When performing software program improvement, a fundamental observe is the versioning and model management of the software program. In lots of fashions of improvement, resembling DevSecOps, model management consists of far more than the supply code but additionally the infrastructure configuration, check suites, documentation and plenty of extra artifacts. A number of DevSecOps maturity fashions contemplate 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 recordsdata is git. That is the software that backs common supply code administration platforms, resembling 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 drawback isn’t distinctive to git. Many instruments used for supply management remedy the issue of the best way to uniquely establish a set of modifications from every other in the same means. In mercurial, one other supply code administration software a changeset is recognized by a 160-bit identifier.
This implies to seek advice from a model in git, one might must specify an ID resembling 521747298a3790fde1710f3aa2d03b55020575aa (or the shorter however no much less descriptive 52174729). This isn’t a great way for builders or customers to seek advice from 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 might be missed. This results in the central query: How can we automate the task of variations (via tags) routinely? This weblog submit explores my work on extending the traditional commit paradigm to allow computerized 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 guide 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 tasks pointed to. Nevertheless, with supporting so many customers of the templates there was inevitably one repository that might break or use the script in a non-conventional means. In a couple of circumstances, we would have liked to revert modifications on the department to allow all repositories to move 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 move 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 similar updates as the principle line of improvement. These branches are created in order that customers didn’t get all of the modifications rolled out on the default department immediately. Lengthy lived branches can develop into stale after they don’t obtain updates which were made to the principle line of improvement. These long-lived, stale branches made it troublesome to scrub 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, resembling HEAD~3, or the hash of the earlier commit earlier than the breaking change was built-in into the default department. This difficulty 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 (sometimes called “reside at head,”) this technique of working was not working for this challenge and person base to take action. We wanted higher model management within the repository with a approach 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 modifications to the repository, the builders selected adopting and implementing typical commits. The standard 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 affect 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 implementing size necessities, the group hoped to keep away from commit messages resembling, 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 among 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 submit 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 submit about git commits
The submit explains the best way to use typical commits to routinely model a repository
The primary motivation behind implementing typical commits was to scrub up the challenge’s git historical past. Having the ability to perceive the modifications {that a} new model brings in via commits alone can pace 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 infrequently, although the steadiness 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 submit makes no suggestions on how usually to commit, it does implement at the least 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 completed within the challenge and described on this weblog submit. With out the flexibility to find out what modified and the affect of the change straight within the git historical past, it might have difficult the hassle and probably pushed in direction of a much less moveable answer. Implementing a 20-character minimal could seem arbitrary and a burden for some smaller modifications nonetheless implementing 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, 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 means through git tags permits customers to see the affect of the change and replace to a brand new model when prepared. Conversely a group might proceed to reside at head till they bumped into a problem after which extra simply see what variations have been accessible to roll again to.
COTS Options
This difficulty of routinely 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 particular CI system, resembling GitHub Actions, or a particular language, resembling 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 challenge isn’t extensively used within the python neighborhood. Equally, there’s a semantic-release software, however this requires Node.js within the construct atmosphere, which is much less frequent in some tasks and industries. There are additionally open-source GitHub actions that allow computerized 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. As a consequence 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 asubmit by Three Dots Labs helped me establish an answer that was oriented towards GitLab, much like my challenge. That submit, nonetheless, left it as much as the reader the best way to decide the following 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 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 may not work if a hotfix was dedicated on to the default department. Whereas this final level must 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 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 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 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 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 (although who would do this?). In every case, the belief 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, function, and so forth.) 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 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 permits reverting to anyone commit and having a model of the software program that handed via no matter assessment course of is in place for modifications going into the trunk or fundamental department of the repository. The right technique must be decided for every challenge. Many instruments that wrap round git, resembling 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 fundamental 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 appears to be like 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 completed 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 at all times up to date by at the least 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 atmosphere, and it was clear sufficient what the script was doing. The identical might 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 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 next_version.py script to generate the following 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 "${CI_API_V4_URL}/tasks/${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.cut 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 challenge was hosted in Gitlab, which supplies 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 might be adjusted. The one gotcha right here is, as said 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 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 affordable). 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 standard commits, is the flexibility to condense these modifications right into a changelog giving builders and customers, “a curated, chronologically ordered record of notable modifications for every model of a challenge”. This helps builders and customers know what has modified, along with the affect.
Having a approach to sign to customers when a library or different piece of software program has modified is essential. 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 difficulty, however they could not at all times be a superb match for any explicit improvement atmosphere. Relating to safety important software program, resembling encryption or authentication, it’s a good suggestion to not roll your individual. Nevertheless, 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 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).
