Thursday, July 25, 2024

Versioning with Git Tags and Standard Commits

When performing software program growth, a fundamental observe is the versioning and model management of the software program. In lots of fashions of growth, corresponding to DevSecOps, model management consists of far more than the supply code but additionally the infrastructure configuration, take a look at suites, documentation and lots of extra artifacts. A number of DevSecOps maturity fashions take into account model management a fundamental observe. This consists of the OWASP DevSecOps Maturity Mannequin in addition to the SEI Platform Impartial Mannequin.

The dominant software for performing model management of supply code and different human readable information is git. That is the software that backs common supply code administration platforms, corresponding to GitLab and GitHub. At its most elementary use, git is great at incorporating modifications and permitting motion to completely different variations or revisions of a challenge being tracked. Nevertheless, one draw back is the mechanism git makes use of to call the variations. Git variations or commit IDs are a SHA-1 hash. This downside isn’t distinctive to git. Many instruments used for supply management resolve the issue of methods to uniquely establish a set of modifications from another in an identical means. In mercurial, one other supply code administration software a changeset is recognized by a 160-bit identifier.

This implies to discuss with a model in git, one could need to specify an ID corresponding to 521747298a3790fde1710f3aa2d03b55020575aa (or the shorter however no much less descriptive 52174729). This isn’t a great way for builders or customers to discuss with variations of software program. Git understands this and so has tags that permit task of human readable names to those variations. That is an additional step after making a commit message and ideally relies on the modifications launched within the commit. That is duplication of effort and a step that may very well be missed. This results in the central query: How can we automate the task of variations (by tags) mechanically? This weblog publish explores my work on extending the standard commit paradigm to allow automated semantic versioning with git tags to streamline the event and deployment of software program merchandise. This automation is meant to avoid wasting growth time and stop points with handbook versioning.

I’ve lately been engaged on a challenge the place one template repository was reused in about 100 different repository pipelines. It was essential to check and ensure nothing was going to interrupt earlier than pushing out modifications on the default department, which a lot of the different initiatives pointed to. Nevertheless, with supporting so many customers of the templates there was inevitably one repository that will break or use the script in a non-conventional means. In just a few instances, we would have liked to revert modifications on the department to allow all repositories to go their Steady Integration (CI) checks once more. In some instances, failing the CI pipeline would hamper growth for the customers as a result of it was a requirement to go the script checks of their CI pipelines earlier than constructing and different levels. Consequently, some customers would create a long-lived department within the template repository I helped preserve. These long-lived branches are separate variations that don’t get the entire similar updates as the principle line of growth. These branches are created in order that customers didn’t get all of the modifications rolled out on the default department straight away. Lengthy lived branches can change into stale after they don’t obtain updates which have been made to the principle line of growth. These long-lived, stale branches made it tough to wash up the repository with out additionally presumably breaking CI pipelines. This turned an issue as a result of when reverting the repository to a earlier state, I typically needed to level to a reference, corresponding to HEAD~3, or the hash of the earlier commit earlier than the breaking change was built-in into the default department. This concern was exacerbated by the truth that the repository was not utilizing git tags to indicate new variations.

Whereas there are some arguments for utilizing the newest and best model of a brand new software program library or module (sometimes called “reside at head,”) this technique of working was not working for this challenge and consumer base to take action. We wanted higher model management within the repository with a approach to sign to customers if a change can be breaking earlier than they up to date.

Standard Commits

To get a deal with on understanding the modifications to the repository, the builders selected adopting and imposing standard commits. The traditional commits specification provides guidelines for creating an express commit historical past on prime of commit messages. Additionally, by breaking apart a title and physique, the influence of a commit could be extra simply deduced from the message (assuming the writer understood the change implications). The usual additionally ties to semantic versioning (extra on that in a minute). Lastly, by imposing size necessities, the crew hoped to keep away from commit messages, corresponding to fastened stuff, Working now,and the automated Up to date .gitlab-ci.yml.

For standard commits the next construction is imposed:

<kind> [optional scope]: <description>

[optional body]

[optional footer(s)]

The place <kind> is considered one of repair, feat, BREAKING CHANGE or others. For this challenge we selected barely completely different phrases. The next regex defines the commit message necessities within the challenge that this weblog publish impressed:

^(function|bugfix|refactor|construct|main)/ [a-z ]{20,}(rn?|n)(rn?|n)[a-zA-Z].{20,}$

An instance of a standard commit message is:

function: Add a brand new publish about git commits

The publish explains methods to use standard commits to mechanically model a repository

The primary motivation behind imposing standard commits was to wash up the challenge’s git historical past. With the ability to perceive the modifications {that a} new model brings in by commits alone can velocity up code opinions and assist when debugging points or figuring out when a bug was launched. It’s a good observe to commit early and sometimes, although the stability between committing each failed experiment with the code and never cluttering the historical past has led to many completely different git methods. Whereas the challenge inspiring this weblog publish makes no suggestions on how usually to commit, it does implement a minimum of a 20-character title and 20-character physique for the commit message. This adherence to standard commits by the crew was foundational to the remainder of the work performed within the challenge and described on this weblog publish. With out the flexibility to find out what modified and the influence of the change instantly within the git historical past, it could have sophisticated the trouble and doubtlessly pushed in direction of a much less moveable resolution. Implementing a 20-character minimal could appear arbitrary and a burden for some smaller modifications nonetheless imposing this minimal is a approach to get to informative commit messages which have actual which means for a human that’s reviewing them. As famous above this restrict can drive builders to rework a commit message from, ci working to Up to date variable X within the ci file to repair construct failures with GCC.

