From medical devices and automotive systems to traffic control networks, high-integrity software quietly underpins modern infrastructure. Achieving absolute software integrity demands exhaustive manual verification, driving engineering costs to unsustainable levels. This economic friction creates an operational bottleneck: the market demands safer software, yet traditional deployment methodologies remain cost-prohibitive. Artificial intelligence (AI) allows software development teams to deliver reliable yet economical code by accelerating code generation. However, AI’s non-deterministic nature presents a fundamental barrier: trust.Mitigating this trust barrier has historically relied on manual human review of all AI outputs. However, human auditing simply cannot scale alongside the velocity of generative pipelines, bottlenecking development. To utilize AI safely in safety-critical domains, engineering frameworks must pivot toward deterministic guarding mechanisms by introducing automated, mathematical validation tools to enforce strict architectural and structural limits on AI-generated artifacts.The <a href="https://www.adacore.com/blog/gnat-foundry-intersection-demonstrating-trustworthy-ai-development" target="_blank" rel="noopener noreferrer">open-source project GNAT Foundry:</a> Intersection provides a concrete blueprint for this hybrid pipeline. By modeling a complex traffic infrastructure layout, the project demonstrates how deterministic toolchains can independently establish structural and functional safety, providing mathematical verification of AI-authored code without scaling manual oversight.<h2>Trusting AI-Authored Code</h2>Integrating artificial intelligence into high-integrity software development introduces two systemic engineering challenges:<ul><li>Unpredictable Failure Modes: AI agents fail in non-human, asymmetric patterns that are difficult to anticipate, making continuous human accountability over AI-authored artifacts mandatory.</li><li>Reviewer Cognitive Overload: The velocity of AI generation easily outpaces human auditing capacity, requiring automated, deterministic safeguards to validate output integrity at scale.</li></ul><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1792594122442-1792594122441.jpeg" width="602" height="403" class="fr-fic fr-dii" />Source: AdaCoreWhile human oversight remains the baseline for legal and operational accountability, relying exclusively on manual code audits introduces an operational bottleneck that fails to leverage human cognitive strengths. Traditional verification paradigms cannot absorb the volume of code generated by automated pipelines.The architecture modeled in GNAT Foundry addresses this imbalance by shifting the burden of verification onto non-AI deterministic tools. This framework confines the AI agent's output within strict mathematical and algorithmic boundaries, allowing human engineers to focus exclusively on high-level conceptual validation.Historically, safety-critical software engineering has relied on strongly typed languages like Ada and its formally verifiable subset, SPARK, alongside validation tooling such as GNATprove, GNAT DAS, and CodeSonar to establish absolute mathematical certainty. The current shift lies in adapting these classic deterministic ecosystems to act as automated verification gates, validating AI-generated software pipelines at machine speed.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1792594122450-1792594122450.png" width="350" height="355" class="fr-fic fr-dii" />Intersection Demo. Source: AdaCore<h3>The Intersection Demo</h3>To evaluate the viability of deterministic AI guarding, the framework utilizes a model of a four-way traffic intersection featuring protected left turns and demand-led pedestrian crosswalks. This layout introduces enough concurrent state variables to simulate real-world safety-critical constraints:<ul><li>Vehicle Detection: Left-turn signals are demand-driven and trigger only upon vehicle presence.</li><li>Pedestrian Actuation: Crosswalk signals activate exclusively via call-button input.</li><li>State Indication: Actuated pedestrian call lights illuminate upon registration and clear when the walk signal initiates.</li><li>Dynamic Displays: Pedestrian control heads suppress countdown timers during active flashing "DON'T WALK" cycles.</li></ul><h3>System Architecture &amp; Artifact Hierarchy</h3>The baseline demonstration constructs a rigorous, vertically integrated engineering stack derived from the <a href="https://mutcd.fhwa.dot.gov/">US Department of Transportation Manual on Uniform Traffic Control Devices</a>. This baseline serves as the formal boundary that constrains AI modification requests.<ul><li>Execution Environments: The core software implementation is written in Ada/SPARK and compiles natively for Linux simulation harnesses, or directly onto bare-metal ARM 32-bit microcontrollers (with or without an RTOS).</li><li>Deterministic Verification Layers: Rather than relying on human evaluation to confirm that the code matches the Low-Level Requirements (LLRs), the platform deploys a multi-tiered verification stack:<ul><li>SPARK Silver Level: Automated mathematical proofs establishing the complete absence of runtime errors (preventing undefined behaviors, overflows, and out-of-bounds access).</li><li>SPARK Gold Level: Formal proofs of functional correctness verifying that the implementation complies exactly with formal specifications.