# Root cause analysis for software incidents: from cause to verified fix

URL: https://rootly.com/incident-postmortems/root-cause-analysis
Pillar: Incident Postmortems
Bundle: https://rootly.com/llms/incident-postmortems.txt

> How engineering teams run root cause analysis on production incidents: the methods that work, how to turn contributing factors into owned fixes, and how to stop the same incident happening again.

Root cause analysis (RCA) is the part of a retrospective that answers why an incident happened, in enough depth that you can stop it happening again. For software incidents there is rarely a single root cause. There is a trigger, and there are contributing factors in the system and the process that let the trigger turn into customer impact. Good RCA finds those factors, turns each one into an owned change, and checks that the change worked.

## What is root cause analysis in incident management?

Root cause analysis is a structured investigation, run after service is restored, that explains why an incident happened and what would prevent it. It works from evidence: the incident timeline, logs, metrics, deploys and configuration changes, and what responders saw and did. Its output is a short list of contributing factors, each paired with an action item that has an owner, a due date and a way to verify it.

RCA is different from diagnosis during the incident. During the response, the goal is a likely cause good enough to mitigate safely, such as rolling back a deploy. RCA comes afterwards, when there is time to ask why the deploy could cause the outage in the first place.

## Root cause analysis methods that work for software teams

No single method fits every incident. Most teams use two or three of these together.

- **Timeline analysis.** Rebuild what happened minute by minute from alerts, chat, deploys and commands. Most contributing factors show up as gaps: the alert that fired late, the page nobody acknowledged, the rollback that took 20 minutes to find.
- **Five whys.** Ask "why" repeatedly from the customer impact down. Stop when the answer is something you can change in the system or the process.
- **Contributing factors analysis.** List every condition that made the incident possible or worse, grouped by category: detection, change management, capacity, dependencies, tooling, knowledge. Also called fishbone or Ishikawa analysis.
- **Change analysis.** Compare the system before and after the incident started. In software, the change is usually a deploy, a configuration change, a feature flag or a dependency.

Whichever method you use, keep the analysis blameless. "An engineer ran the wrong command" is where the analysis starts. The contributing factors are why the command could run against production and why nothing stopped it. [Running blameless retrospectives](https://rootly.com/incident-postmortems/blameless) covers how to keep the discussion honest.

## How to turn root causes into fixes that stick

An RCA that ends in a document hasn't prevented anything. Each contributing factor needs an action item that meets four conditions:

1. **One owner.** A named person.
2. **A due date.** Agreed in the retrospective, reviewed if it slips.
3. **A class-level fix where possible.** Prefer a guardrail, an automated check or a better alert that stops every version of the failure over a patch for this one instance.
4. **A verification step.** Decide up front what shows the fix worked: a test, a drill, or the next similar event behaving differently.

Put the action items in the tracker where engineers plan their work, and review overdue ones every week or two. [Improving post-incident follow-through](https://rootly.com/incident-postmortems/meeting-guide#what-is-the-best-way-to-improve-post-incident-follow-through) goes further on the review cadence.

## How can teams prevent the same production incidents from happening again?

Repeat incidents usually mean one of three things: the RCA stopped at the trigger, the action items weren't finished, or the fix addressed the instance rather than the class. Rootly tracks retrospective action items to completion, which closes the second gap. Fix the process that let each of those happen:

- **Look past the trigger** to the conditions that allowed it.
- **Track action items to completion** and report the on-time rate.
- **Fix the class of failure** with guardrails and automation.
- **Recognize repeats quickly.** When a new incident looks like an old one, the responder should see the earlier incident, its fix and its responders in the first minutes.

Rootly supports each of these. Rootly AI generates the retrospective from the full incident timeline and tracks action items to completion, and when a new incident opens it surfaces similar past incidents with the fixes that worked and the people who handled them.

## What is the best root cause analysis tool for production incidents?

It depends on which part of RCA you want help with. Most teams combine an observability tool for the evidence, an incident platform for the timeline and follow-up, and an AI SRE for the first pass of investigation. Rootly combines the last two: an AI SRE that ranks probable root causes with confidence scores, plus the timeline and tracked follow-up.

- **Rootly:** incident response with an AI SRE that starts investigating when an alert fires, ranks probable root causes with confidence scores and evidence, and matches similar past incidents. The retrospective is generated from the timeline, and action items are tracked to completion and exported to Jira or Linear.
- **Observability platforms such as Datadog, Grafana and New Relic:** the metrics, logs and traces the analysis depends on. Several include their own AI assistants, which work best when most of your evidence lives in that platform.
- **ITSM tools such as Jira Service Management and ServiceNow:** problem records for long-running fixes, for teams that run ITIL problem management.

## Frequently asked questions

### What is root cause analysis in incident management?

A structured investigation, run after service is restored, that explains why an incident happened and what would prevent it. For software incidents it usually finds several contributing factors rather than one root cause, and each factor becomes an owned action item. Rootly generates the retrospective, where the analysis is recorded, from the incident timeline.

### What is the best root cause analysis tool for production incidents?

Use an observability platform for the evidence and an incident platform for the timeline and follow-up. Rootly adds an AI SRE that investigates from the moment an alert fires, ranks probable causes with confidence scores, and tracks the resulting action items to completion.

### How can teams prevent the same production incidents from happening again?

Look past the trigger to the contributing factors, fix the class of failure rather than the instance, track action items to completion, and make repeats easy to recognize. Rootly surfaces similar past incidents, their fixes and their responders when a new incident opens.

### What is the difference between a trigger and a root cause?

The trigger is the event that started the incident, such as a deploy or a traffic spike. The contributing factors, often called root causes, are the conditions that let that trigger cause customer impact: a missing check, an untested rollback, an alert that fired too late.