SShortSingh.
TopTechnologyAI & MLStartupsProgrammingScienceBusinessFinanceCrypto & Web3World
Back to feed
Programming

Why Jira's worklogAuthor JQL returns nothing for Tempo Timesheets users

0
·2 views

A commonly used Jira JQL filter — worklogAuthor = currentUser() — returns no results for users who log time via Tempo Timesheets, even after a full week of entries. This happens because Tempo stores worklogs in its own database and syncs only an anonymised 'shadow' copy to Jira, attributed to Tempo's app account rather than the individual user. Tempo has intentionally designed this behaviour since at least 2018 to enforce its own worklog visibility rules, which Jira's native display cannot accommodate. As a result, any tool that reads Jira's worklog data — including JQL queries, Jira Automation, REST API exports, and BI integrations — cannot identify the actual author of time entries logged through Tempo. Teams needing accurate author attribution must query Tempo's own API directly instead of relying on Jira's worklog records.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

Why AI Automation Workflows That Shine in Demos Often Fail in Production

AI automation pipelines that appear seamless during demos frequently break down once real-world conditions are introduced, such as waiting for human approvals, retrying failed API calls, or pausing for external events. The core problem is that most demo workflows are built as simple request-response sequences, but any workflow that must outlive a single HTTP request requires deliberate distributed systems design. Platform constraints compound the issue — AWS Lambda's 15-minute execution limit and Cloudflare Workers' CPU-time caps make long-running agent tasks unsuitable for basic webhook handlers. Tools like self-hosted n8n shift into a split architecture in queue mode, introducing Redis brokers, worker processes, and shared state management that far exceed typical low-code expectations. Frameworks such as Inngest address this more cleanly by not tying concurrency to waiting or sleeping steps, making them better suited for always-on AI agents that pause mid-workflow.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

How to Build a Secure Authentication System: Key Concepts and Best Practices

A secure authentication system is a critical component of modern software applications, required across websites, banking platforms, mobile apps, and business tools. Beyond a basic login form, a robust system must handle password protection, input validation, session management, and account recovery. Core concepts include distinguishing authentication — verifying who a user is — from authorization, which governs what resources a user can access. Such a system can be built using a range of technologies including Python, JavaScript, Node.js, and databases like MySQL or MongoDB, while the underlying security principles remain consistent. Key implementation areas covered include password hashing, multi-factor authentication, role-based access control, rate limiting, and security logging.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

Dev Team Builds MCP Server to Let AI Agents Sync Code Directly with Allure TestOps

A developer built a custom MCP server for Allure TestOps after noticing that coding agents accelerated development but left manual bottlenecks in the testing workflow. Previously, developers relied on a human to manually transfer information between the codebase, the test management system, and other tools like MockServer. With the MCP integration, an AI agent can read an existing Android UI test, parse its steps, and automatically create or update the corresponding test case in Allure TestOps without human intervention. The same agent can also read a TestOps test case and generate the required mocks via a separate MockServer MCP, linking multiple systems in a single automated flow. The team now uses the agent to audit test coverage — identifying which test cases are automated, which are missing documentation, and where code changes have made existing descriptions stale.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

Developers Build AI Customer Support Agent With Persistent Memory Across Sessions

A team of developers has created a Customer Support Memory Agent as a hackathon project, designed to solve the common problem of customers having to repeatedly re-explain their issues to support systems. The agent uses a memory tool called Hindsight to retain and recall key details from past customer interactions, such as previous tickets, refunds, and billing disputes. When a returning customer sends a new message, the system retrieves relevant stored context and passes it to a Groq large language model, which then generates a personalized, context-aware response. The architecture is built on a Python and Flask backend with a web UI frontend, and it maintains separate memory profiles for individual customers. The memory system can also organize past interactions into a graph structure, visualizing relationships between events in a customer's history.

0 comments Read more at DEV Community