</li><li>Compile-Time Enforcement: Strict static type-safety checks enforced natively by the Ada compiler.</li><li>Dynamic Analysis: Requirements-based testing frameworks engineered to yield 100% Modified Condition/Decision Coverage (MC/DC) and statement coverage.</li></ul></li></ul><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1792594687777-1792594687777.png" width="602" height="104" class="fr-fic fr-dii" />AdaCore's set of comprehensive systems- and software-engineering artifacts. Source: AdaCore.<h3>Traceability Matrix</h3>The cornerstone of this architecture is an interactive HTML <a href="https://www.adacore.com/books/code-traceability-study" target="_blank" rel="noopener noreferrer">traceability matrix</a> that maps the complete chain of custody from the root Concept of Operations (CONOPS) down to individual lines of code and proof obligations.By automating the generation of this evidence, the system isolates exactly where human authority is required, explicitly restricting manual sign-offs to:<ol><li>The CONOPS (serving as the foundational root of the chain of trust).</li><li>Operational CONOPS elements not traced directly to software execution.</li><li>Derived architectural requirements within the HLRs.</li></ol>Every subsequent downstream artifact—including code generation, test compliance, and proof coverage—is validated automatically by the toolchain, eliminating the need for human code-auditing loops.<h3>Executing the Change-Request Workflow</h3>The <a href="http://github.com/AdaCore/gnat-foundry-intersection">GNAT Foundry: Intersection</a> repository provides a pre-configured, automated sandbox designed to evaluate AI capabilities under rigorous engineering constraints. Out of the box, the framework establishes a fully validated, stable baseline.The environment can be initialized and evaluated natively on Linux through the following sequence:<pre># Step 1: Initialize the toolchain (FSF Open-Source or Pro tools)
make setup-community  # Alternative: make setup-pro</pre><pre># Step 2: Execute the real-time intersection graphical simulation
make run-native</pre><pre># Step 3: Compile and generate the comprehensive verification report 
make report</pre>To simulate a real-world engineering pipeline, the demo includes an automated change request scenario located in demo/demo-prompt.md. This workflow tasks a downstream AI agent with adding countdown timers to the pedestrian control units.Upon execution, the AI agent applies a pre-approved patch to the root CONOPS and initiates a multi-tier pipeline. The agent autonomously updates HLRs, refactors the underlying Ada SPARK implementation, generates new requirements-based test scripts, and re-runs the entire mathematical verification stack without human intervention.<h2>Quantifying Certainty: The Deterministic Evidence Layer</h2>The core architectural thesis of the GNAT Foundry framework is that code-level verification should rely on mathematical and algorithmic certainty rather than subjective human inspections. When the AI agent completes an engineering modification, the system generates a definitive verification report.Trust in the modified system is established across four distinct deterministic gates:<ul><li>Compilation Defensibility: The GNAT compiler verifies type-safety and validates localized, compile-time contract checks embedded within the source architecture.</li><li>Mathematical Proof: The SPARK analysis engine mathematically proves the absolute absence of runtime errors (SPARK Silver) and verifies compliance with functional correctness contracts (SPARK Gold).</li><li>Requirements Compliance: Automated test execution via GNAT Test ensures that all requirements-based functional tests achieve a passing state.</li><li>Structural Coverage Validation: GNAT Coverage performs dynamic analysis during test execution, ensuring the modified code achieves 100% statement and Modified Condition/Decision Coverage (MC/DC).</li></ul><h2>Architectural Context: Brownfield vs. Greenfield Deployment</h2>The GNAT Foundry: Intersection project specifically models a brownfield development pipeline. The AI agent is injected into a mature ecosystem with an established, rigid chain of deterministic evidence. This represents the most common enterprise use case: engineering teams seeking to accelerate legacy system iterations without compromising historical safety metrics. Under these constraints, the pre-existing verification framework limits the AI’s decision-making freedom, preventing unverified regressions.However, the underlying workflow scales identically to greenfield projects. In new development lifecycles, the automated verification toolchain enforces safety metrics from day one, guiding the AI agent through the synthesis of clean-sheet artifacts—from initial CONOPS definition down to hardware-targeted test execution.<h2>Algorithmic Verification as the Future of Safety-Critical Pipelines</h2>Shifting the burden of proof from human code review to automated validation engines solves the fundamental scaling problem of generative AI in critical systems. By wrapping non-deterministic agents in rigid mathematical guards, engineering organizations can safely capture the velocity benefits of AI. <a href="https://www.adacore.com/videos/gnat-foundry-intersection">GNAT Foundry</a> demonstrates that development speed and high-integrity software compliance are no longer mutually exclusive paradigms.Learn more about <a href="https://www.adacore.com/offerings">AdaCore’s integrated tools and expert services for high-integrity software</a>.

