Blog

How to create a QA report template (with template)

Applications face ongoing threats from the moment they are deployed. Attackers look for ways to exploit weaknesses, tamper with code, reverse-engineer binaries, or abuse runtime behavior to commit fraud and steal data. Traditional security practices such as periodic scans and one-time reviews still matter, but they only provide point-in-time visibility. Once software is released, it runs in environments you do not fully control, increasing exposure and making ongoing monitoring even more important.A continuous threat-monitoring strategy helps teams detect suspicious activity as it occurs rather than after the damage is done. When paired with runtime protections, monitoring gives teams a way to move from passive visibility to active response. Instead of simply recording attacks, they can identify risky behavior, trigger defined actions, and learn from the results.&nbsp;<h2>What is continuous threat monitoring?</h2>Continuous threat monitoring is the ongoing collection, analysis, and use of security-relevant information to support faster risk decisions and response. NIST defines continuous monitoring as maintaining ongoing awareness of security, vulnerabilities, and threats to support risk management decisions. In practice, that means teams do not wait for a weekly or monthly review cycle to understand what is happening. They monitor enough data, at a sufficient frequency, to detect meaningful threats and act in time.This concept is well established in infrastructure and network security, but it also applies to application runtime threats. For software teams, continuous monitoring can include watching for signs of tampering, unauthorized debugging, rooted devices, suspicious execution patterns, or other runtime conditions that suggest misuse or attack.&nbsp;<h2>How continuous threat monitoring works</h2> An effective continuous threat monitoring program usually operates as a loop:<ul><li>collect signals and runtime data </li><li>analyze those signals in context </li><li>trigger or support an appropriate response </li><li>record the outcome and refine future decisions </li></ul>That loop matters because monitoring alone is not enough. A mature program defines what counts as meaningful risk, what actions should follow, and how the team will use the results to improve policies and protections over time.<h2>Continuous threat monitoring vs. periodic scans: Key differences</h2>Periodic vulnerability scans and audits provide a snapshot of an application’s security posture at a given point in time. They are useful for identifying known issues, validating controls, and supporting compliance activities. But they are scheduled exercises, which means they can miss the activity that happens between assessments.Continuous monitoring evaluates signals as they occur or at a frequency that supports a timely response. That timing difference is the real distinction. Attackers do not wait for the next scan window. Continuous monitoring enables teams to detect suspicious behavior earlier, reduce dwell time, and respond before isolated events escalate into larger incidents.<h2>Why continuous threat monitoring matters for development teams</h2>Development teams are shipping software into increasingly complex environments that include cloud services, mobile devices, embedded systems, partner integrations, and user-controlled endpoints.That creates more opportunities for abuse, especially when applications run in environments that are difficult to trust fully. Runtime visibility helps teams understand how an application is being interacted with after release, including where defensive gaps may exist.For development and security teams, continuous threat monitoring can help:<ul><li>Protect intellectual property from reverse engineering and theft </li><li>Reduce fraud, abuse, and manipulation of application logic </li><li>Minimize outage risk and lower incident response churn </li><li>Improve visibility into how attacks actually unfold in production</li></ul>Threat monitoring is not valuable just because it produces more data. Its value lies in helping teams make better decisions and respond more effectively when suspicious behavior arises.&nbsp;<h2>5 stages of effective continuous threat monitoring</h2>A practical continuous threat monitoring program often includes five stages. These are not a formal universal standard, but they provide a useful way to structure how teams identify, validate, and respond to threats.<h3>Stage 1: Scoping</h3>During scoping, teams identify the applications, workflows, and data paths that carry the highest business and security risk. These assets are prioritized for monitoring because they are most likely to attract abuse or cause significant impact if compromised. Scoping also helps reduce noise by keeping the program focused on what matters most.<h3>Stage 2: Discovery</h3>In the discovery stage, tools observe runtime behavior and collect signals that may indicate suspicious activity. For application protection, those signals may include integrity failures, debugging attempts, rooted-device conditions, or signs of unauthorized modification. The goal is not to collect everything possible, but to collect the data most relevant to meaningful security decisions.<h3>Stage 3: Prioritization</h3>Not every event represents a real threat. Prioritization helps teams separate high-risk activity from benign or low-value events by considering factors such as potential impact, execution context, and recurrence. Without prioritization, even a well-instrumented monitoring system can overwhelm teams with noise.<h3>Stage 4: Validation</h3>Once monitoring surfaces a potentially meaningful event, the next step is validation. Teams confirm whether the signal reflects a real threat, a policy violation, or harmless behavior. Some organizations use testing, simulation, or controlled attack scenarios to verify that detection and response mechanisms behave as intended.<h3>Stage 5: Mobilization</h3>When a validated threat requires action, monitoring should support a fast, consistent response. That may include triggering automated protections, alerting the right team, or escalating the issue for further investigation. The event and its outcome should then feed back into the program so teams can refine thresholds, adjust policies, and improve future response.&nbsp;<h2>Building a continuous monitoring system: Core components</h2>A robust continuous threat monitoring system typically includes several core components that work together.Runtime signal collection Every monitoring program depends on data, but more data is not always better. Effective runtime monitoring focuses on collecting the signals most likely to indicate abuse or compromise while an application is running. That helps teams reduce noise and limit unnecessary overhead.Context-aware threat detection Suspicious-looking behavior is not always malicious. Effective monitoring evaluates events in context, including what happened, where it happened, how often it occurred, and whether it aligns with expected behavior. Context is what helps separate genuine threats from harmless anomalies and reduces false positives.Behavioral analysis and anomaly detection Teams are better positioned to detect misuse when they understand what normal application behavior looks like. Behavioral baselines can help surface deviations that suggest tampering, abuse, reverse engineering, or policy violations. This is especially valuable in runtime scenarios where single events may be ambiguous on their own.Automated response actions Monitoring becomes much more effective when it is connected to predefined response actions. Depending on the tool and the threat model, those actions might include blocking certain operations, restricting access to application features, logging security events, notifying the application, or interrupting suspicious execution. Automation matters most when teams need to react quickly to high-confidence threats.Alerting, visibility, and reporting A useful monitoring system provides stakeholders with clear visibility into what happened, how often it occurred, the likely risk, and the response. It also needs to filter out low-value noise so teams can focus attention where it matters. Too much raw signal, without prioritization, can lead to alert fatigue and reduce the system’s usefulness.&nbsp;<h2>How RASP complements continuous threat monitoring</h2>Runtime application self-protection (RASP) naturally fits into a broader continuous monitoring strategy. NIST describes runtime application self-protection as using runtime instrumentation to detect and block exploitation by leveraging information from inside the running software. OWASP also frames RASP-style resilience checks as defense-in-depth controls that raise the cost to attackers but do not replace secure design.That distinction is important. Monitoring tells you what is happening. RASP-style controls can help the application respond while the event is in progress. In practice, the best results come when runtime protections complement secure coding, testing, vulnerability management, and broader monitoring workflows rather than trying to replace them.Dotfuscator’s runtime Checks are an example of in-app runtime protection. PreEmptive documents that these Checks can detect and respond to unauthorized application activity, including debugging, tampering, and rooted-device conditions. Teams can also configure them to notify the application or trigger predefined responses when those conditions are detected.<h2>Common challenges in continuous monitoring (and how to solve them)</h2>Excessive overhead and noise An early or poorly tuned monitoring system can create too much operational overhead, too many low-value events, and unnecessary performance concerns. Heavyweight approaches are harder to maintain and can make teams less confident in the signals they receive.How to avoid it: Focus on lightweight, targeted monitoring that is tied to clear risk priorities. Limit collection to the signals that matter most, and review tuning regularly to keep the system useful without becoming noisy.Monitoring without action Visibility alone does not protect an application. Teams can end up logging events without assigning ownership, defining escalation paths, or deciding what should happen next. That creates the appearance of coverage without delivering much real protection.How to avoid it: Define response expectations in advance. Teams should know which events trigger alerts, which trigger automated action, who owns the investigation, and when thresholds or policies should be updated.Resource and skill constraints Even when tools automate much of the collection and response logic, continuous monitoring still requires judgment. Teams need to tune detections, understand likely threats, review outcomes, and refine policies as systems and risks evolve. That can strain organizations that lack dedicated security expertise or mature processes.How to avoid it: Use automation to reduce repetitive work, but back it up with training and clear ownership. Teams should understand the most relevant runtime threats, know how their tools behave, and have defined workflows for review and response.&nbsp;<h2>How PreEmptive supports continuous threat monitoring</h2>PreEmptive’s Dotfuscator is designed to protect .NET applications across desktop, mobile, cloud, and IoT scenarios with a layered defense approach. PreEmptive positions Dotfuscator as a multi-tiered application protection tool, and its documentation describes runtime Checks as protections that operate while the application runs, complementing protections applied to the application at rest.With PreEmptive, developers can:<ul><li>Protect intellectual property and trade secrets: Obfuscation makes compiled code harder to reverse engineer while preserving functionality. </li><li>Detect and react to runtime attacks: Dotfuscator Checks can detect conditions such as debugging, tampering, and rooted devices, and can be configured to notify the application or trigger a chosen response. </li><li>Reduce exposure of sensitive strings: Dotfuscator String Encryption hides user strings in assemblies so attackers cannot easily find them with simple search techniques. </li><li>Support broader monitoring workflows: PreEmptive documents that Checks can notify application code of validation results, which teams can use for custom handling, such as disabling features or sending third-party telemetry.</li></ul>PreEmptive fits best within a broader application security strategy. It strengthens runtime visibility and protection, but it does not replace secure development practices, testing, or vulnerability management.&nbsp;<h2>From monitoring to meaningful protection</h2>Security threats do not stop after release. In many cases, they become harder to predict because attackers can observe how an application behaves in real environments and look for ways to bypass its safeguards. Continuous threat monitoring helps teams detect suspicious runtime activity sooner, prioritize what matters, and reduce the impact of attacks through faster response.When continuous monitoring is paired with runtime protections, teams gain more than visibility. They gain the ability to react to high-confidence threats while also learning from what happened. That combination can make application security programs more practical, more responsive, and more resilient over time.To see how PreEmptive tooling can support your application protection strategy, start your free trial today.

