When performing software program improvement, a primary apply is the versioning and model management of the software program. In lots of fashions of improvement, corresponding to DevSecOps, model management consists of far more than the supply code but additionally the infrastructure configuration, check suites, documentation and lots of extra artifacts. A number of DevSecOps maturity fashions think about model management a primary apply. This consists of the OWASP DevSecOps Maturity Mannequin in addition to the SEI Platform Unbiased Mannequin.
The dominant device for performing model management of supply code and different human readable information is git. That is the device that backs widespread supply code administration platforms, corresponding to GitLab and GitHub. At its most simple use, git is great at incorporating adjustments and permitting motion to completely different variations or revisions of a venture being tracked. Nonetheless, 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 drawback shouldn’t be distinctive to git. Many instruments used for supply management remedy the issue of how one can uniquely establish a set of adjustments from some other in an identical manner. In mercurial, one other supply code administration device a changeset is recognized by a 160-bit identifier.
This implies to check with a model in git, one might must 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 check with variations of software program. Git understands this and so has tags that enable task of human readable names to those variations. That is an additional step after making a commit message and ideally is predicated on the adjustments 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 way of tags) robotically? This weblog submit explores my work on extending the traditional 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 improvement time and stop points with guide versioning.
I’ve just lately been engaged on a venture the place one template repository was reused in about 100 different repository pipelines. It was vital to check and ensure nothing was going to interrupt earlier than pushing out adjustments on the default department, which many of the different tasks pointed to. Nonetheless, with supporting so many customers of the templates there was inevitably one repository that will break or use the script in a non-conventional manner. In just a few circumstances, we would have liked to revert adjustments on the department to allow all repositories to cross their Steady Integration (CI) checks once more. In some circumstances, failing the CI pipeline would hamper improvement for the customers as a result of it was a requirement to cross 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 identical updates as the primary line of improvement. These branches are created in order that customers didn’t get all of the adjustments rolled out on the default department straight away. Lengthy lived branches can change into stale once they don’t obtain updates which were made to the primary line of improvement. These long-lived, stale branches made it troublesome to scrub up the repository with out additionally probably breaking CI pipelines. This grew to become an issue as a result of when reverting the repository to a earlier state, I usually 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 challenge 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 venture and consumer base to take action. We would have liked higher model management within the repository with a technique to sign to customers if a change could be breaking earlier than they up to date.
Typical Commits
To get a deal with on understanding the adjustments to the repository, the builders selected adopting and implementing typical commits. The traditional commits specification affords guidelines for creating an specific commit historical past on high of commit messages. Additionally, by breaking apart a title and physique, the affect of a commit might 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 implementing size necessities, the staff hoped to keep away from commit messages, corresponding to mounted stuff, Working now,and the automated Up to date .gitlab-ci.yml.
For typical commits the next construction is imposed:
<sort> [optional scope]: <description>
[optional body]
[optional footer(s)]
The place <sort> is certainly one of repair, feat, BREAKING CHANGE or others. For this venture we selected barely completely different phrases. The next regex defines the commit message necessities within the venture that this weblog submit impressed:
^(function|bugfix|refactor|construct|main)/ [a-z ]{20,}(rn?|n)(rn?|n)[a-zA-Z].{20,}$
An instance of a traditional commit message is:
function: Add a brand new submit about git commits
The submit explains how one can use typical commits to robotically model a repository
The primary motivation behind implementing typical commits was to scrub up the venture’s git historical past. Having the ability to perceive the adjustments {that a} new model brings in by way of commits alone can pace up code critiques and assist when debugging points or figuring out when a bug was launched. It’s a good apply to commit early and infrequently, 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 venture inspiring this weblog submit makes no suggestions on how typically to commit, it does implement at the very least a 20-character title and 20-character physique for the commit message. This adherence to standard commits by the staff was foundational to the remainder of the work carried out within the venture and described on this weblog submit. With out the flexibility to find out what modified and the affect of the change immediately within the git historical past, it will have sophisticated the hassle and probably pushed in direction of a much less moveable answer. Imposing a 20-character minimal could seem arbitrary and a burden for some smaller adjustments nonetheless implementing this minimal is a technique to get to informative commit messages which have actual that 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, typical commits tie themselves to the notion of semantic versioning, which semver.org 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 staff selected to make use of. Having variations denoted on this manner by way of git tags permits customers to see the affect of the change and replace to a brand new model when prepared. Conversely a staff 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 challenge of robotically updating to a brand new semantic model when a merge request is accepted shouldn’t be a brand new concept. There are instruments and automations that present the identical performance however are usually focused at a selected CI system, corresponding to GitHub Actions, or a selected language, corresponding to Python. For instance, the autosemver python package deal is ready to extract data from git commits to generate a model. The autosemver functionality, nonetheless, depends on being arrange in a setup.py file. Moreover, this venture shouldn’t be extensively used within the python neighborhood. Equally, there’s a semantic-release device, however this requires Node.js within the construct surroundings, which is much less widespread in some tasks and industries. There are additionally open-source GitHub actions that allow automated semantic versioning, which is nice if the venture is hosted on that platform. After evaluating these choices although, it didn’t appear essential to introduce Node.js as a dependency. The venture was not hosted on GitHub, and the venture was not Python-based. On account 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 some weblog posts on the topic. First asubmit by Three Dots Labs helped me establish an answer that was oriented towards GitLab, much like my venture. That submit, nonetheless, left it as much as the reader how one can decide the subsequent tag model. Marc Rooding expanded the Three Dots Labs submit along with his personal weblog submit. 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 an extra guide 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 might not work if a hotfix was dedicated on to the default department. Whereas this final level needs to be disallowed by coverage, the pipeline ought to nonetheless be strong ought to it occur. Given the probability of error on this case of commits on to major, it’s much more vital that tags are generated for rollback and monitoring. Given these elements, I made a decision to choose utilizing the traditional commit varieties from the git historical past to find out the model replace wanted.
