Tag: application quality

  • 2024: The Year of Testing

    2024: The Year of Testing

    Caveat: I currently cover DevOps – including test – and security – including the *AST. This does give me a viewpoint that others may not have, and also potentially gives me blinders that others may not have. I am not generally a predictor but a reader of direction. Proclamations like this are rare for me and are shorter-term/more tactical than the grab bag of predictions usually seen this time of year.

    I was thinking about this one this morning; the direction of travel of traditional and security testing is very similar at the moment. We are seeing the vast complexity of the overall space and the rapid growth of automation come together to once again make all testing a topic worthy of consideration.

    I loved test-driven development (TDD). I can’t speak for others, but RPC/API is just a cool way to implement centralized functionality, and TDD is almost mandatory to develop APIs and the clients that call them. There is no UI to distract or have separate requirements in API development; there is a stated function and correct or incorrect behavior. To a computer scientist, this is predictable beauty. TDD made it more than predictable—it made it a trail through the woods. “We know it needs to do X, Y and Z, with the only side effects being A,” can be detailed with tests to prove that is what it does, and then look to prove the “It doesn’t have other side effects” part. And you can do it all while developing—no need for dev/test iterations. Like any IT solution, it isn’t a great fit for everything, but even if it isn’t called TDD, it is nearly impossible to develop API-based solutions without some level of TDD.

    The thing is, TDD is absolutely a developer’s tool. Developers and DevOps pretty much must use it to turn out usable APIs. The test team only has a use for a subset of those tests. Because if the dev team did it right, they passed all the TDD tests, and the test team’s role in that scenario is to look closely at what the developers might have missed. Integration is often beyond the control of an individual dev and/or API, for example, because many different clients may be integrating with it. Test teams, in a true TDD shop, fill the gaps with meaningful tests that evaluate the overall application quality.

    Security testing—at least static application security testing (SAST) and increasingly dynamic application security testing (DAST)—are also becoming a core part of the development process. This is as it always should have been. It is far easier to fix a security flaw (or any flaw) at the design/early code stage than in a testing process after deployment. The quality of security testing inputs is also increasing, making it far more viable. And finally, the tools are being effectively integrated. Being a developer who did a lot of security work because we often needed it and had no one to do it, I will say I get a little rush about being told right in my IDE about the security risks in my code. I write, I see, I revise where necessary, and I turn out better code. All without having to pause for security or bang heads over policy.

    To date, all this testing is separate, but I suspect, having vaguely the same location in the process of development, that testing will all occur together and the markets may even start to merge a bit—though merger of security testing and functional/performance testing tools is a bit of a stretch, it is more possible than in the past. In most orgs, even though developers have an increasing stake in both sets of testing, the target purchasers are still two different groups—security versus test—with different priorities. That’s why I think they aren’t likely to merge any time soon.

    While AI in most technology spaces is currently experiencing the same level of FUD as XML, Java, JavaScript or low-code was in the past, all super-hyped tools in tech do have a sweet spot. Security and testing are in that sweet spot for AI. Both in test generation and in results evaluation, it does it better, faster and with more coverage while giving staff a concrete set of results to pore over and improve security posture and/or code quality. I cannot say loudly enough that in terms of enabling the steps in the SDLC that have suffered from a lack of resources and timeline to write, execute and evaluate tests, AI is a game-changer.

    So, all of these things together mean that 2024 is a good time to take a hard look at your testing environment(s) and architecture, both traditional testing and security testing. Now that AI has made advanced automation a fact, it is time to consider implementing the level of testing we always knew we should have but never had the resources. Build it right into the build process and make it part of the organizational DNA—just like Agile is.

    If you could hire a specialist who would paw through your code and give you a list of the 25 things you need to fix first, you would do it. That is, essentially, what modern testing tools are. They can do more tests, evaluate more issues and side effects and filter down to the “No—this is really important!” list without costing the team a ton of time.