Continuous Threat Monitoring for Application Security

How TestRail rebuilt itself around AI—without losing the test management discipline that put us here in the first place.<h3>The takeaway in 30 seconds</h3><ul><li>53% of code is now AI-generated or AI-assisted, and 61% of QA teams report moderate-to-dramatic increases in testing demand because of it (2026 Sembi Software Quality Pulse Report). </li><li>Generic AI in QA tooling is underdelivering—only 17.2% of teams call its impact significant. Most existing AI assistants weren’t built for testing. </li><li>TestRail by Sembi is now an AI-driven quality intelligence platform, powered by Sembi IQ—not just test management software. </li><li>The TestRail AI portfolio today: AI test case generation, AI script generation, and the AI evaluation template. Coming in June 2026: AI test prioritization (early access). </li><li>The throughline across every capability: AI handles velocity; testers keep judgment. </li></ul><h2>The job didn’t change, but a lot of other things did: How TestRail responded</h2>Software is being written faster than QA can absorb it. According to the <a data-mce-href="https://www.testrail.com/first-edition-software-quality-pulse-report/" href="https://www.testrail.com/first-edition-software-quality-pulse-report/">2026 Sembi Software Quality Pulse Report</a> (SSQPR)—a survey of 3,800 QA and security professionals—an estimated 53% of organizational code is now AI-generated or AI-assisted. The same report finds that 61% of teams are seeing moderate to dramatic increases in QA testing demand because of that AI-generated code. Regression suites have grown past anyone’s ability to run them serially, and release cadences keep compressing.QA still owns the same outcome—confidence at release—but the path there now runs through an exploding test surface, ambiguous AI behavior, and a stubborn capacity problem (44.7% of QA teams report being understaffed, per the SSQPR).Over the last 18 months we built AI into our platform to address these modern QA challenges, and help address the heaviest parts of it—drafting test cases from your requirements, scaffolding automation from those cases, evaluating the AI features your team is shipping, and ranking test runs by real risk—each one working from the coverage and defect history already in your instance, not generic internet data.&nbsp;These capabilities run on Sembi IQ, our quality AI engine. Same discipline, done faster and more reliably, minimizing technical debt. Here’s what it looks like, why we’re building it this way, and what’s coming next.<h2>Why generic AI keeps failing QA teams</h2>The first wave of AI in QA tooling wasn’t built for QA, it was general-purpose AI bolted onto test workflows. The result was familiar: brittle test cases that ignored coverage, automation scripts that didn’t reflect real frameworks, and “AI suggestions” that sounded confident but missed the point of regression strategy.The pattern shows up in the numbers. Sixty percent of QA teams report some improvement from AI in testing, but only 17.2% describe the impact as significant. The Sembi Pulse Report calls AI’s effect on QA “uneven”—plenty of investment, plenty of activity, but no widespread step-change in outcomes. AI that hasn’t been trained on testing creates more cleanup work than it saves.&nbsp;“AI is showing what is wrong within QA—implementing it on top&nbsp; &nbsp; &nbsp; of&nbsp; &nbsp; &nbsp; an unstable architecture, and AI really highlights the cracks in their systems.” &nbsp;— Patrícia Duarte Mateus, TestRail Solution Architect (2026 Sembi Software Quality Pulse Report). A general-purpose model can write something that looks like a test case. It can’t tell you which test already covers that path, whether the assertion is reproducible across environments, or whether the data violates residency rules.QA isn’t a writing task. It’s a system—test cases tied to requirements, defects tied to runs, traceable across releases. A model trained on the open internet wasn’t trained on any of that.TestRail’s AI was. That’s the difference.<h2>What “AI built for testing” actually means</h2>When we talk about AI-first quality, we mean three things.Domain-trained. Our AI is built on Sembi IQ—the underlying intelligence layer powering every AI capability across the Sembi family of software quality and security tools. It understands the difference between a smoke test and a regression run, between a coverage gap and a duplicate, between a flaky failure and a real defect. It was trained on testing context, not bolted on after the fact.Workflow-native and context-aware. Every AI capability lives inside the project, the run, the test case—wherever testers already work. Nothing sits in a separate UI demanding context-switching. Jira Issue Connect, ADO integration, cross-project reporting, and enterprise access controls are the foundation, not the afterthought.Tester-controlled. AI proposes; the tester decides. Every output is reviewable, editable, and reversible. Human judgment is the final word on what gets shipped to customers, because that’s how QA actually works in regulated environments, in audit-bound industries, and in any team that’s been burned by a confident-sounding hallucination.This isn’t a single feature; it’s a posture—and it shapes every AI capability we ship.<h2>The AI portfolio, in one frame</h2>Three capabilities make up TestRail’s AI surface today, with a fourth arriving in June. Each one answers a different question, but they share the same throughline: handle the velocity, give back the judgment.<h3>AI test case generation </h3>Turn a requirement, a user story, or a Jira ticket into structured, coverage-aware test cases in seconds. Our three-step workflow gives testers full visibility into what’s being proposed and why—so generated cases land as drafts to refine, not noise to clean up. Teams using it report up to 90% faster authoring on net-new test sets.<h3>AI script generation</h3>Production-quality automation scaffolding straight from your existing test cases, in the framework you already use. Roughly 30 seconds of generation replaces 30 to 45 minutes of boilerplate setup. It’s scaffolding, not a plug-and-play replacement for your engineers—exactly the contract QA actually wants from AI in automation.<h3>AI evaluation template</h3>Go beyond pass/fail for the parts of your product where pass/fail doesn’t apply. AI features are non-deterministic—the same prompt can produce different outputs, and quality is a spectrum. Our evaluation template adds Quality Rating fields, multi-dimensional scoring, and a Quality Insights dashboard inside TestRail, so AI features get the same evaluation rigor as everything else you ship. We use this template internally to evaluate our own AI before it ships—it’s tried and tested, not theoretical.<h3>AI test prioritization (coming in June)</h3>When regression suites grow past the time you have to run them, the question stops being what to test and becomes what to test first. AI test prioritization ranks your suite using the data you already own—your own execution history, defect patterns, and flaky-test signals—combined with semantic context about what each test actually covers.