Tag: Version Control

The Version Control Strategies series

1. Organisation antipattern – Release Feature Branching
2. Organisation pattern – Trunk Based Development
3. Organisation antipattern – Integration Feature Branching
4. Organisation antipattern – Build Feature Branching

Build Feature Branching is oft-incompatible with Continuous Integration

Build Feature Branching is a version control strategy where developers commit their changes to individual remote branches of a source code repository prior to the shared trunk. Build Feature Branching is possible with centralised Version Control Systems (VCSs) such as Subversion and TFS, but it is normally associated with Distributed Version Control Systems (DVCSs) such as Git and Mercurial – particularly GitHub and GitHub Flow.

In Build Feature Branching Trunk is considered a flawless representation of all previously released work, and new features are developed on short-lived feature branches cut from Trunk. A developer will commit changes to their feature branch, and upon completion those changes are either directly merged into Trunk or reviewed and merged by another developer using a process such as a GitHub Pull Request. Automated tests are then executed on Trunk, testers manually verify the changes, and the new feature is released into production. When a production defect occurs it is fixed on a release branch cut from Trunk and merged back upon production release.

Consider an organisation that provides an online Company Accounts Service, with its codebase maintained by a team practising Build Feature Branching. Initially two features are requested – F1 Computations and F2 Write Offs – so F1 and F2 feature branches are cut from Trunk and developers commit their changes to F1 and F2.

Two more features – F3 Bank Details and F4 Accounting Periods – then begin development, with F3 and F4 feature branches cut from Trunk and developers committing to F3 and F4. F2 is completed and merged into Trunk by a non-F2 developer following a code review, and once testing is signed off on Trunk + F2 it is released into production. The F1 branch grows to encompass a Computations refactoring, which briefly breaks the F1 branch.

A production defect is found in F2, so a F2.1 fix for Write Offs is made on a release branch cut from Trunk + F2 and merged back when the fix is in production. F3 is deemed complete and merged into Trunk + F2 + F2.1 by a non-F3 developer, and after testing it is released into production. The F1 branch grows further as the Computations refactoring increases in scope, and the F4 branch is temporarily broken by an architectural change to the submissions system for Accounting Periods.

When F1 is completed the amount of modified code means a lengthy code review by a non-F1 developer and some rework are required before F1 can be merged into Trunk + F2 + F2.1 + F3, after which it is successfully tested and released into production. The architectural changes made in F4 also mean a time-consuming code review and merge into Trunk + F2 + F2.1 + F3 + F1 by a non-F4 developer, and after testing F4 goes into production. However, a production defect is then found in F4, and a F4.1 fix for Accounting Periods is made on a release branch and merged into Trunk + F2 + F2.1 + F3 + F1 + F4 once the defect is resolved.

In this example F1, F2, F3, and F4 all enjoy uninterrupted development on their own feature branches. The emphasis upon short-lived feature branches reduces merge complexity into Trunk, and the use of code reviews lowers the probability of Trunk build failures. However, the F1 and F4 feature branches grow unchecked until they both require a complex, risky merge into Trunk.

The Company Accounts Service team might have used Promiscuous Integration to reduce the complexity of merging each feature branch into Trunk, but that does not prevent the same code deviating on different branches. For example, integrating F2 and F3 into F1 and F4 would simplify merging F1 and F4 into Trunk later on, but it would not restrain F1 and F4 from generating Semantic Conflicts if they both modified the same code.

This example shows how Build Feature Branching typically inserts a costly integration phase into software delivery. Short-lived feature branches with Promiscuous Integration should ensure minimal integration costs, but the reality is feature branch duration is limited only by developer discipline – and even with the best of intentions that discipline is all too easily lost. A feature branch might be intended to last only for a day, but all too often it will grow to include bug fixes, usability tweaks, and/or refactorings until it has lasted longer than expected and requires a complex merge into Trunk. This is why Build Feature Branching is normally incompatible with Continuous Integration, which requires every team member to integrate and test their changes on Trunk on at least a daily basis. It is highly unlikely every member of a Build Feature Branching team will merge to Trunk daily as it is too easy to go astray, and while using a build server to continuously verify branch integrity is a good step it does not equate to shared feedback on the whole system.

Build Feature Branching advocates that the developer of a feature branch should have their changes reviewed and merged into Trunk by another developer, and this process is well-managed by tools such as GitHub Pull Requests. However, each code review represents a handover period full of opportunities for delay – the developer might wait for reviewer availability, the reviewer might wait for developer context, the developer might wait for reviewer feedback, and/or the reviewer might wait for developer rework. As Allan Kelly has remarked “code reviews lose their efficacy when they are not conducted promptly“, and when a code review is slow the feature branch grows stale and Trunk merge complexity increases. A better technique to adopt would be Pair Programming, which is a form of continuous code review with minimal rework.