    And keep rocking it. We’re all sitting on bugs and vulnerabilities. I do hope we are way past denying that fact. Apps are big old complex monsters; these days, sometimes built from generations of technologies cobbled together. Of course they have issues. Even yours. So let’s take steps to clean it up, just a little bit. And keep clipping along.

  • Decidability: SLOs to Measure Quality Attributes

    Decidability: SLOs to Measure Quality Attributes

    You may be familiar with the “-ilities”–the quality attributes that define a system’s so-called “non-functional” parts. One that isn’t on the list is decidability, which is the ultimate quality attribute because it focuses on what truly matters to your team and your customers.

    You can’t do it all. Fast, cheap and good? Here in the real world, we have tradeoffs. Software is the mouth that keeps on eating, but it isn’t a digital free lunch. When we decide what not to do, we bring greater clarity and productivity to the team.

    Of course, the world is full of “shoulds.” We should make our product accessible, we know. It should be secure; it should be reliable. Of course. But how do you decide the limit of your shoulds? To enable decidability, you need context. To gain a contextual understanding of your most critical quality attributes, you must talk to real people and reason about your situation. There’s no other way.

    New Design, Wrong Decision

    I had an eye-opening experience that demonstrated firsthand how quality attributes don’t necessarily deliver business results. Six years ago, I was working on e-learning software for public schools. One of the main problems was user management. We were selling to massive institutions that often had only one administrator responsible for onboarding all the users across hundreds of schools. While school was out, they had to enter data for the schools, create classes and assign teachers.

    Once we had a design for improving the workflow (luckily, before implementing it), we took the mockups to a customer to show the fancy ideas we had built. They surprised us by showing us “what it looks like for them”–a spreadsheet! They were copying/pasting everything by hand.

    So we asked them, “What if we gave you a way to upload the spreadsheet? Would that work?” And, of course, the answer was yes! So we scrapped our plans to build a fancy form and made a drag-and-drop input field to validate and upload the sheet. The decision was clear.

    Measuring Decidability

    How do we measure the critical business drivers and turn them into decidability? The closest answer I’ve found is service level objectives (SLOs). These take the fuzzy -ilities and turn them into metrics and goals that can quickly inform decisions.

    SLOs are a universal tool for measuring quality attributes from an indicator expressed as a proportion of “good” events for a given quality like reliability or scalability. Decidability comes from aggregating the SLO data across your services, geographies, user groups and quality attributes over time.

    Comparing an indicator to a particular goal (tuned over time as the business and engineering context changes), we can derive an allotment of allowable “bad” time or events–an error budget. Setting an error budget for reliability, performance, scalability, etc., allows you to decide how poorly your service can operate and still keep customers happy. Further, you can find the breaking point of user behavior and defend your service proactively from violating the quality attributes you expect.

    Technical debt inevitably stems from all the -ilities you should focus on but couldn’t quite get to, given your situation. Some technical debt is deliberate and even prudent. To tackle technical debt, you must decide which new features to put on hold to make space in the roadmap. SLOs provide a data-driven baseline to make this decision quickly and accurately by telling you when your -ilities are within error budget or out of whack.

    Scaling Decidedly

    You might start on a small, single-purpose project that’s rarely used. But if that changes, you must decide how to invest, redesign and maintain it. Suddenly, you might care about performance, scalability, security and the rest. First, you need decidability so you can prioritize your work. So you gather the people together who care about the service, and you start to think and talk and stack rank. You use your instrumentation to build metrics, set clear goals and do what it takes to keep the system running within expectations.

    Automating action based on error budgets is the magic of setting SLOs. They aren’t just for sophisticated, highly available services. Error budgets are extremely valuable in systems when you aren’t sure what to do. SLOs create decidability in all the trade-offs of all the quality attributes and the functional needs of the app or service you’ve carefully built.

    If you put decidability first, the rest will fall into place.

  • Organized Architecture

    Organized Architecture

    I’ve mentioned IT pendulums plenty of times before, but today I’m going to use them to explore a different angle on IT: The age of containers and microservices. It’s time to ask yourself if the current architectural design of the overall product offering of IT is the best one you can afford to offer.