&nbsp;It’s an approach few tools take: the AI isn’t guessing from the outside; it’s surfacing the risk patterns already sitting in your instance, so the first hour of regression catches the defects that matter, not the ones you’ve already squashed eleven times. And it shows its work—every ranking comes with the reasoning for why a test landed where it did.&nbsp;That’s the answer to the confident-sounding hallucination problem, and the reason the judgment stays yours: nothing gets ranked without a reason you can check. (Coming next month with CLI integration to follow.)Behind all four sits the same engine, the same data model, the same audit trail.<h3>DesignWise test optimization (Sembi sister product, works alongside TestRail)&nbsp;</h3>Where TestRail’s AI works on the test cases that already exist, DesignWise comes upstream—into test design before tests are written. It uses AI-assisted coverage modeling to generate the smallest set of test cases that cover the most ground, then exports Gherkin scenarios that drop cleanly into TestRail and Ranorex. Teams report ~50% less test creation time, ~60% fewer tests to maintain, and 1.5–2x fewer production defects. It’s the Sembi family’s answer to “are we testing the right things?”—the question TestRail picks up once the tests are in the system.&nbsp;One platform. One source of truth for AI-era quality.<h2>The throughline: velocity from the model, judgment from the team</h2>Every AI capability we ship answers the same question—where should QA spend its judgment?The answer, every time, is the high-stakes, ambiguous, contextual decisions humans are best at. AI handles the boilerplate, the volume, the pattern-matching, the first-draft work. Testers handle the rest. That split is what makes AI usable inside a QA org—not the demos, the proof points, or the model size.It’s also why TestRail is rebuilding around AI without losing what testers came here for: control, traceability, and confidence in the audit trail.<h2>What’s next</h2>AI test prioritization is the next chapter of this story, not the end of it. It’s the most ambitious AI capability we’ve shipped because it does the hardest thing—it helps you decide what to do first. We’ve spent the last year preparing for that moment: training models on the right data, embedding evaluation rigor into our own dev cycles, and making sure prioritization arrives with the same human-in-the-loop posture as everything else.It’s also arriving at the right moment. AI now leads QA and security investment priorities at 35.7%—well ahead of every other category, per the Sembi Pulse Report. The bet QA leaders are making on AI is real. The question is which AI investments actually pay off. We built prioritization to be one of them.You’ve always used TestRail to do this work—author your cases, organize your suites, decide what to run, and judge what’s working. Our new AI-driven platform doesn’t change the job. It helps you do it better, faster, and more confidently, working from the test data you already own. Same discipline you’ve trusted for years—now the system of record for quality at scale.<h2>Want to go deeper?</h2><ul><li>See the <a data-mce-href="https://www.testrail.com/ai-evaluation-template/?utm_source=partners_website&amp;utm_medium=partners&amp;utm_campaign=dube&amp;utm_term=&amp;utm_content=ai-evaluation-template" href="https://www.testrail.com/ai-evaluation-template/?utm_source=partners_website&amp;utm_medium=partners&amp;utm_campaign=dube&amp;utm_term=&amp;utm_content=ai-evaluation-template" data-mce-selected="inline-boundary">AI Evaluation Template</a> in action. </li><li>Read how AI <a data-mce-href="https://www.testrail.com/blog/ai-test-automation/" href="https://www.testrail.com/blog/ai-test-automation/">accelerates automation script development.</a> </li><li>Catch up on the <a data-mce-href="https://www.testrail.com/blog/testrail-9-5/" href="https://www.testrail.com/blog/testrail-9-5/">TestRail 9.5 release</a> and the AI test case generation rollout. </li><li>Read the full 2026 Sembi Software Quality Pulse Report. </li><li>Watch this space for AI test prioritization early access in June.</li></ul><h2> Frequently asked questions</h2>What is AI test prioritization? AI test prioritization is a TestRail capability that ranks tests in a regression suite by predicted risk, using historical execution data, defect patterns, and semantic context. It tells QA teams what to run first when there isn’t time to run everything. AI test prioritization begins rolling out in mid-June.How is TestRail’s AI different from a general AI assistant? TestRail’s AI is purpose-built for QA. It’s trained on test management context (functional vs. regression vs. smoke vs. exploratory), runs inside QA workflows (Jira-connected, audit-aware, role-controlled), and keeps testers in the loop on every output. General AI assistants weren’t built with QA’s vocabulary, constraints, or compliance posture in mind—and the data shows it: per the 2026 Sembi Pulse Report, only 17.2% of QA teams describe the impact of AI on testing as significant.What is the AI evaluation template? The AI evaluation template is a TestRail feature designed to evaluate non-deterministic AI features—where the same input can produce different outputs and quality lives on a spectrum. It adds Quality Rating fields, multi-dimensional scoring, and a Quality Insights dashboard, all inside TestRail. It’s the first AI evaluation template purpose-built into a test management platform.Does TestRail’s AI train on customer data? No. Sembi IQ does not train its models on customer data—it sends the LLM only what you choose to share, doesn’t store it, and keeps it encrypted in transit. Using your own historical execution data to prioritize your own test runs is a separate thing: it happens inside your instance to rank your tests, and it is not model training.Which AI capabilities are available today? AI test case generation, AI script generation, and the AI evaluation template are generally available today on TestRail Cloud. AI test prioritization begins rolling out in mid-June.How much code is now AI-generated, and what does that mean for QA? According to the 2026 Sembi Software Quality Pulse Report, an estimated 53% of organizational code is now AI-generated or AI-assisted, and 61% of QA teams report moderate to dramatic increases in testing demand as a direct result. AI-driven coding velocity is outpacing the QA capacity built to validate it—which is the gap TestRail’s AI portfolio is engineered to close.About the dataStatistics in this article are drawn from the 2026 Sembi Software Quality Pulse Report, a survey of 3,800 QA engineers, security professionals, developers, and engineering leaders conducted in late 2025 and early 2026. Sembi is the parent company of TestRail.Explore TestRail’s AI capabilities →Visit <a data-mce-href="https://www.testrail.com/ai" href="https://www.testrail.com/ai">testrail.com/ai</a> to see every AI capability in one place, watch the latest demos, and stay first in line for AI test prioritization early access.

