DevPlaza, an emerging leader in developer tooling innovation, set out to solve one of the most persistent challenges in modern software engineering: identifying and resolving bugs early in the development lifecycle. As software complexity grew, traditional QA and code review pipelines could no longer scale, leading to production failures, wasted developer hours, and delayed releases.

The problem: manual testing, delayed detection, and fragmented workflows

DevPlaza’s engineering teams were spending excessive time chasing bugs across different systems, Jira tickets, GitHub pull requests, CI/CD logs, and static code analysis tools. Critical issues were often discovered late in the development cycle, impacting release velocity and user experience.

Despite investing in top-tier tooling, DevPlaza lacked a cohesive strategy for software testing with AI. Manual reviews couldn't keep up with code changes. CI/CD logs were too noisy to identify real issues. And quality metrics were locked in silos, slowing down developers and delaying releases. It needed an intelligent system to unify workflows, prevent regressions early, and automate test-related decisions.

The solution: SDLC copilot for smarter software testing with AI

To address these challenges, GoML and DevPlaza co-developed a modular, scalable solution centered around software testing with AI. The MVP integrated four purpose-built agents across the software lifecycle.

Git agent

• Validates pull requests with AI-powered checks

• Automatically analyzes code diffs to flag potential bugs

GitHub actions agent

• Parses CI/CD logs to detect and diagnose failures

• Recommends automated fixes for common error patterns

Jira agent

• Provides AI-driven task and bug triage

• Analyzes historical issue data to forecast failure types

SonarQube agent

• Detects code vulnerabilities and test gaps

• Delivers AI-backed suggestions to improve test coverage and maintainability

Together, these agents formed the backbone of DevPlaza’s new software testing with AI pipeline, enabling early detection, consistent quality enforcement, and faster resolution cycles.

The impact: reliable code, faster shipping, and scalable testing workflows

DevPlaza’s AI-enabled testing ecosystem led to dramatic improvements across the board:

• 50% reduction in time-to-fix for detected bugs

• 60% improvement in unit test coverage, supported by SonarQube AI insights

• 30% fewer CI/CD build failures, resolved proactively through log analysis

• High developer satisfaction, with AI eliminating repetitive review tasks

With software testing with AI now embedded into DevPlaza’s SDLC, teams can focus on building features, not firefighting issues.

Key lessons for engineering leaders

Common pitfalls to avoid

• Relying solely on manual QA for quality

• Delaying investment in software testing with AI

• Building AI tools without developer involvement

What to do instead

• Start with an MVP for bug detection and test optimization

• Leverage structured tools (Git, Jira, SonarQube) as training inputs

• Ensure multi-agent interoperability and observability

Ready to transform software testing with AI?

Let GoML help you bring intelligent testing and automation into your SDLC.

Reach out for an executive AI briefing to explore how a custom AI copilot can help you build better, faster, and more confidently, just like we did with DevPlaza.