The GNAT Foundry Intersection Demo

Guarding AI with Deterministic Trust

What happens when a car, a hospital infusion pump, or a fighter jet starts solving the same engineering problem? A decade ago, that question wouldn't have made sense. Today, it defines the future of every safety-critical industry.Automotive manufacturers were the first to name this shift, coining the term "Software-Defined Vehicle" as modern cars began deriving their functionality from software rather than hardware alone. But automotive didn't invent the trend; it just gave it a name. Aircraft, medical devices, trains, industrial robots, factory automation systems, and military platforms are all undergoing the same transformation.Software is no longer simply controlling hardware. It defines what a product can do, for as long as that product stays in service, and that changes the engineering mindset these industries need far more than it changes their technology stack.This article explores why industries are moving toward software-defined systems and why continuous assurance is important for maintaining trust throughout the product lifecycle.<h2>What Does "Software-Defined" Actually Mean?</h2>“Software-defined” does not mean a product has an internet connection or supports over-the-air (OTA) updates. The more important shift is what determines the product’s capabilities. In a traditional embedded system, hardware and software are designed around a largely fixed set of functions. Once the product reaches the market, changes often require hardware modifications, recalls, or a new product generation.A software-defined system is designed to evolve after deployment. Software updates can add features, improve performance, fix defects, respond to cybersecurity threats, and introduce new capabilities without replacing the underlying hardware. <a href="https://www.mckinsey.com/features/mckinsey-center-for-future-mobility/our-insights/software-defined-hardware-in-the-age-of-ai">McKinsey</a> describes software-defined hardware as programmable and flexible. They allow products to handle new tasks through software updates rather than costly hardware upgrades.The automotive industry provides a clear example. <a href="https://arxiv.org/html/2605.30001v1">Modern SDVs</a> use centralized computing and software-hardware decoupling to support OTA updates and new applications throughout the vehicle’s life.<h2>Why Industries Are Moving Toward Software-Defined Systems</h2>Software-defined systems have become practical as several technologies have matured simultaneously. Companies are also seeing clear benefits from making software a larger part of the product.<h3>What Made the Shift Possible</h3>Key enablers include:<ul><li>More powerful processors and cheaper storage support complex software and AI workloads.</li><li>Connectivity and cloud infrastructure make remote monitoring, data collection, and OTA updates possible at scale.</li><li>AI acceleration enables intelligent functions to run directly on embedded systems.</li><li>Virtualization and containers make software easier to deploy and manage across platforms.</li><li>Mature CI/CD practices allow teams to develop, test, and release updates more frequently.</li><li>Customer expectations are shifting toward products that improve through post-purchase updates.</li></ul><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1787596732505-1787596732505.png" width="624" height="416" class="fr-fic fr-dii" />Source: Parasoft<h3>Why Companies Are Embracing It</h3>Software can add functionality without requiring a new hardware design. It can also extend product life, deliver security fixes faster, reduce maintenance through remote diagnostics, and help companies respond quickly to changing customer needs.This makes software a direct source of product value. McKinsey estimates that automotive software and electronics could become <a href="https://www.mckinsey.com/features/mckinsey-center-for-future-mobility/our-insights/mapping-the-automotive-software-and-electronics-landscape-2024">a $462 billion market by 2030</a>. This highlights the growing economic role of software in physical products. <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1787596732519-1787596732519.png" width="624" height="636" class="fr-fic fr-dii" />Source: McKinsey<h3>New Business Models</h3>Software-defined products also enable subscriptions, feature unlocks, predictive maintenance, fleet analytics, and remote diagnostics. These models allow companies to generate value throughout the product lifecycle rather than only at the initial sale.As a result, software is no longer simply a supporting cost. It is becoming a competitive differentiator and a source of ongoing revenue.<h2>Software-Defined Systems Are Transforming Every Safety-Critical Industry </h2>The software-defined shift is not limited to the automotive industry. Across many different industries, software is taking a larger role in system behavior, connectivity, maintenance, and future upgrades.