From Test Management to Quality Intelligence: Inside TestRail’s AI-First Era

<figure id="figure-kychf0zccmqazghb2"><img id="kychf0zccmqazghb2" src="https://cdn.durable.co/blocks/2clVOzu5NZzxHdaEoBXsGKUREfRUqLtJQ8hB1wQNhIwDccTmJBoX2ZfD8PWnZsGA.png" alt="" title="" class="object-cover bg-gray-100 rounded-2xl md:rounded-3xl lg:rounded-4xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/2clVOzu5NZzxHdaEoBXsGKUREfRUqLtJQ8hB1wQNhIwDccTmJBoX2ZfD8PWnZsGA.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure>Enterprise applications are where sensitive business logic runs and customer data moves. A single exploitable flaw can cascade into an incident that takes months to contain. In large environments, the problem is rarely a total lack of security testing. It is uneven coverage, inconsistent enforcement, and too little context to prioritize what matters first.Key takeaways:<ul><li>Enterprise application security fails when it’s treated as a compliance exercise rather than a continuous practice. </li><li>Injection attacks, broken access controls, and vulnerable dependencies each require a different detection method; no single tool can cover all of them. </li><li>SAST, SCA, DAST, and IAST operate at different layers of the stack, and the gaps between them are where the most damaging vulnerabilities live. </li><li>Kiuwan helps reduce that gap with Code Security for SAST and Insights for SCA, plus IDE and CI/CD integrations, compliance mappings, and broad language support for heterogeneous portfolios. </li></ul><h3>Why enterprise application security keeps failing at scale</h3>Security programs fail in a specific way that audits don’t catch. A team has SAST configured for two application teams. Twelve others shipped code last quarter without any coverage. The security dashboard looks healthy because it only shows what’s being scanned. What isn’t scanned doesn’t show up as a gap; it just shows up later as a breach.Enterprise environments run hundreds of applications across dozens of teams, languages, and deployment pipelines. Security coverage that works for one team rarely extends consistently across all of them. The attack surface grows faster than the security program can track it, and the teams adding the most surface area are often the ones farthest from security oversight.OWASP guidance continues to highlight categories such as broken access control, injection, security misconfiguration, and software supply chain risk. These issues persist not because teams are unaware of them, but because no single testing method is designed to catch all of them.<h3>Common enterprise application security threats and how they reach production</h3>Five vulnerability categories consistently show up in enterprise application security incidents. Understanding how each one reaches production is where detection and prevention start.Injection attacksSQL injection, command injection, and LDAP injection share a common root: unsanitized input that reaches an interpreter. The vulnerability lives in the code itself, specifically in how developers handle user-supplied data before it interacts with back-end systems.SAST tools analyze source code without executing it, tracing data flows from input sources to sensitive sinks. With proper tuning, they can surface parameterization failures, insecure string concatenation, and missing input validation at the point where the flaw was introduced. Running SAST in the IDE means developers see injection risks while they’re still writing the affected method, not after a penetration tester finds it in production.Broken access controls Broken access controls are the most consistently exploited vulnerability category in enterprise applications. The failure pattern is usually subtle: a role check fires correctly on page load but doesn’t protect the underlying API endpoint. Or a direct object reference that the UI never exposes becomes accessible when you construct the request manually. Authorization logic that works in the happy path breaks the moment an attacker steps outside it.Dynamic Application Security Testing (DAST) is often useful in this context. By simulating real HTTP requests against a running application, DAST can probe whether authorization checks hold up when request parameters are manipulated, when session tokens are replayed, or when endpoints are accessed directly. SAST can flag missing access control decorators or obvious authorization gaps. Still, DAST can help detect authorization bypass scenarios that only surface when requests are constructed outside the normal application flow.Cross-site scriptingXSS vulnerabilities stem from output rendering flaws: user-controlled data reaching the DOM or HTTP response without proper encoding. Stored XSS lives in the database until it executes in a victim’s browser. Reflected XSS travels through URLs. DOM-based XSS bypasses server-side controls entirely and manipulates the document object model client-side.SAST covers the server-side rendering cases well, flagging unencoded output and missing context-aware escaping, but its coverage is only as good as the framework it understands. A custom templating layer or a legacy rendering path that predates the framework’s built-in escaping will often produce false negatives. Teams that rely solely on SAST for XSS coverage and run custom or legacy rendering code should treat that assumption as a gap until it’s verified with DAST. DOM-based XSS requires DAST or manual review regardless, since the vulnerable execution path lives in client-side JavaScript that static analysis of server-side code won’t reach.Misconfigured cloud servicesCloud misconfigurations don’t live in application code. They live in infrastructure definitions, IAM policies, storage bucket permissions, and network security group rules. An S3 bucket with public read access, an overprivileged service account, or an exposed management interface creates application security risk without a single vulnerable line of code.Infrastructure-as-code (IaC) scanning and cloud security posture management (CSPM) tools are the primary controls here. IaC scanning catches misconfigured Terraform or CloudFormation templates before they’re deployed. CSPM continuously monitors cloud environments for configuration drift against security benchmarks. DAST can help surface exposed endpoints as part of broader application testing, but it’s not a substitute for dedicated cloud configuration controls. The key is treating cloud configuration as part of your application security program, not as a separate workstream owned by someone else.Vulnerable third-party libraries Modern applications rely heavily on open-source components. Every dependency you pull in carries its own vulnerability and maintenance history, and new CVEs continue to emerge in widely used packages. Most teams don’t find out a component is vulnerable until a scanner flags it, and by then it’s already been running in production for months.Software composition analysis (SCA) is purpose-built for this problem. SCA helps maintain an inventory of your open-source dependencies, maps components to known vulnerabilities from sources such as the NIST National Vulnerability Database, and flags components that may need upgrading or review.Without SCA, you’ve got no reliable way to know whether the version of Log4j, OpenSSL, or requests you’re running carries a known critical flaw. Manual tracking doesn’t scale across enterprise codebases.<h3>Why enterprise application security requires more than one testing method</h3>Most tool consolidation decisions get made at the procurement level, where “we already have a SAST tool” closes the conversation before anyone asks what it’s missing. The problem is that each method operates at a different layer of the stack, and the gaps between them are exactly where the most damaging vulnerabilities live.Here’s how they relate:<ul><li>SAST analyzes source code, bytecode, or binaries without execution. It’s fast, it integrates into the development workflow, and it finds vulnerabilities early when they’re cheapest to fix. It can’t see runtime behavior, environment configuration, or how components interact once deployed. Teams that rely solely on SAST often have solid coverage of their own code but no visibility into what their dependencies are doing. </li><li>SCA scans dependencies against known vulnerability databases. It’s the only reliable way to maintain visibility into open-source risk at scale. It doesn’t analyze how those dependencies are used or whether the vulnerable code path is actually reachable in your application, so SCA findings need context before they can be added to a prioritized remediation queue. </li><li>DAST runs against a live application, simulating attacker behavior. It surfaces issues that only appear at runtime, including auth bypass scenarios and configuration exposures such as missing security headers and weak TLS settings. It requires a deployed environment and doesn’t give you direct line-of-code findings that developers can act on immediately. DAST is most useful when it tests the application the way an attacker would, not just crawl it the way a user would. </li><li>Interactive application security testing (IAST) instruments the application during testing, monitoring code execution in real time. It bridges SAST and DAST by providing accurate, context-aware findings. It requires an agent deployed in the test environment and works best when there’s already solid automated test coverage to exercise the application paths you want monitored. Findings typically have lower false-positive rates than traditional static scanning, though results depend on configuration and test coverage depth. The tradeoff is deployment complexity: IAST adds instrumentation overhead that not every team is set up to manage.