Pipeline as Code: Managing CI/CD complexity and sprawl

Understanding the role of CI/CD

Historically, CI/CD has encompassed an automated, imperative process where code was built, tested and deployed to relatively static staging environments. Developers were able to work closely with IT operations to accelerate software builds and ensure reliability while using YAML, which represents data structures in human readable formats. YAML files enabled pipelines to reside alongside application code. They bridged the gap between CI and CD targets.

However, the trend toward distributed services and independently functioning components, like cloud-native, have complicated that process model. The onset of infrastructure as code (IaC) and accompanying tools, such as Kubernetes, Ansible and Terraform, introduced vast scalability potential and the principle of reusability. These concepts are foundational for pipelines as code, which can ensure consistency across projects and reduce operational overhead. But incorporating YAML files as version controls in PaC configuration also leads to fragmentation, environment-specific duplication and excessive small files. This is particularly cumbersome at scale.

In other words --YAML sprawl. While research indicates many organizations continue to employ scripted imperative pipelines, they still need to use PaC to stay competitive. Declarative CI/CD now offers a standardized structure with incremental stages that are easier to manage, read and secure. It also provides a direct path to GitOps; that is, a single source of truth for infrastructure and application state.

For example, instead of traditional build methods where code commits trigger the CI pipeline to push build/test/deployment to a desired state, today’s modern CD workflows rely on the GitOps pull-based model. Agents, such as Argo CD, Flux, Jenkins X and Harness, can detect any changes and pull the required responses instead of pushing them to the target system, significantly improving both security and scalability. While not a prerequisite for Pipeline as Code, GitOps both complements and accelerates declarative CI/CD approaches and, by extension, helps streamline PaC adoption.

Pipeline as Code: benefits and limitations

Once in place, developers can use PaC to manage builds and deployments as code stored in Jenkins files that reside in repositories alongside application code. The source code and its dependencies are then compiled and packaged into a deployable artifact, like Docker image and binary, for testing, deployment and continuous monitoring. Pipeline as Code solves complicated questions that are often difficult to determine when using imperative, sequential text files for builds. These include identifying those responsible for previous changes, why the revisions were executed and whether the workload code was functioning before the last change.

DevOps teams also gain other deployment advantages by employing code-based templating solutions, such as Jenkins Shared Libraries and GitLab CI, to centralize modular, reusable pipeline code. They can also access GitOps engines, like ArgoCD, Flux and CodeFresh, for specialized scaffolding that ensures a targeted environment matches the designated state stored in Git. Finally, in the current era of modern development, primary PaC advantages that can’t be understated are automatic policy enforcement, rollbacks and continuous compliance to ensure integrity throughout the SDLC.

However, the increased complexity introduced by PaC also poses the potential for pipeline debt or maintenance issues that can complicate configuration files, build scripts and workflows. Code that contains complex conditional logic or relies on multiple libraries can present troubleshooting hurdles. Moreover, failed pipelines can require substantial time investments. For example, a trivial YAML syntax error or missing environmental variable often leads to slow feedback loops and long delays waiting for a build to fail.

In addition to a steep management learning curve, developers can encounter a general lack of visibility into PaC flows and the relationships between workloads. Lack of reproducibility represents a common obstacle. For instance, visualizations of environmental settings can vary significantly depending on the device -- e.g., workstation, laptop, mobile, etc. In terms of security, hardcoding sensitive data directly into pipelines can lead to critical vulnerabilities, such as exposure of API keys, passwords, tokens and other secrets. Finally, over-reliance on third-party libraries within a pipeline can introduce new vulnerabilities and supply-chain risks if those dependencies are uncovered.

Overcoming PaC challenges and technical debt

The major tools involved with aspects of modern CI/CD software delivery pipelines are well known, including Jenkins, GitHub, GitLab and CircleCI. As C-suite and IT leaders undertake IaC implementations and look to reduce PaC technical debt, they need to choose specialization tools that integrate smoothly with existing infrastructures. For example, GitHub Actions will blend seamlessly with a preexisting GitHub CI/CD platform, and the same holds true when working with GitLab.