<h3>1. Automotive</h3>Vehicles are moving toward centralized computing, software-defined architectures, and advanced driver assistance systems (ADAS). It also includes autonomous driving and digital cockpits. Over-the-air (OTA) updates allow manufacturers to add features and improve vehicle software after delivery.<h3>2. Aerospace and Defense</h3>Aircraft rely on software for avionics, flight management, mission systems, predictive maintenance, and connected operations. Defense platforms similarly use software to deliver new mission capabilities and respond to changing operational needs.<h3>3. Medical and Rail</h3>Connected medical devices can receive software updates and security fixes throughout their lives. The <a href="https://www.fda.gov/regulatory-information/search-fda-guidance-documents/postmarket-management-cybersecurity-medical-devices">FDA</a> now expects manufacturers to address cybersecurity across the device lifecycle, including post-market vulnerabilities. Rail systems are also becoming more software-intensive, with standards such as <a href="https://standards.iteh.ai/catalog/standards/clc/32d1d924-9bfd-477e-adf4-de6e3358be77/en-50716-2023?srsltid=AfmBOoqJZ8bGQYQrvDwmDIVeqMpv_rG9VfdtbXFA4aSX5G15B-sxRAqV">EN 50716</a> supporting modern software development for railway control and protection systems.<h3>4. Industrial Automation, Robotics, and Agriculture</h3>Factories and agricultural systems are adopting robotics, digital twins, edge AI, and predictive maintenance. These technologies allow machines to use real-time data to improve operations and detect problems earlier. The International Federation of Robotics reported that <a href="https://ifr.org/ifr-press-releases/news/global-robot-demand-in-factories-doubles-over-10-years">542,000 industrial robots</a> were installed worldwide in 2024, more than twice the number installed a decade earlier.Although each industry has unique regulations, they are solving the same engineering challenge: continuously evolving software without compromising safety or reliability.<h2>The Engineering Lifecycle Has Fundamentally Changed </h2>Traditional embedded development followed a mostly linear path:Design → Develop → Verify → ReleaseAfter launch, engineering teams often shifted their focus to maintenance. They usually introduced major changes with a new hardware revision or product generation.Software-defined systems change this model. Teams can now update products throughout their operational life with software updates, security patches, new features, and AI improvements. The lifecycle becomes continuous:Define → Design → Implement → Integrate → Verify → Validate → Deliver → Operate → Maintain → UpdateProducts no longer have a clear “final” state. Each update can change system behavior or introduce new risks. Teams must engineer, test, verify, and validate every update before release with the same level of confidence as the original product.This shift also changes how engineering teams work. Teams must move from release-focused development to continuous engineering. They must keep verification and compliance active throughout the product lifecycle as the software evolves.<h2><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1787596732526-1787596732526.jpeg" width="624" height="356" class="fr-fic fr-dii" />Infinity symbol. Source: AdobeStock</h2><h2>Engineering Software-Defined Systems: Required Capabilities</h2>A continuous lifecycle introduces a new problem: how can teams prove that every software change remains safe, secure, and compliant? A CI/CD pipeline can automate delivery, but speed alone is not enough for safety-critical systems. Verification must become part of the pipeline so that defects and compliance gaps are identified before software reaches the product.A software-defined engineering workflow therefore needs capabilities such as:<ul><li>Static analysis and coding standards: Find defects, unsafe code patterns, and violations of rules such as MISRA, AUTOSAR, CERT, and CWE early in development.</li><li>Automated unit and regression testing: Check new code while confirming that existing functions still behave as expected after changes.</li><li>Structural code coverage: Measure how thoroughly tests exercise the code, including branch, condition, and, where required, MC/DC coverage.</li><li>Requirements traceability: Link requirements to test cases and results, creating evidence that can support safety and security assessments.</li><li>Automated reporting and evidence: Turn test, coverage, and analysis results into consistent, audit-ready compliance records.</li><li>AI-assisted remediation: Help developers address static-analysis findings faster while keeping verification under engineering control.