Implementation
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 manner (containing all department commits). Which means 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 typical commits and is written appropriately with the proper typical commit sort (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 submit assumes that the repository makes use of a squash-and-merge technique for integrating adjustments. 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 best way. Nonetheless, 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 primary department and branches merged into it. This allows reverting to anyone commit and having a model of the software program that handed by way of no matter overview course of is in place for adjustments going into the trunk or major department of the repository. The right technique needs to be decided for every venture. Many instruments that wrap round git, corresponding to Gitlab, make the method for both technique simple with settings and configuration choices.
With all the traditional commit messages for the reason that final merge to major captured, these commit messages had been handed off to the next_version.py 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 sort 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 subsequent 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 manner the repo is all the time up to date by at the very least a patch model.
The logic within the Python script could be very easy as a result of it was already a dependency within the construct surroundings, and it was clear sufficient what the script was doing. The identical may very well be rewritten in Bash (e.g., the semver device), 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 similar because the default department (normally major). The script portion of the job provides curl and Python to the picture. Subsequent it will get the final commit by way of 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 next_version.py script to generate the subsequent tag model and eventually pushes a tag with the brand new model utilizing curl to the Gitlab API.
```
levels:
- launch
tag-release:
guidelines:
- if: $CI_COMMIT_REF_NAME == $CI_DEFAULT_BRANCH
stage: launch
script:
- apk add curl git python3
- LAST_COMMIT=$(git log -1 --pretty=%B) # Final commit message
- LAST_TAG=$(git describe --tags --abbrev=0) # Final tag within the repo
- NEXT_TAG=$(python3 next_version.py ${LAST_TAG} ${LAST_COMMIT})
- echo Pushing new model tag ${NEXT_TAG}
- curl -k --request POST --header "PRIVATE-TOKEN:${TAG_TOKEN}" --url "${CI_API_V4_URL}/tasks/${CI_PROJECT_ID}/repository/tags?tag_name=${NEXT_TAG}&ref=major"
```
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 by way of the if/elseif/else statements to increment the final tag to the suitable subsequent tag and prints out the subsequent 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"
else:
patch_version = int(brokenup_tag[2])
next_tag = brokenup_tag[0]+"."+brokenup_tag[1]+"."+str(patch_version+1)
print(next_tag)
```
Lastly, the final step is to push the brand new model to the git repository. As talked about, this venture was hosted in Gitlab, which offers 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 major 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 (major) 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 major motivation is to keep away from a scenario the place a chunk of software program is in both a state of model lock (the lack 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 working into points the place an API name is modified or eliminated, and software program is not going to 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 standard commits, is the flexibility to condense these adjustments right into a changelog giving builders and customers, “a curated, chronologically ordered listing of notable adjustments for every model of a venture”. This helps builders and customers know what has modified, along with the affect.
Having a technique to sign to customers when a library or different piece of software program has modified is vital. Even so, it’s not essential to have versioning be a guide course of for builders. There are merchandise and free, open supply options to this challenge, however they might not all the time be a very good match for any explicit improvement surroundings. On the subject of safety important software program, corresponding to encryption or authentication, it’s a good suggestion to not roll your personal. Nonetheless, for steady integration (CI) jobs typically business off-the shelf (COTS) options are extreme and produce important 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 exhibits how the method might 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).