Semantic Versioning

As famous, standard commits tie themselves to the notion of semantic versioning, which defines as “a easy algorithm and necessities that dictate how model numbers are assigned and incremented.” The usual denotes a model quantity consisting of MAJOR.MINOR.PATCH the place MAJOR is any change that’s incompatible, MINOR is a backward appropriate change with new options, and PATCH is a backward appropriate bug repair. Whereas there are different versioning methods and a few famous points with semantic versioning, that is the conference that the crew selected to make use of. Having variations denoted on this means through git tags permits customers to see the influence of the change and replace to a brand new model when prepared. Conversely a crew may proceed to reside at head till they bumped into a problem after which extra simply see what variations had been accessible to roll again to.

COTS Options

This concern of mechanically updating to a brand new semantic model when a merge request is accepted isn’t a brand new concept. There are instruments and automations that present the identical performance however are typically focused at a selected CI system, corresponding to GitHub Actions, or a selected language, corresponding to Python. For example, the autosemver python package deal is ready to extract info from git commits to generate a model. The autosemver functionality, nonetheless, depends on being arrange in a file. Moreover, this challenge isn’t extensively used within the python group. Equally, there’s a semantic-release software, however this requires Node.js within the construct setting, which is much less widespread in some initiatives and industries. There are additionally open-source GitHub actions that allow automated semantic versioning, which is nice if the challenge is hosted on that platform. After evaluating these choices although, it didn’t appear essential to introduce Node.js as a dependency. The challenge was not hosted on GitHub, and the challenge was not Python-based. Because of these limitations, I made a decision to implement my very own minimal viable product (MVP) for this performance.

Different Implementations

Having determined towards off-the-shelf options to the issue of versioning the repo, subsequent I turned to a couple weblog posts on the topic. First apublish by Three Dots Labs helped me establish an answer that was oriented towards GitLab, much like my challenge. That publish, nonetheless, left it as much as the reader methods to decide the following tag model. Marc Rooding expanded the Three Dots Labs publish together with his personal weblog publish. Right here he suggests utilizing merge request labels and pulling these from the API to determine the model to bump the repository to. This strategy had three drawbacks that I recognized. First, it appeared like a further handbook step so as to add the proper tags to the merge request. Second, it depends on the API to get tags from the merge request. Lastly, this could not work if a hotfix was dedicated on to the default department. Whereas this final level ought to be disallowed by coverage, the pipeline ought to nonetheless be sturdy ought to it occur. Given the probability of error on this case of commits on to fundamental, it’s much more essential that tags are generated for rollback and monitoring. Given these elements, I made a decision to decide on utilizing the standard commit varieties from the git historical past to find out the model replace wanted.


This template repository referenced within the introduction makes use of GitLab because the CI/CD system. Consequently, I wrote a pipeline job to extract the git historical past for the default department after being merged. The pipeline job assumes that both (1) there’s a single commit, (2) the commits had been squashed and that every correctly formatted commit message is contained within the squash commit, or (3) a merge commit is generated in the identical means (containing all department commits). Because of this the setup proposed right here can work with squash-and-merge or rebase-and-fast-forward methods. It additionally handles commits on to the default department, if anybody would try this. In every case, the belief is that the commit–whether merger, squash, or regular–still matches the sample for standard commits and is written appropriately with the proper standard commit kind (main, function, and many others.). The final commit is saved in a variable LAST_COMMIT in addition to the final tag within the repo LAST_TAG.

A fast apart on merging methods. The answer proposed on this weblog publish assumes that the repository makes use of a squash-and-merge technique for integrating modifications. There are a number of defensible arguments for each a linear historical past with all intermediate commits represented or for a cleaner historical past with solely a single commit per model. With a full, linear historical past one can see the event of every function and all trials and errors a developer had alongside the way in which. Nevertheless, one draw back is that not each model of the repository represents a working model of the code. With a squash-and-merge technique, when a merge is carried out, all commits in that merge are condensed right into a single commit. This implies that there’s a one-to-one relationship with commits on the principle department and branches merged into it. This allows reverting to anybody commit and having a model of the software program that handed by no matter evaluate course of is in place for modifications going into the trunk or fundamental department of the repository. The proper technique ought to be decided for every challenge. Many instruments that wrap round git, corresponding to Gitlab, make the method for both technique easy with settings and configuration choices.