</li></ul><a href="https://www.parasoft.com/products/parasoft-c-ctest/">Parasoft</a> C/C++test and C/C++test CT bring these activities into development and CI/CD workflows. C/C++test combines static analysis, unit testing, structural coverage, requirements traceability, and compliance reporting. C/C++test CT extends automated testing and coverage into CI/CD environments. Both support verification practices used across standards including ISO 26262, ISO/SAE 21434, IEC 61508, IEC 62304, DO-178C, and EN 50716.<h2>Safety and Security Can No Longer Be Separate</h2>The boundaries between functional safety and cybersecurity become less clear as products become connected and continuously updated. A security weakness could affect a safety-related function. A software update that fixes a security issue could also change how the system behaves. The same risk applies when teams update AI models or other software components.Safety and security standards still address different risks, but engineering teams need to consider them together. For example, ISO 26262 covers functional safety in road vehicles, while ISO/SAE 21434 covers cybersecurity risks across the vehicle lifecycle. ISO describes these standards as complementary. Similar links exist across other safety-critical industries:<ul><li>Medical: IEC 62304 for medical software alongside cybersecurity practices such as IEC 62443 where applicable.</li><li>Aerospace: DO-178C for airborne software alongside DO-326B for aircraft cybersecurity.</li><li>Industrial: IEC 61508 for functional safety alongside IEC 62443 for industrial automation and control-system security.</li><li>Rail: EN 50716 establishes software development requirements for railway control and protection systems.</li></ul>This also changes how organizations approach compliance. NIST SP 800-204D recommends integrating software supply-chain security into DevSecOps and CI/CD pipelines. This allows teams to find and address security risks during development instead of waiting until the final release. <h2>AI's Dual Role: Accelerating Development and Demanding New Compliance </h2>AI is changing software-defined systems in two ways. Engineering teams are using AI to speed up development, while AI itself is becoming part of safety-critical products.During development, AI can help with code generation, defect analysis, static-analysis remediation, unit-test creation, and test optimization. These tools can help teams work faster, but AI-generated code is not automatically safe or compliant. It still needs code analysis, testing, review, and verification, just like software written by developers.AI embedded in products creates a different challenge. Computer vision, ADAS, medical diagnostics, predictive maintenance, and industrial automation use machine learning. Unlike traditional software, an AI model can work as designed yet still produce an unsafe result when it encounters conditions not well represented in its training data. <a href="https://www.iso.org/standard/83303.html">ISO/PAS 8800:2024</a> addresses AI-related safety risks in road vehicles and provides guidance for building safety assurance around AI-based functions.<h3>Compliance is Now Continuous</h3>Traditionally, most compliance work was completed before a product was released. Software-defined systems change this model. Products can continue to evolve through software updates, security patches, feature releases, AI model improvements, cloud service changes, and operational data.Each change can affect safety, security, or system behavior. Teams therefore need to assess changes and maintain the required testing, documentation, and compliance evidence throughout the product lifecycle.Frameworks such as the <a href="https://www.nist.gov/itl/ai-risk-management-framework">NIST AI Risk Management Framework</a> support this lifecycle approach by addressing AI risks across development, evaluation, deployment, and use. For software-defined systems, the goal is to maintain confidence in safety, security, and compliance with every change.<h2>The Next Competitive Advantage</h2>Software-defined systems allow products to evolve continuously, but every update can introduce new risks to safety, security, reliability, and compliance. Engineering teams therefore need to move beyond simply delivering software faster. They need to verify every change with confidence.Parasoft C/C++test and C/C++test CT help address this challenge by automating static analysis, testing, code coverage, traceability, and compliance activities across the development lifecycle. By embedding continuous verification into modern workflows, teams can enable faster software evolution without compromising safety and regulatory requirements.Ready to build continuous assurance into your software lifecycle? <a href="https://www.parasoft.com/products/parasoft-c-ctest/c-cpptest-free-trial/">Request a Parasoft demo today!</a>

Discover how software-defined systems are transforming automotive, aerospace, medical, rail, and industrial engineering with continuous software evolution.

