Closed-Loop Autonomous Software Development via Jira-Integrated Backlog Orchestration: A Case Study in Deterministic Control and Safety-Constrained Automation

TL;DR

This paper introduces a control-architecture-based closed-loop system for autonomous software development, integrating Jira and AI assistance, demonstrating high success and safety in managing complex workflows.

Contribution

It presents a novel control-oriented framework for autonomous software lifecycle management with safety, traceability, and AI integration, validated through extensive case study results.

Findings

100% success in 152 initial runs with high confidence interval

System managed over 1,600 backlog items across seven task families

Autonomous dispatch completed 6 out of 10 security tickets without human intervention

Abstract

This paper presents a closed-loop system for software lifecycle management framed as a control architecture rather than a code-generation tool. The system manages a backlog of approximately 1,602 rows across seven task families, ingests 13 structured source documents, and executes a deterministic seven-stage pipeline implemented as seven scheduled automation lanes. The automation stack comprises approximately 12,661 lines of Python across 23 scripts plus 6,907 lines of versioned prompt specifications, with checkpoint-based time budgets, 101 exception handlers, and 12 centralized lock mechanisms implemented through four core functions and eight reusable patterns. A Jira Status Contract provides externally observable collision locking, and a degraded-mode protocol supports continued local operation when Jira is unavailable. Artificial-intelligence assistance is bounded by structured context packages, configured resource caps, output re-validation, and human review gates. A formal evaluation of the initial 152-run window yielded 100% terminal-state success with a 95% Clopper-Pearson interval of [97.6%, 100%]; the system has since accumulated more than 795 run artifacts in continuous operation. Three rounds of adversarial code review identified 51 findings, all closed within the study scope (48 fully remediated, 3 closed with deferred hardening), with zero false negatives within the injected set. In an autonomous security ticket family of 10 items, six were completed through pipeline-autonomous dispatch and verification, two required manual remediation, and two were closed by policy decision. The results indicate that bounded, traceable lifecycle automation is practical when autonomy is embedded within explicit control, recovery, and audit mechanisms.