</li></ul> Relying on SAST alone means you’ll miss runtime access control failures, cloud configuration issues, and known CVEs in your dependencies. Relying on DAST alone means you’ll find behavioral issues but lack the code-level context developers need to fix them efficiently. Using SCA without SAST means you’re tracking dependency risk but not catching the injection flaws and logic errors your own developers are introducing.<h3>Operational challenges that weaken enterprise application security</h3>Deploying multiple methods doesn’t automatically solve the problem. The real integration challenge is getting findings from all of them into a shared context where they can be prioritized together. That’s a workflow problem as much as a tooling problem. Consistent enforcement becomes the bottleneck.Tool sprawl and alert fatigue Picture a security engineer on a Monday morning trying to answer one question: Are we exposed to the CVE that dropped over the weekend? To find out, they’ve got to check the SAST dashboard, cross-reference the SCA report, pull the DAST results from last week’s staging run, and figure out whether the affected component is actually called in any of the production code paths. That’s an hour of correlation work before any remediation starts. Multiply that by every significant CVE disclosure and every sprint cycle, and you understand why vulnerability backlogs grow even when teams are working hard.Teams spend significant time dealing with false positives from security scanning tools. When findings from four separate tools arrive without shared context about priority or overlap, remediation slows to a triage exercise. The backlog grows regardless of how many tools you’re running.Prioritization by exploitability, not raw severity A critical-severity CVE in a library function that’s never called in your application is not your most urgent problem. A high-severity injection flaw in an authentication endpoint that’s exposed to the internet probably is. CVSS scores don’t make that distinction. They measure inherent vulnerability characteristics, not exploitability in your specific environment.The gap matters because most enterprise codebases generate more findings than any team can remediate in a sprint cycle. Without a way to separate what’s actually reachable and exposed from what’s theoretically severe, teams either freeze on triage or work through findings in the wrong order. Effective prioritization requires correlating static analysis results with runtime context and deployment information. Most teams that skip that step don’t realize they’re doing so. They just wonder why the same vulnerability classes keep reappearing sprint after sprint.Compliance frameworks as a testing strategy anchor The teams that get the most value from compliance frameworks are the ones that treat them as a coverage floor, not a finishing line. PCI-DSS scope reduction is a case in point: teams that architect systems to minimize what falls in scope rather than to maximize security coverage pass their audits and still carry exploitable gaps. Their interpretation is what failed them.OWASP Top 10, PCI-DSS, and NIST are still worth anchoring to. PCI-DSS requires vulnerability scanning and penetration testing for applications that handle cardholder data. NIST SP 800-53 specifies security testing as part of a broader control framework. The OWASP Top 10 reflects where real-world exploitation is concentrated, making it a useful signal for prioritizing testing investment beyond what compliance strictly requires.If your program can demonstrate coverage aligned to all three frameworks, you are in a stronger position to reduce common blind spots and support audit readiness. That still does not guarantee security, but it gives teams a more defensible baseline.Kiuwan supports compliance-oriented reporting and policy alignment across standards such as OWASP, CWE/SANS, PCI DSS, CERT, NIST, MISRA, and ISO-based quality models, making compliance reporting easier without turning compliance into the whole security strategy.<h3>How to integrate enterprise application security into CI/CD pipelines</h3>A finding caught in the IDE takes minutes to fix. The same finding caught in a pre-production gate takes a sprint to schedule and another to fix. The same finding takes months and a forensics team after the breach. The cost of a vulnerability doesn’t stay constant as it moves through the SDLC. It compounds. Teams that understand this don’t run security as a separate late-stage gate. They push testing as close to the point of code creation as the tooling allows.Teams that get this right typically follow a consistent pattern:<ul><li>Run SAST and SCA on every pull request, with findings scoped to the changed code so developers aren’t triaging an entire codebase backlog on every commit. </li><li>Set quality gates that block merges for high-severity findings in critical code paths, while routing lower-severity findings to a tracked backlog. </li><li>Run DAST against deployed staging environments on a scheduled basis or as part of pre-production release gates. </li><li>Configure notifications to route findings to the team that owns the affected component, not to a central security inbox that no one has bandwidth to monitor.</li></ul><h3> How Kiuwan helps reduce tool sprawl in enterprise application security</h3>Kiuwan takes a broader platform approach to tool sprawl and coverage consistency. Code Security covers SAST, while Insights covers SCA, giving teams a more unified way to review application and dependency risk without pretending one testing method can do everything.<ul><li>Unified visibility across proprietary and open-source risk: within the Kiuwan platform, Code Security analyzes your proprietary code, while Insights analyzes open-source dependencies. Together, they give teams broader visibility into application risk without forcing separate workflows for every layer. </li><li>30+ language support across heterogeneous portfolios: a financial services firm running Java microservices alongside a COBOL core banking system cannot afford a SAST tool that only covers half the codebase. Kiuwan supports 30+ languages, including both modern and legacy environments, helping teams apply static analysis more consistently across large portfolios. </li><li>Direct IDE and CI/CD integration: Developers can run Kiuwan analysis from IDEs such as Visual Studio, VS Code, Eclipse, and JetBrains tools, then automate scans through CI/CD workflows using tools such as Jenkins, GitLab, and Azure DevOps. That helps teams surface issues earlier, while the code is still fresh and easier to fix. </li><li>Prioritized findings over raw noise: one of the biggest problems in enterprise AppSec is not just finding vulnerabilities, but deciding what to fix first. Kiuwan Code Security is designed to help teams detect and prioritize vulnerabilities, review findings in a dashboard, and apply quality gates and remediation guidance earlier in the workflow. </li><li>Compliance mapping built in: Kiuwan maps findings to OWASP Top 10, CWE/SANS Top 25, PCI DSS, CERT, MISRA, NIST, and ISO/IEC 25000, making compliance reporting easier without turning compliance into the whole security strategy.&nbsp;</li></ul>Engineering managers and IT leaders who need visibility into codebase health over time, not just point-in-time vulnerability counts, can extend that foundation with Kiuwan Insights for software composition analysis and SBOM generation, and with Kiuwan Code Quality and Governance for software analytics, policy visibility, technical debt reduction, and portfolio-level reporting.<h3>Build an enterprise application security program that reduces breach risk</h3>Passing an audit and preventing a breach are two different outcomes, and most enterprise security programs are optimized for the wrong one. Kiuwan helps teams close that gap by combining Code Security for SAST with Insights for software composition analysis, alongside the IDE and CI/CD integrations developers already use. If you are ready to see what is currently exposed in your applications, <a data-mce-href="https://www.kiuwan.com/request-a-trial/?utm_source=partners_website&amp;utm_medium=partners&amp;utm_campaign=dube&amp;utm_term=&amp;utm_content=free_trial" href="https://www.kiuwan.com/request-a-trial/?utm_source=partners_website&amp;utm_medium=partners&amp;utm_campaign=dube&amp;utm_term=&amp;utm_content=free_trial">start your free Kiuwan trial</a>.