Specialized tools offer a number of workarounds to reduce pipeline debt, such as the ability to store artifacts, deploy containers or control dedicated CD automation. Main areas of focus are automating quality checks, enforcing standards and gaining increased visibility. Taking a shift-left approach to CI/CD will also include static code analysis, behavioral/structural evaluations, dependency tracking and consistent quality checks. Moreover, it’s clear that more stable and efficient pipelines are the result of proactive mitigation and fast feedback loops based on a fail fast strategy in which errors are uncovered early in the development lifecycle.

Below are the ways in which IT leaders and DevOps teams can address the challenges introduced by PaC.

1. Analyzing static code

In terms of strategies, IT leaders and DevOps teams should emphasize incremental analyses, scanning recent changes rather than an entire codebase. Key goals are to gain actionable feedback that they can address within their workflows and an ability to customize rules that adhere to specific project needs. Some tools that enable this include:

• SonarQube. A proprietary tool with a Community Edition that offers code-quality checks and security enforcement across multiple development teams. The platform integrates with all the major CI/CD solutions through detailed dashboards and provides Quality Gates that block non-compliant builds while supporting over 30 programming languages.
• Snyk Code. This focuses on speed and usability with AI-powered, real-time feedback based directly in IDEs and pull requests. The tool provides actionable suggestions for code fixes and integrates seamlessly with the broader Snyk security platform for software composition analysis (SCA), container scanning, misconfiguration detection and IaC flaws.
• Semgrep. An open source, lightweight and highly customizable analysis tool known for both its speed and YAML-based custom rules. A well-regarded community-based tool, Semgrep is part of a larger, integrated ecosystem of application security tools that integrate with CI pipelines and meet the demand for granular static code analysis.

2. Evaluating PaC functionality

Analyzing how PaC practices operate and ensuring that code meets both behavioral requirements and structural standards is critical to ensuring successful software development and deployment. Increasingly, the most optimal tools integrate AI-driven testing, semantic analysis and automated verification within pipelines. These tools include:

• Harness. Developers use the proprietary Harness to automate the creation, editing and management of PaC through AI. In addition to pipeline generation, test-based modifications and security controls, team members can use plain language prompts to gain configuration insights and improve software delivery outcomes.
• TestRigor. This includes a proprietary AI-driven tool to automate behavioral testing across UIs using plain language prompts. The tool enables developers to overcome common reproducibility issues by providing test capabilities across web, mobile, desktop and APIs.
• Cucumber. This is an open-source tool for Behavior-Driven Development (BDD). Engineers can validate pipeline behavior using the domain-specific language (DSL) Gherkin to write test scenarios in human-readable formats. The tool fosters cross-silo collaborations, ensuring that non-technical specifications can be directly translated into automated tests that align business goals with software deliverables.

3. Tracking and resolving dependency issues

Dependency tracking in modern CI/CD pipelines falls under the category of Software Composition Analysis (SCA), which is critical for tracking artifacts in modern PaC practices. DevOps teams can integrate tools directly into the development and CI/CD workflow to identify and manage dependencies, vulnerabilities and license compliance issues:

• Black Duck. This is a proprietary tool for both security evaluations and dependency analyses. Developers can identify direct and transitive dependencies in PaC components, including source code, artifacts, containers and firmware. The open source security and license compliance tool uses scanning technologies to evaluate dependency, binary, codeprint and code snippets, generating a software bill of materials inventory to ensure visibility and compliance.
• Mend.io. Formerly WhiteSource, this solution provides a vendor-based tool for policy enforcement and license compliance at scale. The solution offers continuous monitoring to ensure comprehensive coverage for SCA as well as static application security testing (SAST). Developers can both identify vulnerabilities in third-party components and uncover pipeline code errors that generate software conflicts and security flaws.
• OWASP Dependency-Check. This open source solution offers basic, free vulnerability scanning and OWASP Dependency-Track for continuous, platform-level analysis. While noted for returning high numbers of false-positives, the tool is regularly updated to stay current with new CI/CD trends and ensures that only secure code moves to production.

Kerry Doyle writes about technology for a variety of publications and platforms. His current focus is on issues relevant to IT and enterprise leaders across a range of topics, from nanotech and cloud to distributed services and AI.