    Agile and the whole “developer is king” movement brought us a whole lot of independent app teams running off and doing their own thing. While the core of that movement was short-lived, we got a lot of good and bad out of it. The good included breaking the traditional “We change at glacial rates” process that some IT shops had—because a team absolutely wants the tools that will make their job easier, not the ones pre-approved by committee or whatever process a given org used. And the bad included the very things those of us who’ve seen this type of thing play out before warned about. Suddenly, we had critical apps and infrastructure relying on tools that one dev used in his hobby development. Sometimes that was good, and for sure it happened before Agile came along, but now we had an avalanche of it. Stuff that hadn’t been through security; often code/libraries/apps that no one outside the team (or the individual) knew was running in the organization. Some of that stuff is still happening.

    And then we hit true uptake of microservices. That drove us to break things up in non-traditional ways. A lot of good came of that process, but a lot of complexity did, too. Complexity shared across applications with a huge spike in API calls and operations with a spike in deployed processes and routing. Perhaps the worst thing to come out of the combination of the two is “Who cares why it failed? Just spin up another!” That movement is mostly dead (thank goodness), but it only died because of the number of high-profile “Oops” issues that it caused. It allowed attackers to go unnoticed; it caused paying customers to spread security details across the web; it caused users to suffer crashes that would have been fixed in any sane development environment … and some of those are still out there. Cases where there’s a script to restart an app or service every X hours because it crashes randomly. Not acceptable. Make all the excuses you like, but if you don’t know what’s causing the crash, you’re wrong to leave the app or service running.

    And we introduced a huge amount of complexity. Now we have double the infrastructure—one physical, one virtual (container or cloud). We have applications spread across a lot more systems (be they instances or physical systems) and often across platforms; even across data centers. We live in a very cool time, with some sweet stuff going on. But complexity is a very real problem we need to solve. One app/app portfolio may have limited complexity, but we still allow teams too much leeway in defining the environment the application lives in. We need to offer a happy medium—a world where there are options, but those options are defined, manageable, and secure.

    So we’re likely to see a resurgence in the importance of enterprise architecture teams again, with their focus being on organizing the mess we’ve made. Indeed, I know this is underway in a couple of Fortune 500 companies I stay in touch with. And I know of one organization that isn’t doing this and has all their UI functionality running through a single container … for multiple applications. That is no better architecture than each using a completely different UI toolset. So, I expect they’ll want coordinated solutions also. Just so something like that can’t happen again.

    You all are doing it, day in and day out. As I often say, just don’t let this bit drag you down. Confront it head-on, approach standardization with “Yes, where it is good; no, where it is not” as your credo. Security is the big one, but maintainability is another. Even with tools like Ansible at their disposal, managing 50 different environments is a bit much for ops if it could be done with five. And keep rocking it. Through a pandemic, meeting increased demand, giving users more and more. Thanks once again, from all of those users who don’t know to thank you.

     

  • Report: Software Quality Issues Costing U.S. Companies $2.08 Trillion

    Report: Software Quality Issues Costing U.S. Companies $2.08 Trillion

    A report published today by the Consortium for Information & Software Quality (CISQ) estimates poor software quality collectively cost companies in the U.S. an estimated $2.08 trillion in 2020.

    The report, co-sponsored by software quality and security tools provider Synopsys, attempts to calculate, at least theoretically, the cost of known software failures. The report identifies operational software failures, estimated to be $1.56 trillion, as the single largest cost. The next-largest cost contributor is unsuccessful development projects, which totals $260 billion, an estimated 46% increase since 2018, according to the report. The project failure rate remained steady, at about 19%, for over a decade, according to the report.

    Finally, legacy systems issues contributed $520 billion to costs in 2020, a significant decrease from the $635 billion these issues cost in 2018.

    That estimate does not include technical debt, which CISQ estimates at another $1.13 trillion. Technical debt costs have been growing at a rate of 14% per year since 2018, the report noted.