Enterprise Application Security: How to Close the Gaps That Lead to Breaches

Key takeaways: Code quality spans five dimensions (readability, maintainability, efficiency, security, and testability), each affecting different stakeholders. Teams improve code quality through coding standards, code reviews, automated testing, and regular refactoring. Static code analysis also automates enforcement by tracking metrics like complexity and duplication. Modern platforms combine SAST, SCA, and quality governance to streamline the entire process.Improving code quality requires combining proven development practices with automated enforcement tools.&nbsp;<a data-mce-href="https://stripe.com/files/reports/the-developer-coefficient.pdf" href="https://stripe.com/files/reports/the-developer-coefficient.pdf" target="_blank" rel="noopener">According to Stripe’s Developer Coefficient report</a>, developers estimate spending 13.5 hours per week on technical debt, plus 3.8 hours per week addressing poor code quality, totaling 17.3 hours per week (about 42% of their work time).This guide covers:<ul><li>The five dimensions of code quality and their impact </li><li>Foundational practices: standards, reviews, testing, and refactoring </li><li>Static code analysis and key metrics to track </li><li>Tooling integration in CI/CD pipelines and IDEs </li><li>Unified platforms for SAST, SCA, and quality governance</li></ul><h3>The five dimensions of code quality</h3>Code quality isn’t a single metric. It spans five interconnected dimensions that affect different stakeholders in different ways:<ul><li>Readability: How quickly developers can understand code they didn’t write. </li><li>Maintainability: How easily you can modify code without breaking existing functionality. </li><li>Efficiency: Runtime performance and resource consumption. </li><li>Security: Vulnerabilities that could expose data or systems. </li><li>Testability: How well the code supports automated validation.</li></ul>Each dimension creates its own problems. Unreadable code burns developer hours every day. Poor maintainability increases the risk of bugs reaching users. And when code is hard to test or inefficient to run, leadership sees timelines slip.Improving code quality means addressing all five dimensions, not just the ones causing immediate friction.<h3>Foundational practices every team needs</h3>Before investing in tooling to improve code quality, teams need foundational practices in place. These create the baseline discipline that tools build on.Coding standards Coding standards maintain a consistent style across the codebase. When everyone follows the same conventions for naming, formatting, and structure, developers can read each other’s work with less cognitive friction. Standards reduce the mental load of context-switching between different coding styles. That matters more than any individual preference.Code reviews Code reviews catch problems early and spread knowledge across the team. A second set of eyes can spot logic errors, security gaps, and maintainability issues before they reach production. Reviews also transfer domain knowledge, expose junior developers to senior thinking, and prevent any single person from becoming a bottleneck on specific modules.Automated testing Automated testing validates behavior at every layer of your application. Structure tests as a pyramid: fast unit tests at the base, integration tests in the middle, and slower end-to-end tests at the top. Unit tests run in milliseconds and catch regressions quickly. Integration tests verify that components work together correctly. End-to-end tests confirm user-facing workflows but run slowly, so you typically need fewer of them. This pyramid gives you fast feedback loops without burning hours waiting for test suites to run to completion.Regular refactoring Regular refactoring keeps small annoyances from hardening into architectural problems. When you refactor continuously, you address code smells while they are still easy to fix. Wait too long, and minor issues compound into technical debt that requires major rewrites. This makes refactoring part of normal development work rather than a separate phase.<h3>Static code analysis: The automated quality engine</h3><a data-mce-href="https://www.kiuwan.com/static-code-analysis/" href="https://www.kiuwan.com/static-code-analysis/" target="_blank" rel="noopener">Static code analysis</a> reads your source code without executing it. It helps flag bugs, code smells, security issues, and violations of standards before they reach production. Unlike manual code reviews, static analysis runs consistently across your codebase and can catch patterns humans might miss.<h3>Key metrics to track</h3>A few key metrics can help you track whether your codebase is getting healthier or accumulating technical debt over time:Cyclomatic complexity Measures how many independent paths exist through a function. Higher complexity typically means more potential bugs and harder testing, and often signals a need for refactoring.Code duplication Tracks repeated logic across your codebase. Higher duplication increases maintenance costs because changes and fixes must be repeated across multiple places.Maintainability index Combines factors like complexity, code volume, and comment density into a single score. Watching this metric over time can show whether refactoring is improving maintainability or whether debt is accumulating.<h3>Static analysis vs. SAST</h3>General static analysis focuses on code health: bugs, maintainability issues, and standards violations. SAST (Static Application Security Testing) prioritizes security vulnerabilities.<a data-mce-href="https://www.kiuwan.com/blog/sast-tools/" href="https://www.kiuwan.com/blog/sast-tools/" target="_blank" rel="noopener">SAST tools</a> often use techniques like taint analysis and control flow analysis to catch issues such as:<ul><li>Injection flaws </li><li>Authentication weaknesses </li><li>Insecure data handling </li><li>Other security-specific issues </li></ul>Both approaches analyze code without running it, but SAST prioritizes exploitable risk while general static analysis prioritizes broader code quality and maintainability.