The Rise of Software-Defined Systems: Why Every Safety-Critical Industry Is Becoming a Software Company

Engineering teams already use a mix of static analysis, unit testing, code coverage, and compliance documentation tools. But the real challenge is that these activities often operate independently. This results in extra manual work, as engineers have to correlate results, quality teams have to gather evidence from multiple sources, and compliance preparation can become time-consuming. A unified testing solution brings software verification activities into a single connected workflow. It does not force teams to abandon the tools they already trust. Parasoft delivers <a href="https://www.parasoft.com/solutions/c-cpp-testing-tools/">C/C++ software testing solutions</a> that integrate with existing IDEs, CI/CD pipelines, and testing frameworks. This makes adoption intuitive and reduces the need for retraining or vendor lock-in. Let’s explore how a unified testing solution like Parasoft helps engineering teams streamline verification while preserving the workflows and frameworks they've already invested in.<h2>Why Traditional Software Testing Falls Short </h2>Traditional software testing solutions were not built for today's distributed development environments. Companies now rely on diverse IDEs, open-source frameworks, automated pipelines, and collaborative development workflows. When verification tools cannot adapt to this ecosystem, they introduce unnecessary complexity instead of improving software quality. <h3>Common challenges include:</h3><ul><li>Proprietary workflows: Many platforms now require teams to adopt vendor-specific workflows instead of integrating with existing development practices. This can make adoption more disruptive than beneficial.</li><li>Limited ecosystem support: Engineering teams often work across Eclipse, VS Code, CLion, GitHub, CMake, Dev Containers, Bazel, GoogleTest, and custom frameworks. Traditional solutions do not always support these diverse environments.</li><li>AI not integrated into verification: Many development teams already use AI coding assistants, but these tools are not designed to support end-to-end software verification. Activities such as static analysis remediation, coverage closure, and compliance documentation often remain manual, reducing the overall productivity gains.</li><li>Legacy testing approaches: Older platforms were designed around manual or centralized testing rather than continuous verification integrated into modern CI/CD pipelines.</li><li>Poor developer experience: Developers want static analysis, testing, and compliance checks to run inside the IDEs and pipelines they already use, not through separate tools that interrupt their workflow.</li><li>Weak support for open-source adoption: Companies depend on proven open-source technologies and want to extend them with verification and compliance capabilities rather than replace them entirely.</li></ul>Modern verification platforms focus on integration rather than requiring companies to replace the tools they already trust. Parasoft extends existing development ecosystems by integrating with popular IDEs, CI/CD pipelines, GoogleTest, and custom testing frameworks, enabling teams to unify verification while preserving established workflows.<h2>A Unified Testing Solution That Works With Your Existing Ecosystem </h2>Effective software verification depends on more than individual testing activities. Static analysis, unit testing, code coverage, requirements traceability, and compliance reporting should work together throughout the development lifecycle. Parasoft connects these activities into a <a href="https://www.parasoft.com/industries/embedded/">unified verification workflow</a> without requiring teams to replace their existing toolchain.Here’s how Parasoft connects each stage of the process:<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1784648711021-1784648711021.png" width="624" height="369" class="fr-fic fr-dii" />Parasoft's Unified Verification Layer<ul><li>Static analysis: Helps to detect coding standard violations, security weaknesses, and software defects early in development. Support for standards such as MISRA, AUTOSAR C++14, CERT, and CWE helps organizations enforce secure coding practices while reducing security risks in connected and embedded systems.</li><li>Unit testing: C/C++test CT extends existing frameworks such as GoogleTest with enterprise-grade verification capabilities instead of replacing them. Teams can continue using their current test assets while adding coverage, traceability, and compliance reporting.</li><li>Code coverage: Measure statement, branch, and MC/DC coverage using existing test suites rather than creating new ones. This helps companies meet safety-critical coverage objectives while reducing manual verification effort.</li><li>Requirements traceability: Connect requirements to source code, test cases, coverage results, and verification artifacts in a single workflow. Automated traceability replaces manual spreadsheets and provides continuous visibility into development progress and release readiness.