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When performing software program improvement, a fundamental observe is the versioning and model management of the software program. In lots of fashions of improvement, equivalent to DevSecOps, model management contains far more than the supply code but in addition the infrastructure configuration, check suites, documentation and lots of extra artifacts. A number of DevSecOps maturity fashions take into account model management a fundamental observe. This contains 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 recordsdata is git. That is the software that backs common supply code administration platforms, equivalent to GitLab and GitHub. At its most simple use, git is superb at incorporating modifications and permitting motion to totally different variations or revisions of a undertaking 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 drawback just isn’t distinctive to git. Many instruments used for supply management clear up the issue of the right way to uniquely establish a set of modifications from every other in an analogous method. In mercurial, one other supply code administration software a changeset is recognized by a 160-bit identifier.
This implies to confer with a model in git, one could need to specify an ID equivalent to 521747298a3790fde1710f3aa2d03b55020575aa (or the shorter however no much less descriptive 52174729). This isn’t a great way for builders or customers to confer 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 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 way of tags) robotically? This weblog submit 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 improvement time and forestall points with handbook versioning.
I’ve not too long ago been engaged on a undertaking the place one template repository was reused in about 100 different repository pipelines. It was necessary to check and ensure nothing was going to interrupt earlier than pushing out modifications on the default department, which many 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 method. In just a few instances, we would have liked to revert modifications on the department to allow all repositories to cross their Steady Integration (CI) checks once more. In some instances, 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 phases. Consequently, some customers would create a long-lived department within the template repository I helped keep. These long-lived branches are separate variations that don’t get all the identical updates as the primary line of improvement. 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 develop into stale once they don’t obtain updates which have been 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 turned an issue as a result of when reverting the repository to a earlier state, I usually needed to level to a reference, equivalent to HEAD~3, or the hash of the earlier commit earlier than the breaking change was built-in into the default department. This subject 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 most recent and best model of a brand new software program library or module (also known as “stay at head,”) this methodology of working was not working for this undertaking and person base to take action. We would have liked higher model management within the repository with a technique to sign to customers if a change can be breaking earlier than they up to date.
Typical Commits
To get a deal with on understanding the modifications to the repository, the builders selected adopting and imposing typical commits. The traditional commits specification provides guidelines for creating an express commit historical past on high of commit messages. Additionally, by breaking apart a title and physique, the impression of a commit could be extra simply deduced from the message (assuming the creator understood the change implications). The usual additionally ties to semantic versioning (extra on that in a minute). Lastly, by imposing size necessities, the group hoped to keep away from commit messages equivalent to, fastened stuff, Working now,and the automated Up to date .gitlab-ci.yml.
For typical commits the next construction is imposed:
<kind> [optional scope]: <description>
[optional body]
[optional footer(s)]
The place <kind> is one in every of repair, feat, BREAKING CHANGE or others. For this undertaking we selected barely totally different phrases. The next regex defines the commit message necessities within the undertaking that this weblog submit impressed:
^(characteristic|bugfix|refactor|construct|main)/ [a-z ]{20,}(rn?|n)(rn?|n)[a-zA-Z].{20,}$
An instance of a traditional commit message is:
characteristic: Add a brand new submit about git commits
The submit explains the right way to use typical commits to robotically model a repository
The principle motivation behind imposing typical commits was to scrub up the undertaking’s git historical past. Having the ability to perceive the modifications {that a} new model brings in by way of commits alone can velocity up code critiques and assist when debugging points or figuring out when a bug was launched. It’s a good observe 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 totally different git methods. Whereas the undertaking inspiring this weblog submit makes no suggestions on how typically to commit, it does implement no less than a 20-character title and 20-character physique for the commit message. This adherence to standard commits by the group was foundational to the remainder of the work achieved within the undertaking and described on this weblog submit. With out the flexibility to find out what modified and the impression of the change immediately within the git historical past, it will have difficult the trouble and probably pushed in the direction of a much less moveable answer. Implementing a 20-character minimal could seem arbitrary and a burden for some smaller modifications nevertheless imposing this minimal is a technique to get to informative commit messages which have actual which means for a human that’s reviewing them. As famous above this restrict can power builders to remodel 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 suitable change with new options, and PATCH is a backward suitable bug repair. Whereas there are different versioning methods and a few famous points with semantic versioning, that is the conference that the group selected to make use of. Having variations denoted on this method by way of git tags permits customers to see the impression of the change and replace to a brand new model when prepared. Conversely a group may proceed to stay at head till they bumped into a difficulty after which extra simply see what variations have been out there to roll again to.
COTS Options
This subject of robotically updating to a brand new semantic model when a merge request is accepted just isn’t a brand new thought. There are instruments and automations that present the identical performance however are usually focused at a particular CI system, equivalent to GitHub Actions, or a particular language, equivalent to Python. For example, the autosemver python bundle is ready to extract data from git commits to generate a model. The autosemver functionality, nevertheless, depends on being arrange in a setup.py file. Moreover, this undertaking just isn’t extensively used within the python group. Equally, there’s a semantic-release software, however this requires Node.js within the construct atmosphere, 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 undertaking is hosted on that platform. After evaluating these choices although, it didn’t appear essential to introduce Node.js as a dependency. The undertaking was not hosted on GitHub, and the undertaking was not Python-based. Resulting from 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 asubmit by Three Dots Labs helped me establish an answer that was oriented towards GitLab, just like my undertaking. That submit, nevertheless, left it as much as the reader the right way to 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 method 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 might 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 strong ought to it occur. Given the chance of error on this case of commits on to most important, it’s much more necessary 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.
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 have 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 method (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 (although who would try this?). In every case, the idea is that the commit (whether or not merger squash or common) nonetheless matches the sample for typical commits and is written appropriately with the proper typical commit kind (main, characteristic, 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 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 characteristic and all trials and errors a developer had alongside the best way. 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 primary department and branches merged into it. This allows reverting to anybody commit and having a model of the software program that handed by way of no matter assessment course of is in place for modifications going into the trunk or most important department of the repository. The proper technique ought to be decided for every undertaking. Many instruments that wrap round git, equivalent to Gitlab, make the method for both technique simple with settings and configuration choices.
With all the standard commit messages for the reason that final merge to most important captured, these commit messages have 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 to be for the presence of “main” or “characteristic” 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 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 method the repo is all the time up to date by no less than 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 atmosphere, 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 identify is similar because the default department (normally most important). 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 at last pushes a tag with the brand new model utilizing curl to the Gitlab API.
```
phases:
- 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 "${CI_API_V4_URL}/initiatives/${CI_PROJECT_ID}/repository/tags?tag_name=${NEXT_TAG}&ref=most important"
```
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.cut up(".")
if "main/" in last_commit:
major_version = int(brokenup_tag[0])
next_tag = str(major_version+1)+".0.0"
elif "characteristic/" 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 undertaking 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 most important 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 (most important) 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 most important 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 bundle with out having to launch new variations of each dependent bundle) 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 person 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 listing of notable modifications for every model of a undertaking”. This helps builders and customers know what has modified, along with the impression.
Having a technique to sign to customers when a library or different piece of software program has modified is necessary. 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 subject, however they could not all the time be a great match for any specific improvement atmosphere. Relating to safety essential software program, equivalent to encryption or authentication, it’s a good suggestion to not roll your personal. Nevertheless, for steady integration (CI) jobs generally industrial off-the shelf (COTS) options are extreme and convey 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 reveals how the method could be minimally tied to a particular construct or CI system and never depending on a particular language or runtime (even when Python was used out of comfort).
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