With all the standard commit messages for the reason that final merge to fundamental captured, these commit messages had been handed off to the Python script. The logic is fairly easy. For inputs there’s the present model quantity and the final commit message. The script merely seems for the presence of “main” or “function” because the commit kind within the message. It really works on the premise that if any commit within the department’s historical past is typed as “main” the script is finished and outputs the following main model. If not discovered, the script searches for “minor” and if not discovered the merge is assumed to be a patch model. On this means the repo is all the time up to date by a minimum of a patch model.

The logic within the Python script may be very easy as a result of it was already a dependency within the construct setting, and it was clear sufficient what the script was doing. The identical may very well be rewritten in Bash (e.g., the semver software), in one other scripting language, or as a pipeline of *nix instruments.

This code defines a GitLab pipeline with a single stage (launch) that has a single job in that stage (tag-release). Guidelines are specified that the job solely runs if the commit reference title is identical because the default department (normally fundamental). The script portion of the job provides curl and Python to the picture. Subsequent it will get the final commit through the git log command and shops it within the LAST_COMMIT variable. It does the identical with the final tag. The pipeline then makes use of the script to generate the following tag model and eventually pushes a tag with the brand new model utilizing curl to the Gitlab API.



- launch




stage: launch


- apk add curl git python3

- LAST_COMMIT=$(git log -1 --pretty=%B) # Final commit message

- LAST_TAG=$(git describe --tags --abbrev=0) # Final tag within the repo

- NEXT_TAG=$(python3 ${LAST_TAG} ${LAST_COMMIT})

- echo Pushing new model tag ${NEXT_TAG}

- curl -k --request POST --header "PRIVATE-TOKEN:${TAG_TOKEN}" --url "${CI_API_V4_URL}/initiatives/${CI_PROJECT_ID}/repository/tags?tag_name=${NEXT_TAG}&ref=fundamental"


The next Python script takes in two arguments, the final tag within the repo and the final commit message. The script then finds the kind of commit through the if/elseif/else statements to increment the final tag to the suitable subsequent tag and prints out the following tag to be consumed by the pipeline.

import sys

last_tag = sys.argv[1]
last_commit = sys.argv[2]
next_tag = ""
brokenup_tag = last_tag.break up(".")

if "main/" in last_commit:
major_version = int(brokenup_tag[0])
next_tag = str(major_version+1)+".0.0"

elif "function/" in last_commit:
feature_version = int(brokenup_tag[1])
next_tag = brokenup_tag[0]+"."+str(feature_version+1)+".0"

patch_version = int(brokenup_tag[2])
next_tag = brokenup_tag[0]+"."+brokenup_tag[1]+"."+str(patch_version+1)


Lastly, the final step is to push the brand new model to the git repository. As talked about, this challenge was hosted in Gitlab, which gives an API for git tags within the repo. The NEXT_TAG variable was generated by the Python script, after which we used curl to POST a brand new tag to the repository’s /tags endpoint. Encoded within the URL is the ref to make the tag from. On this case it’s fundamental however may very well be adjusted. The one gotcha right here is, as acknowledged beforehand, that the job runs solely on the default pipeline after the merge takes place. This ensures the final commit (HEAD) on the default department (fundamental) is tagged. Within the above GitLab job, the TAG_TOKEN is a CI variable whose worth is a deploy token. This token must have the suitable permissions arrange to have the ability to write to the repository.

Subsequent Steps

Semantic versioning’s fundamental motivation is to keep away from a state of affairs the place a chunk of software program is in both a state of model lock (the shortcoming to improve a package deal with out having to launch new variations of each dependent package deal) or model promiscuity (assuming compatibility with extra future variations than is cheap). Semantic versioning additionally helps to sign to customers and keep away from operating into points the place an API name is modified or eliminated, and software program won’t interoperate. Monitoring variations informs customers and different software program that one thing has modified. This model quantity, whereas useful, doesn’t let a consumer know what has modified. The following step, constructing on each discrete variations and traditional commits, is the flexibility to condense these modifications right into a changelog giving builders and customers, “a curated, chronologically ordered checklist of notable modifications for every model of a challenge”. This helps builders and customers know what has modified, along with the influence.

Having a approach to sign to customers when a library or different piece of software program has modified is essential. Even so, it isn’t essential to have versioning be a handbook course of for builders. There are merchandise and free, open supply options to this concern, however they might not all the time be match for any explicit growth setting. Relating to safety essential software program, corresponding to encryption or authentication, it’s a good suggestion to not roll your individual. Nevertheless, for steady integration (CI) jobs generally industrial off-the shelf (COTS) options are extreme and produce vital dependencies with them. On this instance, with a 6-line BASH script and a 15-line Python script, one can implement auto semantic versioning in a pipeline job that (within the deployment examined) runs in ~ 10 seconds. This instance additionally reveals how the method could be minimally tied to a selected construct or CI system and never depending on a selected language or runtime (even when Python was used out of comfort).

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