    Joe Jarzombek, director for government and critical infrastructure programs for Synopsys, said it’s apparent there’s not enough focus on quality assurance within software supply chains. However, as organizations begin to realize how dependent their digital business transformation initiatives are on quality software, business executives will be forced to shift focus.

    Unfortunately, most business executives today still don’t understand or appreciate how software is constructed. Most organizations don’t even have a bill of materials describing the components that make up an application, noted Jarzombek.

    That issue becomes especially problematic in the event of a security breach, because too many organizations can’t draw a correlation between vulnerabilities and the impact on specific components of an application, added Jarzombek. At the same time, however, cybercriminals are becoming more efficient at scanning applications to discover and exploit those vulnerabilities, said Jarzombek.

    Many organizations also don’t appreciate how adept cybercriminals have become at exploiting what are perceived as low-level vulnerabilities to distribute malware laterally across an application, said Jarzombek. There is a tendency to patch only the most critical vulnerabilities within an application, while the rest are left to future updates. When fixing low-level vulnerabilities is not prioritized, these vulnerabilities may remain over multiple release cycles.

    Ultimately, Jarzombek said, rather than treating security separately from the application development process, it should be addressed as part of a comprehensive approach to software quality assurance. The immediate challenge, however, is that not enough developers have access to the tools they need to address these ongoing cybersecurity issues.

    The impact of poor software quality on individual companies will vary widely, but poor software quality is itself a universal issue. Most software development teams are not deliberately ignoring these issues; instead, the processes currently employed to build software are not especially robust, mature or, for that matter, automated, leaving organizations vulnerable.

  • OverOps Allies With SonarQube To Improve Application Quality

    OverOps Allies With SonarQube To Improve Application Quality

    OverOps, a provider of tools for dynamically analyzing code at runtime, has partnered with SonarQube, a provider of static analysis tools for source code, to enhance application quality by unifying code analytics.

    Chen Harel, vice president of product for OverOps, said the two companies have collaborated to create a plugin through which DevOps teams can now more easily analyze source code before it gets deployed in a production environment.

    The goal is to equip DevOps teams with the tools required to assure application quality as more responsibility for maintaining applications continues to shift left on to the shoulders of developers, said Harel. In the absence of tools to analyze code, he noted, developers are not going to be able to identify, for example, potential vulnerabilities in source code and errors at run-time.

    The plugin also allows issues identified by OverOps at runtime to be fed back into the SonarQube tools for analyzing source code to identify issues that previously would not have been discovered, he added.

    Specifically, when the OverOps plugin is installed, it automatically creates an event rule for Java code based on new, critical, resurfaced and unique runtime errors. When a quality gate fails a release based on these criteria, users can view the issues directly within their SonarQube dashboard. OverOps then provides a direct link to the event analysis containing the full context behind the error, including the stack trace, variable state and system state.

    As a result, Harel said DevOps teams are going to be able to reproduce an issue much faster, which should result in much shorter times to remediation. Without such tools, DevOps teams can spend weeks reproducing an error that only takes a few minutes to actually fix.

    Harel said with both the OverOps and SonarQube tools included as part of the gates set up within a continuous integration/continuous delivery (CI/CD) platform, it becomes possible to substantially improve the overall quality of the applications being developed. That’s critical because developing applications faster often results in more errors having to be addressed, said Harel.

    There’s a lot of debate these days concerning the degree to which the adoption of DevOps might be increasing the total cost of IT. It’s a lot more expensive to fix an application after it’s deployed in a production environment than it is to address issues as they arise in the application development process. As such, there’s a lot more focus on code analytics to help ensure that DevOps processes are churning out not just more code faster but also higher quality applications.

    Of course, the level of DevOps sophistication tends to vary widely by organization. However, as more gates that address application quality assurance are erected on CI/CD platforms, the better the DevOps outcome will become. The challenge is making sure the DevOps culture being fostered prizes application quality as much as it does how much code is being written and deployed how fast.

    — Mike Vizard