Technology Tales

Getting to know Jira, its workflows, test management capabilities and the need for governance

3^rd March 2026

Developed by Atlassian and first released in 2002 as a straightforward bug and issue tracker aimed at software developers, Jira has since grown into a platform used for project management across a wide range of industries and disciplines. The name itself is a truncation of Gojira, the Japanese word for Godzilla, originating as an internal nickname used by Atlassian developers for Bugzilla, the bug-tracking tool they had previously relied upon.

A Family of Products, Each With a Purpose

The Jira ecosystem has expanded well beyond its original single offering, and it is worth understanding what each product is designed to do. Jira (formerly marketed as Jira Software, now unified with Jira Work Management) remains the flagship, built around agile project management with Scrum and Kanban boards at its core. Jira Service Management serves IT operations and service desk teams, handling ticketing and customer support workflows; it originated as Jira Service Desk in 2013, following Atlassian's discovery that nearly 40 per cent of their customers had already adapted the base product for service requests, and it was rebranded in 2020. At the enterprise level, Jira Align connects team delivery to strategic business goals, while Jira Product Discovery helps product teams capture feedback, prioritise ideas and build roadmaps. Together, these products span the full organisational hierarchy, from individual contributors up to executive portfolio management.

Core Features

Agile Boards and Backlog Management

Jira supports a range of agile methodologies, with two primary project templates available to teams. The Scrum template is designed for teams that deliver work in time-boxed sprints, providing backlog management, sprint planning and capacity tracking in a single view. The Kanban template, by contrast, is built around a continuous flow of work, helping teams visualise tasks as they move through each stage of a process without the constraint of fixed iterations. Both templates support custom configurations for teams whose ways of working do not map neatly to either model.

Reporting and Analytics

Jira's reporting suite provides visibility into project progress through various charts and metrics. The Burndown chart tracks remaining story points against the time left in a sprint, offering an indication of whether the team is on course to complete its committed work. The Burnup chart takes a complementary view, tracking how much work has been completed over time and making it straightforward to compare planned scope against actual delivery. These tools are useful for identifying patterns in team performance, though they are most informative when used consistently over several sprints rather than in isolation.

Custom Workflows

Teams can design workflows that reflect their own processes, defining the states an issue passes through and the transitions between them. Automation rules can be applied to handle repetitive steps without manual intervention, reducing administrative overhead on routine tasks. This flexibility is one of the more frequently cited reasons for adopting Jira, though it does require ongoing governance to prevent workflows from becoming inconsistent or unwieldy as teams and processes evolve.

Jira Query Language

Jira Query Language (JQL) provides a structured way to search and filter issues across projects, enabling teams to construct precise queries based on any combination of fields, statuses, assignees, dates and custom attributes. For organisations that invest time in learning it, JQL is a practical tool for building custom reports and dashboards. It is also the underlying mechanism for many of Jira's more advanced filtering and automation features.

Integration Options

Jira connects with a range of tools both within and outside the Atlassian ecosystem. Confluence handles documentation, Bitbucket manages code repositories and links commits directly to Jira issues, and Loom, acquired by Atlassian in 2023, adds asynchronous video communication. Third-party integrations, including Zoom and a broad catalogue of tools available through the Atlassian Marketplace, extend this further for teams with specific requirements.

Test Management With Xray

Jira does not include dedicated test management functionality by default, and teams that need to manage structured test cases alongside their development work typically turn to the Xray plugin, one of the most widely used additions in the Atlassian Marketplace. Xray operates as a native Jira application, meaning it adds new issue types directly to the Jira instance rather than sitting as a separate external tool. The issue types it introduces include Test, Test Set, Test Plan and Test Execution, all of which behave like standard Jira issues and can be searched, filtered and reported on using JQL.

A key capability is requirements traceability: Xray links test cases directly to the user stories and requirements they cover, and connects those in turn to any defects raised during execution. This gives teams a clear picture of test coverage and release readiness without having to leave Jira or reconcile data from separate systems. Test executions can be manual or automated, and Xray integrates with CI/CD toolchains (including Jenkins and Robot Framework) via a REST API, allowing automated test results to be published back into Jira and associated with the relevant requirements.

Xray also supports Behaviour-Driven Development (BDD), enabling teams to write tests in Gherkin syntax and manage them alongside their other Jira work. For organisations already using Jira as their central project management tool, Xray offers a practical route to bringing QA activities into the same workflow rather than maintaining a separate test management system.

Who is Jira Best Suited For?

Jira is generally considered most suitable for larger teams that require detailed control over workflows, reporting and resource allocation, and that have the capacity to dedicate administrative effort to the platform. Smaller teams or those without a dedicated Jira administrator may find the learning curve significant, particularly when configuring custom workflows or working with more advanced reporting features. Pricing is subscription-based, with tiers determined by user count and deployment model (cloud-hosted or self-managed), which means costs can increase substantially as an organisation grows.

Project Types: Tailoring Access to Needs

Jira divides its project spaces into two categories that serve different audiences. Team-managed projects offer simplified configuration for smaller, autonomous teams that want to get started without involving a Jira administrator. Company-managed projects grant administrators full control over customisation, permissions and settings, making them more appropriate for enterprises with complex requirements and multiple teams operating within the same instance. The two types can coexist within the same deployment, giving organisations the option to apply different governance models to different teams as their needs dictate.

Strengths and Limitations

Jira's scalability is one of its more consistent strengths, in terms of both the size of the user base it can support and the complexity of workflows it can accommodate. Its query functions give teams a precise way to interrogate project data, and its breadth of integrations means it can be connected to most standard development and collaboration toolchains.

