance testing, by contrast, is a destructive process. The goal of the QA engineer is to break things—kicking the tires and carefully documenting their destruction. And when you ask your creators to become destroyers, it doesn’t always go well.

    We can’t fault developers for not wanting to find faults in their code. Just as the best builders are not the best demolition experts, software developers want to create and then move on from one project to the next. So their reluctance to engage in testing is understandable. It’s part of who they are. The truth is that most developers find testing a monotonous chore. We need to learn how to encourage and incentivize them to become good testers of their own code.

    Provide an Encouraging Environment

    The first step toward making developers better testers is to educate them on the importance of testing as part of the development process. They may initially think that you’re only interested in saving money on hiring QA testers, or that they are being asked to perform the work of two employees. Introduce shift left testing as an integral part of the development process, explaining that continuous early testing is key to building high-quality software. By putting testing within the context of elevating software quality, developers will become more engaged in the process, raising the stakes and their involvement in projects.

    Make Software Testing Easier for Your Developers

    Since testing is not likely to be the primary interest of your developers, simplifying the task is going to make it at least more palatable to them. Test automation tools can be a big part of this strategy. Automated test scripts can be run flawlessly, frequently, and with much greater load capacity than with manual testing. You’ll be able to scale your tests to mimic a single user, or thousands of concurrent users, to discover how your software holds up under high demand. Pixel-for-pixel aberrations and errors in your graphic user interface (GUI) can be analyzed more quickly and effectively than with manual testing.

    Automated testing also takes much of the monotony out of testing, preventing burnout and resentment among your employees. Writing scripts also has the benefit of being, inherently, a creative, not destructive, process. By integrating the running of scripts into the normal workday of the developer, and emphasizing their value in building the best software possible, testing becomes a valued task, rather than a mindless chore.

    Providing Other Incentives

    When you adopt shift left testing, you just might be asking your developers to take on a role that they don’t enjoy as much as coding. And they may never enjoy it as much. But that doesn’t mean there aren’t actions you can take. If you want to keep your most valued developers happy and encourage them to stay with you, think about providing other incentives that make the entire job package irresistible. Make your company a great company to work for. From the working environment to other perks and compensation, any way you make your team happy can help them manage integrating new responsibilities.

    Testing Tools Lighten the Load

    As test automation technology gets more sophisticated, testing tools that incorporate aspects of artificial intelligence technologies to assist in test script generation and self-repair will play a larger role in enabling your developers to focus on the more creative aspects of the job — addressing design requirements and functionality (the “what” and “why” factors) in novel ways, and leaving more analytical, procedural tasks (the “how” factor) up to the tools. As testing tools perform more of the monotonous tasks for us, and with greater accuracy, developers will have more time for the creative tasks that keep them happy. These technologies are evolving as we speak and will address many of the issues described above. But you might still want to consider buying some doughnuts for the break room.