</li><li>Compliance reporting: Compliance evidence is generated as a byproduct of everyday verification activities. Because testing, coverage, traceability, and analysis remain connected, teams can produce audit-ready reports throughout development instead of preparing documentation at the end of a project. </li></ul>Every verification activity contributes to the next. This helps engineering teams to improve software quality, accelerate certification readiness, and preserve the development environments, CI/CD pipelines, and testing frameworks they already trust.<h2>Where AI Fits Into Software Verification </h2>In <a href="https://www.parasoft.com/solutions/functional-embedded-software-testing/">safety and security-critical development</a>, engineering judgment, code reviews, and validation are important. AI simply helps teams complete repetitive verification tasks more efficiently so the developers can focus on higher-value work.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1784648711080-1784648711080.png" width="624" height="336" class="fr-fic fr-dii" />Parasoft Unified Software Verification Architecture Across the SDLCParasoft applies AI to verification activities where it delivers the greatest value. <ul><li>AI can help developers remediate static analysis violations by suggesting fixes that align with established coding standards.</li><li>It can also generate unit test cases to improve code coverage and make it easier to identify and verify untested paths without the manual effort of writing every test from scratch. </li><li>By accelerating test creation and helping close statement, branch, and MC/DC coverage gaps, AI supports faster verification while maintaining the quality expectations of modern software development.</li><li>AI-assisted workflows also help engineering teams scale verification across modern CI/CD pipelines, reducing manual effort while supporting embedded DevOps practices.</li></ul>Moreover, AI-generated recommendations do not apply automatically. Engineers review, validate, and approve every suggested code change or test case before it becomes part of the software baseline. This human-in-the-loop approach preserves auditability and accountability while accelerating verification workflows.<h2>A Dashboard View of Software Quality and Compliance</h2>As verification activities become connected, teams also need a single place to track progress and demonstrate compliance. Parasoft DTP (Development Testing Platform) serves as the centralized reporting and analytics layer, bringing together results from static analysis, unit testing, code coverage, and requirements traceability into one dashboard.With DTP, engineering and compliance teams can:<ul><li>Monitor verification progress across projects from a single dashboard.</li><li>Track code coverage, traceability, and compliance with coding standards.</li><li>Identify quality trends and outstanding verification tasks.</li><li>Generate audit-ready compliance evidence without manually assembling reports.</li><li>Give developers, quality engineers, and compliance managers a shared view of project status.</li></ul>Because the same dashboard serves multiple stakeholders, everyone works from a common source of truth. This helps teams make more informed decisions while keeping software quality and compliance visible throughout the development lifecycle.<h2>How Day-To-Day Testing Changes With Parasoft </h2>The impact of a unified verification workflow is most visible in day-to-day development. Rather than changing how teams work, Parasoft enhances the tools and processes they already use. The comparison below highlights how everyday software verification becomes more connected, automated, and efficient.<div><table><tbody><tr><td>Before</td><td>With Parasoft</td></tr><tr><td>Multiple disconnected testing and verification tools</td><td>Connected verification workflow across the existing toolchain</td></tr><tr><td>Separate reporting systems</td><td>Centralized reporting through a web dashboard (Parasoft DTP)</td></tr><tr><td>Manual collection of compliance evidence</td><td>Continuous visibility into compliance and audit readiness</td></tr><tr><td>Tool-specific workflows that require migration</td><td>Existing IDEs, CI/CD pipelines, and testing frameworks remain unchanged</td></tr><tr><td>Manual investigation of defects and test creation</td><td>AI-assisted remediation and test generation to improve productivity</td></tr><tr><td>Frequent context switching between tools</td><td>Integrated verification within existing development environments</td></tr></tbody></table></div><h2><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1784648711086-1784648711086.png" width="624" height="364" class="fr-fic fr-dii" />Modernize Software Verification Without Rebuilding Your Toolchain</h2>Engineering teams do not necessarily need more testing tools. They need better integration between the tools they already trust. Parasoft simplifies software verification without disrupting established development practices. It adds the visibility, automation, traceability, and TÜV SÜD-certified verification technologies needed to support safety- and security-critical software development.Ready to simplify software verification? Explore Parasoft's unified testing solutions to see how they fit into your existing development ecosystem. <a href="https://www.parasoft.com/contact/">Schedule a demo</a> to learn how your team can accelerate software quality and compliance.

Discover how Parasoft helps engineering teams unify verification, automate compliance reporting, and accelerate testing using the tools they already trust.