A significant consideration for any Jira deployment is the degree of upfront decision-making it requires. Because the platform places few constraints on how it is configured, teams must establish their own conventions around workflow design, issue hierarchy, naming and permissions before adoption begins in earnest. Without that groundwork, it is straightforward for individual teams to configure Jira in incompatible ways, making cross-team reporting difficult and creating inconsistencies that become harder to unpick over time. Organisations that treat Jira as something to be governed, rather than simply installed, tend to get considerably more out of it.

The principal technical limitation is its dependence on the wider Atlassian ecosystem. Advanced portfolio planning, capacity forecasting and cross-programme dependency management typically require either a higher-tier plan or additional tooling. Advanced Roadmaps (now called Plans) are available natively within Jira Premium and Enterprise, providing cross-team timeline planning and scenario modelling. For capacity planning, budget tracking and timesheet management, many organisations turn to third-party Marketplace tools such as Tempo. Teams evaluating Jira should factor in both the cost of the appropriate licence tier and any supplementary tooling they are likely to need.

Where to Go From Here

Jira has grown considerably from the issue tracker it was when first released in 2002, and is now used by over 300,000 organisations worldwide. Its capabilities are broad, and its configurability makes it adaptable to a wide range of team structures and workflows. That same configurability, however, means the platform rewards investment in setup and ongoing administration, and organisations should assess whether they have the resources to realise that potential before committing. For those looking to explore further, Atlassian's official guides, its wider documentation, the support portal, the Atlassian Community and the developer documentation are useful starting points, and there are courses from an independent provider too.

Generating Git commit messages automatically using aicommit and OpenAI

25^th October 2025

One of the overheads of using version control systems like Subversion or Git is the need to create descriptive messages for each revision. Now that GitHub has its copilot, it now generates those messages for you. However, that still leaves anyone with a local git repository out in the cold, even if you are uploading to GitHub as your remote repo.

One thing that a Homebrew update does is to highlight other packages that are available, which is how I got to learn of a tool that helps with this, aicommit. Installing is just a simple command away:

brew install aicommit

Once that is complete, you now have a tool that generates messages describing very commit using GPT. For it to work, you do need to get yourself set up with OpenAI's API services and generate a token that you can use. That needs an environment variable to be set to make it available. On Linux (and Mac), this works:

export OPENAI_API_KEY=<Enter the API token here, without the brackets>

Because I use this API for Python scripting, that part was already in place. Thus, I could proceed to the next stage: inserting it into my workflow. For the sake of added automation, this uses shell scripting on my machines. The basis sequence is this:

git add .
git commit -m "<a default message>"
git push

The first line above stages everything while the second commits the files with an associated message (git makes this mandatory, much like Subversion) and the third pushes the files into the GitHub repository. Fitting in aicommit then changes the above to this:

git add .
aicommit
git push

There is now no need to define a message because aicommit does that for you, saving some effort. However, token limitations on the OpenAI side mean that the aicommit command can fail, causing the update operation to abort. Thus, it is safer to catch that situation using the following code:

git add .
if ! aicommit 2>/dev/null; then
    echo "  aicommit failed, using fallback"
    git commit -m "<a default message>"
fi
git push

This now informs me what has happened when the AI option is overloaded and the scripts fallback to a default option that is always available with git. While there is more to my git scripting than this, the snippets included here should get across how things can work. They go well for small push operations, which is what happens most of the time; usually, I do not attempt more than that.

Version control of large files on GitHub

27^th August 2024

When you try pushing large files to a GitHub repository, you may find that you breach its 100 MB limit. When you do, you either need to buy a data pack or exclude the file from being tracked. In my case, I decided that the monthly fee for 50 GB was not overly onerous, so I added that. Excluding such files using the .gitignore functionality makes a lot of sense, too.

If you decide to proceed as I did, you will need to install git-lfs. Since that may vary by operating system, I am leaving to you to look for those details on the website that I have linked to earlier. Activating it for your user account needs the following:

git lfs install

Following that, you need to flag the file or type of file using a command like the following:

git lfs track "[file path with name or search pattern]"

Executing the above adds the file path including the file name or the search pattern (normal operating system wildcards like * work here) to a file named .gitattributes in the root of the repository folder hierarchy. If that file no longer exists, it will get created the first time that this is done. It will also need to be added to the repository using git add like any other file. A general command like the following will also do it anyway, since it covers everything in the relevant folder:

git add .

After making a commit, the next step is to push the contents into GitHub. At this stage, the large file or files will be recognised and sent to large file storage with only a text link in the main area. Everything else will be handled as normal.

While on this subject, I need to add a few words of warning. Pushing a large file to GitHub without doing things up front will cause the operation to fail. That may make the transition over to large file storage all the more tricky, since things will be out of order. Moving everything to a temporary folder and again cloning the repository was how I got out of this impasse when it happened to me. Then, I could get the large file handling set up before getting going again. It is better to sort things like this out at the start of the process, rather than attempting to remedy things part way through the process.

Keeping a file or directory out of a Git or GitHub repository

26^th August 2024

Recently, I have begun to do more version control of files with Git and GitHub. However, GitHub is not a place to keep files with log in credentials. Thus, I wanted to keep these locally but avoid having them being tracked in either Git or GitHub.

Adding the names to a .gitignore file will avoid their inclusion prospectively, but what can you do if they get added in error before you do? The answer that I found is to execute a command like the following:

git rm -r --cached [path to file or directory with its name]

That takes it out of the staging area and allows the .gitignore functionality to do its job. The -r switch makes the command recursive, should you be working with the contents of a directory. Then, the --cached flag is what does the removal from the staging area.

While the aforementioned worked for me when I had an oversight, the following is also suggested:

git update-index --assume-unchanged [path to file or directory with its name]

That may be working without a .gitignore file, which was not how I was doing things. Nevertheless, it may have its uses for someone else, so that is why I include it above.