<h3>Automating code quality with the right tools</h3>Static analysis works best when it’s integrated into your existing development workflow, not bolted on as an afterthought. Different tool types serve different purposes:<table style="border-collapse: collapse; width: 100.002%;" border="1" data-mce-style="border-collapse: collapse; width: 100.002%;"><tbody><tr><td style="width: 24.8442%;" data-mce-style="width: 24.8442%;">Tool Type</td><td style="width: 24.8442%;" data-mce-style="width: 24.8442%;">Primary Focus</td><td style="width: 25.6255%;" data-mce-style="width: 25.6255%;">Examples</td><td style="width: 24.8442%;" data-mce-style="width: 24.8442%;">What It Catches</td></tr><tr><td style="width: 24.8442%;" data-mce-style="width: 24.8442%;">Linters</td><td style="width: 24.8442%;" data-mce-style="width: 24.8442%;">Coding standards &amp; style</td><td style="width: 25.6255%;" data-mce-style="width: 25.6255%;">ESLint, Pylint, RuboCop</td><td style="width: 24.8442%;" data-mce-style="width: 24.8442%;">Style violations, simple bugs, formatting issues</td></tr><tr><td style="width: 24.8442%;" data-mce-style="width: 24.8442%;">Static Analyzers</td><td style="width: 24.8442%;" data-mce-style="width: 24.8442%;">Code quality &amp; complexity</td><td style="width: 25.6255%;" data-mce-style="width: 25.6255%;">SonarQube, CodeClimate</td><td style="width: 24.8442%;" data-mce-style="width: 24.8442%;">Unused variables, unreachable code, cyclomatic complexity</td></tr><tr><td style="width: 24.8442%;" data-mce-style="width: 24.8442%;">Security Scanners</td><td style="width: 24.8442%;" data-mce-style="width: 24.8442%;">Vulnerabilities &amp; risk</td><td style="width: 25.6255%;" data-mce-style="width: 25.6255%;">Checkmarx, Veracode, Kiuwan</td><td style="width: 24.8442%;" data-mce-style="width: 24.8442%;">Injection risks, insecure cryptography, hardcoded secrets, and unsafe patterns</td></tr></tbody></table><h3>CI/CD pipeline integration</h3> Integrating these tools into your CI/CD pipeline helps block problematic code before it reaches production.&nbsp;When a pull request triggers your build pipeline, static analysis runs automatically. If the code violates your quality gates (for example, critical vulnerabilities, complexity thresholds, or policy violations), the build can fail. This prevents bad code from merging, rather than discovering problems weeks later.<h3>IDE plugins</h3>IDE plugins let developers fix issues while writing code, not days later during code review. Real-time feedback highlights problems as you type, often with suggested fixes available immediately.This shortens the feedback loop from hours or days down to seconds, so quality enforcement feels like a helpful nudge, not a tollbooth.<h2>Unified platforms vs. point solutions</h2>Most teams cobble together separate tools for linting, static analysis, and security scanning. Each tool requires its own configuration, produces its own reports, and operates in its own silo. With that setup, developers waste time reconciling results, figuring out which findings matter most, and managing multiple dashboards.Unified platforms solve this by combining SAST, SCA (Software Composition Analysis), and code quality governance into a single system. Instead of juggling multiple tools with different interfaces, teams get a single view of code health.Kiuwan exemplifies this approach with support for 30+ programming languages, including legacy languages that specialized tools may not cover well. This lets teams standardize quality enforcement across their stack, whether they’re working in modern JavaScript frameworks or maintaining long-lived COBOL systems.The value becomes clearer in technical debt management. Platforms like Kiuwan can prioritize what to fix first based on severity and estimated effort, turning a sprawling backlog into a more actionable list.<h2>Improve code quality and reduce technical debt with Kiuwan</h2>Code quality doesn’t have a finish line. You either reinforce it every day or watch it erode. The right tooling makes that reinforcement consistent.Kiuwan helps teams detect issues and prioritize remediation so developers can focus on the fixes that matter most. The platform brings together:<ul><li>SAST and SCA to identify security risks in both proprietary code and open-source components </li><li>Multi-language support across 30+ languages, including legacy codebases </li><li>Governance and reporting to help teams track progress over time and focus effort where it has the most impact </li><li>CI/CD integrations that support automated scanning and policy enforcement earlier in the delivery process</li></ul><figure id="figure-h167xm414emq99mgag"><img id="h167xm414emq99mgag" src="https://cdn.durable.co/blocks/2cQkPvF8zmn7GbGlcdPt0EPN0g6MN0O6s5JULLZDC0m3LUDE7AkRtEFOGzVIvGXj.png" alt="" title="" class="object-cover bg-gray-100 rounded-2xl md:rounded-3xl lg:rounded-4xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/2cQkPvF8zmn7GbGlcdPt0EPN0g6MN0O6s5JULLZDC0m3LUDE7AkRtEFOGzVIvGXj.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure>Static analysis finds the problems. Kiuwan helps you organize and act on them. <a data-mce-href="https://www.kiuwan.com/request-a-trial/?utm_source=partners_website&amp;utm_medium=partners&amp;utm_campaign=dube&amp;utm_term=&amp;utm_content=free_trial" href="https://www.kiuwan.com/request-a-trial/?utm_source=partners_website&amp;utm_medium=partners&amp;utm_campaign=dube&amp;utm_term=&amp;utm_content=free_trial" target="_blank" data-mce-selected="inline-boundary">Start your free trial</a> to see how unified code quality management can reduce technical debt and support faster development.<a data-mce-href="https://dbinsights.co.za/speak-to-expert" href="https://dbinsights.co.za/speak-to-expert" target="_blank" rel="noopener">Speak to an expert.</a>