How Parasoft Unifies Software Verification

<div id="isPasted">With projections that the number of electric vehicles (EVs) will <a href="https://www.iea.org/reports/by-2030-evs-represent-more-than-60-of-vehicles-sold-globally-and-require-an-adequate-surge-in-chargers-installed-in-buildings" target="_blank">represent more than 60% of global vehicle sales by 2030</a>, the number of EV chargers must grow proportionally. There is a clear need for a reliable EV charging infrastructure; however, inconsistency among EV charger connectors, plug types and charging protocols can increase range anxiety and even influence drivers’ purchasing decisions, because they are unsure whether the vehicle they’re considering will be compatible with the chargers they’ll need on local or long-distance trips.To address the concerns of EV drivers and simplify the charging experience, the answer seems simple: employ EV charging standards for charging stations that enable compatibility between almost any EV and the grid, also known as interoperability.“Standards are important so that cars can communicate across common protocols and interfaces to EV chargers, and so that EV chargers have standardized interfaces to communicate back to utilities,” said Henrik Mannesson, general manager of energy infrastructure at our company.“Being able to charge your EV and not having to worry about having the right adapter or connector is super important to the success of the energy transition and EV charging,” said Brandon Kanter, systems manager of EV charging.Because there are many EV manufacturers and charger types, the road to standardization is complex. Standardization efforts require both software and hardware, and open-source software in particular is especially crucial. Given its accessibility and collaboration-focused nature, open-source software has the potential to address interoperability challenges.<h2>Open-Source Software, Standardization and Market Growth</h2>Open-source software is a collection of software stacks, or collections of code, that enable a device or system, such as an EV charger, to implement an action, like charging communication. Anyone can access and contribute to open-source software code, which gives it particular advantages and benefits in the context of standards.At events called plugfests, design engineers test compatibility with their stacks and various EVs; the best software stacks can become standardized. “The stack becomes the standard because everyone tested, gave feedback, and came to an agreement. Now we can all take this software stack and use it,” said Artem Aginskiy, general manager of Arm®-based processors.The accessibility and collaborative nature of open-source software benefit EV charging manufacturers in several ways. With a standardized stack, EV charger manufacturers can alleviate interoperability challenges. Charger manufacturers benefit from faster time to market and lower development costs because open-source software is so accessible. Manufacturers can meet the demand for an expanded EV charging infrastructure. System reliability improves as well, because several people review the same code and find bugs, finding and resolving issues within software happens more quickly.For EV owners, not only does standardized software make their driving and charging experience easier, but they can also look forward to more long-term support. In other words, since anyone can contribute to open-source software, EV owners don’t have to worry about their charging experience changing if one EV charger manufacturer discontinues their product. The improved reliability and uniformity of charging have the potential to increase driver confidence and proliferate EV adoption.And by having visibility into charger source code (and the protocol implementations within that code), EV manufacturers can check that their proprietary (closed-source) solutions remain compatible with the charging infrastructure implemented on the other end of the vehicle.<h2> </h2><h2 id="isPasted">How TI Has Embraced Open Source?</h2>The transition to open-source software is one that our company has embraced, both by supporting the development and adoption of standards and by providing a wide range of EV charging-related reference designs.</div><div>The standardization of open-source software allows for further optimization of hardware. “Our <a href="https://www.ti.com/product/AM625" target="_blank">Arm-based processors</a> run standard Linux distributions, making it easy for manufacturers to integrate our hardware into their charging systems,” Brandon said. Our processors also support the addition of wired and wireless connectivity devices across various protocols, depending on the need for that charger.Our company’s support goes beyond hardware; we also provide direct support for the development of software. “We’ve worked with third parties to develop an <a href="https://www.ti.com/tool/TIDC-EVSE-WIFI" target="_blank">electric vehicle service equipment kit</a>, an EV charging reference design that designers can use to implement EV supply equipment,” Artem said.We’ve also engaged with third parties by hosting events for the open-source community. Our upstream-first mentality means providing support to third parties adopting open source before a device is widely available, so they can review code for a certain standard in advance. This enables them to start working quickly and remove potential software driver barriers.<h2>Open Source and The Future</h2>As more EV charging companies adopt and build upon open-source technology, EV charging networks have the potential to achieve new feats in interoperability with the grid.“There are two sides to a charging station – the EV side, and the side that’s drawing power from the local electric utility,” Henrik said. “You want the connection on both sides to be seamless so that the charging station can talk back and forth with the utilities.”EV interoperability is an important part of that vision. By sharing data in a standardized way, the grid can better adapt and shed demand, relieve stress on the grid, or increase capacity. Even at the residential level, industry standards achieved through open source enable the use of bidirectional charging to intelligently shed load from the grid to prevent it from overloading. This means that charged EVs may be able to send electricity to the grid when prices are high, and consume electricity from the grid when prices are low.Advancements that open source enables have the potential to bolster the growth of EV ownership, increase consumer confidence in the reliability and accessibility of chargers and the charging experience and build out a much-needed EV charging infrastructure.“Agreeing about how the different pieces are connected isn’t just a positive for TI; it’s a positive for consumers, and essential to EV industry growth and sustainability,” Brandon said. “Everybody’s contributing, and we are proud to play an important role.”</div>

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