Insights & Ideas
Learn about all things quality, agile, project management and DevOps from our content authors.
Company Blog
SpiraPlan vs. Jira Data Center for Heads of DevOps: Why DevOps Leaders Need Lifecycle Traceability, Not Just Issue Tracking
May 21, 2026
For Heads of DevOps, Platform Engineering leaders, Release Engineering leaders, and DevSecOps executives, the challenge is not simply managing tickets. It is orchestrating the entire path from idea to production.
DevOps leaders need to connect planning, code, builds, pipelines, tests, defects, releases, risks, approvals, infrastructure, and deployment evidence. They are responsible for accelerating delivery while improving reliability, governance, security, and auditability. That means the core system of record has to do more than track backlog items and sprint tasks.
SpiraPlan vs. Jira Data Center for Heads of QA / QE: Why Quality Leaders Need More Than Issue Tracking
May 18, 2026
For Heads of QA, Heads of QE, Test Directors, and Quality Engineering leaders, the software delivery landscape has changed dramatically. Quality is no longer a final-stage testing function. It is a continuous, risk-aware, traceability-driven discipline that spans requirements, development, automation, exploratory testing, release readiness, compliance, customer impact, and executive reporting.
Jira Data Center has historically played an important role in this ecosystem. It is a powerful work tracking platform, and many QA teams use it to manage defects, sprint work, test-related tasks, and agile delivery workflows. But Jira Data Center was not originally designed as a complete quality engineering management platform.
That distinction matters.
Testing Chatbots with Spira, Rapise and SureWire from Inflectra
May 18, 2026
AI chatbots are quickly moving from experimental side projects to production systems that answer customer questions, support employees, triage requests, summarize knowledge, and interact with business workflows. But testing a chatbot is very different from testing a traditional web application:
- A chatbot does not always return the same response twice.
- It may depend on a prompt, a model version, a knowledge base, a temperature setting, a user persona, a conversation history, or a retrieval-augmented generation pipeline.
- It may also be exposed to unexpected user behavior, adversarial prompts, sensitive data, and policy boundaries that are difficult to validate with traditional test scripts alone.
That is why chatbot testing requires a layered approach that combines both traditional deterministic testing and new agent-based testing approaches.