A Developer’s Guide to Improve Code Quality in Your Codebase

Distributor hands carefully packaging product order with branded box and merchandise in organized fulfillment workspace

A sneak peek at key findings from Sembi’s first-ever industry-wide survey | RanorexWe surveyed nearly 4,000 QA engineers, developers, security professionals, and engineering leaders to understand the real state of software quality in 2026. The inaugural Sembi Software Quality Pulse Report is full of insights for automation teams—including some that will feel very familiar, and some that should reshape how you’re thinking about your testing strategy.TL;DRAutomation investment is real, but the execution gap is widening. AI-generated code is driving more testing volume than current automation coverage can absorb, and most teams still aren’t fully integrated with their CI/CD pipelines. The teams pulling ahead are the ones pairing robust UI test automation with AI-assisted capabilities and tighter DevOps integration. <h3>Automation Has Come Far, But Execution Gaps Are Real &nbsp;</h3><figure id="figure-fm9k3fj7wmmq80jpl3"><img id="fm9k3fj7wmmq80jpl3" src="https://cdn.durable.co/blocks/397PAQwm8U7RJbubKsKMPH0uYjdcQcvmOETeNKLIp772AOVuUKlGiSUJIm4Tttu3.png" alt="" title="" class="object-cover bg-gray-100 rounded-2xl md:rounded-3xl lg:rounded-4xl" style="aspect-ratio: 3/2; object-position: center center;" data-corners="default" data-aspect="3:2" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/397PAQwm8U7RJbubKsKMPH0uYjdcQcvmOETeNKLIp772AOVuUKlGiSUJIm4Tttu3.png" data-mce-style="aspect-ratio: 3/2; object-position: center center;"></figure>The good news: most teams have made significant investments in test automation. The less good news: the return on those investments isn’t always meeting expectations. Skill shortages, tooling complexity, and fragile test suites are limiting what automation can actually deliver.<ul><li>57% of QA tests are currently automated, demonstrating that teams are still struggling to close the test automation gap </li><li>Only ~26% of QA teams describe themselves as mostly or fully integrated with DevOps pipelines </li><li>Quality, security, and compliance top the list of what teams care about most at release—yet quality processes haven’t caught up to the pace teams are actually expected to maintain</li></ul> For UI automation teams specifically, the pressure is real. Systems are more distributed, interfaces are more complex, and the expectation to move faster hasn’t slowed down. Teams that are still maintaining brittle, mostly manual test suites are feeling the strain of that gap more than anyone. <h3>AI Code Generation Is Creating a New Testing Surface</h3><figure id="figure-c4ipdz8aanmq80n4vx"><img id="c4ipdz8aanmq80n4vx" src="https://cdn.durable.co/blocks/32Stt0FnUnv1DUfmsdg0zM64xAW129RFb27TkdtC9OTVhuzSCiYLWsbHKX8vLon9.png" alt="" title="" class="object-cover bg-gray-100 rounded-2xl md:rounded-3xl lg:rounded-4xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/32Stt0FnUnv1DUfmsdg0zM64xAW129RFb27TkdtC9OTVhuzSCiYLWsbHKX8vLon9.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure> &nbsp;Here’s the number that should be on every automation engineer’s radar: respondents reported that an average of 53% of their code is now AI-generated or AI-assisted. This shows that AI-driven development isn’t just a passing curiosity, it’s a fundamental change in what QA teams are being asked to cover.AI-generated code can produce more features faster, but it also introduces new inconsistency patterns, edge cases, and UI behaviors that traditional test scripts weren’t designed to catch. The volume is up, the unpredictability is up, and traditional coverage strategies may not scale to match.<ul><li>61% of respondents report moderate to dramatic increases in QA testing demand due to AI-generated code </li><li>Only 17% say AI-driven testing tools have had a significant impact, and most gains are still incremental</li></ul> The teams winning the automation game in 2026 are the ones pairing robust UI test automation with AI-assisted test generation, self-healing capabilities, and smarter coverage analysis. The goal isn’t just running more tests—it’s running the right tests, faster, with less maintenance overhead. <h3>Integration Is the Differentiator Between High and Low Performers</h3><figure id="figure-rdebpnl10umq80p9df"><img id="rdebpnl10umq80p9df" src="https://cdn.durable.co/blocks/f9ZqfeHERb3yfFkxBNJzNxojerU4uMp0fv6ozlBUgCk0NBx430N7PuRhaSmfMDUg.png" alt="" title="" class="object-cover bg-gray-100 rounded-2xl md:rounded-3xl lg:rounded-4xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/f9ZqfeHERb3yfFkxBNJzNxojerU4uMp0fv6ozlBUgCk0NBx430N7PuRhaSmfMDUg.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure>Across every dimension of the survey, integration with the broader DevOps pipeline was the clearest separator between high- and low-performing teams. Teams that are deeply integrated report faster cycles and lower defect leakage. Teams that aren’t are struggling with delayed feedback and blind spots in quality coverage.For UI automation platforms, this makes seamless CI/CD integration non-negotiable. When automated UI tests run in context with the full development pipeline—feeding real-time results into test management, defect tracking, and release dashboards—teams can make faster decisions with more confidence. <h2>The Staffing Math Isn’t Working</h2><figure id="figure-f2gs7j0wamq80qm3h"><img id="f2gs7j0wamq80qm3h" src="https://cdn.durable.co/blocks/10QRK6sq2A4E8aLlo9rhqNtzregdlRG2G0suHvruTrgsz9ohKtZDxFL4FoFYgKWU.png" alt="" title="" class="object-cover bg-gray-100 rounded-2xl md:rounded-3xl lg:rounded-4xl" style="aspect-ratio: 3/2; object-position: center center;" data-corners="default" data-aspect="3:2" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/10QRK6sq2A4E8aLlo9rhqNtzregdlRG2G0suHvruTrgsz9ohKtZDxFL4FoFYgKWU.png" data-mce-style="aspect-ratio: 3/2; object-position: center center;"></figure>44.7% of QA teams report being understaffed, and most don’t expect meaningful headcount growth in the next 12 months. For automation teams, this means the pressure to do more with less isn’t going away. Automation tools that reduce setup complexity, lower the barrier to building robust UI tests, and help non-specialist team members contribute to test coverage are increasingly essential.<h3> Want the full picture?</h3>This is just a snapshot of what the Sembi Software Quality Pulse Report covers. The full report dives deep into automation maturity, AI adoption, DevOps integration, and the convergence of QA and security—with data from nearly 4,000 practitioners.<a data-mce-href="https://www.ranorex.com/first-edition-software-quality-pulse-report/" href="https://www.ranorex.com/first-edition-software-quality-pulse-report/">Download the Sembi Software Quality</a> Pulse Report today!<h2>&nbsp; FAQ</h2>What does the 2026 Software Quality Pulse Report reveal about test automation maturity?Most teams have made real automation investments—57% of QA tests are currently automated—but execution gaps remain. Skill shortages, fragile test suites, and limited CI/CD integration are preventing teams from getting the full return on those investments. Automation coverage alone isn’t enough; it needs to be connected, maintained, and scaled with the right tooling.How is AI-generated code affecting UI test automation?With 53% of all code now AI-generated or AI-assisted, QA teams are facing new UI behaviors, edge cases, and inconsistency patterns that traditional test scripts weren’t built to catch. 61% of respondents report moderate to dramatic increases in testing demand as a result. Self-healing test capabilities and AI-assisted test generation are becoming essential for automation teams trying to keep pace. Why is CI/CD integration so important for automation teams?The report found that integration with DevOps pipelines is the single clearest differentiator between high- and low-performing QA teams. Only about 26% of QA teams are mostly or fully integrated—meaning most automation suites still operate with delayed feedback loops and limited pipeline visibility. Teams with deep CI/CD integration report both faster release cycles and lower defect leakage. How are QA and security responsibilities converging for automation engineers?The report shows that 68% of professionals see strong value in aligning QA and security, and automation teams are at the front line of that convergence. As AI-generated code increases both volume and vulnerability surface, automated testing pipelines are increasingly expected to validate not just functionality, but security risk and release readiness. Teams that build security validation into their automation workflows early are better positioned for what’s ahead. What should automation teams prioritize to stay competitive in 2026?The data points to three priorities: closing the CI/CD integration gap, adopting AI-assisted test generation to scale coverage without scaling headcount, and building more resilient test suites that don’t break with every UI change. With 44.7% of QA teams already understaffed, automation efficiency—not just automation volume—is what separates teams that can keep up from those that can’t.

The State of Test Automation in 2026: Key Findings from the Software Quality Pulse Report

Dube Insights

Distributor of quality & test management, automated testing and security solutions.

Poppins

IBM Plex Mono

IBM Plex Sans

Roboto

By continuing to use this website, you consent to the use of cookies in accordance with our Privacy Policy.
Please click the privacy policy button at the bottom of the page to view our privacy policy.

Blog

How to create a QA report template (with template)

Continuous Threat Monitoring for Application Security

From Test Management to Quality Intelligence: Inside Test...

Enterprise Application Security: How to Close the Gaps Th...

A Developer’s Guide to Improve Code Quality in Your Codebase

The State of Test Automation in 2026: Key Findings from t...