Asking developers working on orthogonal tasks to share responsibility for integrating a feature into Trunk dilutes responsibility. When one developer has authority for a feature branch and another is responsible for its Trunk merge both individuals will naturally feel less responsible for the overall outcome, and less motivated to obtain rapid feedback on the feature. It is for this reason Build Feature Branching often leads to what Jim Shore refers to as Asynchronous Integration, where the developer of a feature branch starts work on the next feature immediately after asking for a review, as opposed to waiting for a successful review and Trunk build. In the short-term Asynchronous Integration leads to more costly build failures, as the original developer must interrupt their new feature and context switch back to the old feature to resolve a Trunk build failure. In the long-term it results in a slower Trunk build, as a slow build is more tolerable when it is monitored asynchronously. Developers will resist running a full build locally, developers will then checkin less often, and builds will gradually slowdown until the entire team grinds to a halt. A better solution is for developers to adopt Synchronous Integration in spite of Build Feature Branching, and by waiting on Trunk builds they will be compelled to optimise it using techniques such as acceptance test parallelisation.

Build Feature Branching works well for open-source projects where a small team of experienced developers must integrate changes from a disparate group of contributors, and the need to mitigate different timezones and different levels of expertise outweighs the need for Continuous Integration. However, for commercial software development Build Feature Branching fits the Wikipedia definition of an antipattern – “a common response to a recurring problem that is usually ineffective and risks being highly counterproductive“. A small, experienced team practising Build Feature Branching could theoretically accomplish Continuous Integration given a well-structured architecture and a predictable flow of features, but it would be unusual. For the vast majority of co-located teams working on commercial software Build Feature Branching is a costly practice that discourages collaboration, inhibits refactoring, and by implicitly sacrificing Continuous Integration acts as a significant impediment to Continuous Delivery. As Paul Hammant has said, “you should not make branches for features regardless of how long they are going to take“.

The Version Control Strategies series

1. Organisation antipattern – Release Feature Branching
2. Organisation pattern – Trunk Based Development
3. Organisation antipattern – Integration Feature Branching
4. Organisation antipattern – Build Feature Branching

Integration Feature Branching is overly-costly and unpredictable

Integration Feature Branching is a version control strategy where developers commit their changes to a shared remote branch of a source code repository prior to the shared trunk. Integration Feature Branching is applicable to both centralised Version Control Systems (VCS) and Distributed Version Control Systems (DVCS), with multiple variants of increasing complexity:

• Type 1 – Integration branch and Trunk. This was originally used with VCSs such as Subversion and TFS
• Type 2 – Feature branches, an Integration branch, and Trunk. This is used today with DVCSs such as Git and Mercurial
• Type 3 – Feature release branches, feature branches, an Integration branch, and Trunk. This is advocated by Git Flow

In all Integration Feature Branching variants Trunk represents the latest production-ready state and Integration represents the latest completed changes ready for release. New features are developed on Integration (Type 1), or short-lived feature branches cut from Integration and merged back into Integration on completion (Types 2 and 3). When Integration contains a new feature it is merged into Trunk for release (Types 1 and 2), or a short-lived feature release branch cut from Integration and merged into Trunk and Integration on release (Type 3). When a production defect occurs it is fixed on a release branch cut from Trunk, then merged back to Integration (Types 1 and 2) or a feature release branch if one exists (Type 3).

Consider an organisation that provides an online Company Accounts Service, with its codebase maintained by a team practising Type 2 Integration Feature Branching. Initially two features are requested – F1 Computations and F2 Write Offs – so F1 and F2 feature branches are cut from Integration and developers commit their changes to F1 and F2.

Two more features – F3 Bank Details and F4 Accounting Periods – then begin development, with F3 and F4 feature branches cut from Integration and developers committing to F3 and F4. F2 is completed and merged into Integration, and after testing it is merged into Trunk and regression tested before its production release. The F1 branch is briefly broken by a computations refactoring, with no impact on Integration.

When F3 is completed it is merged into Integration + F2 and tested, but in the meantime a production defect is found in F2. A F2.1 fix is made on a F2.1 release branch cut from Trunk + F2, and after its release F2.1 is merged into and regression tested on both Integration + F2 + F3 and Trunk + F2. F3 is then merged into Trunk and regression tested, after which it is released into production. F1 continues development, and the F4 branch is temporarily broken by changes to the submissions system.

When F1 is completed and merged into Integration + F2 + F3 + F2.1 it is ready for production release, but a business decision is made to release F4 first. F4 is completed and after being merged into and tested on both Integration + F2 + F3 + F2.1 + F1 and Trunk + F2 + F3 + F2.1 it is released into production. Soon afterwards F1 is merged into and regression tested on Trunk + F2 + F2.1 + F3, then released into production. A production defect is found in F4, and a F4.1 fix is made on a release branch cut from Trunk + F2 + F2.1 + F3 + F4 + F1. Once F4.1 is released it is merged into and regression tested on both Integration + F2 + F3 + F2.1 + F1 + F4 and Trunk + F2 + F2.1 + F3 + F4 + F1.

In this example F1, F2, F3, and F4 all enjoy uninterrupted development on their own feature branches. The use of an Integration branch reduces the complexity of each merge into Trunk, and allows the business stakeholders to re-schedule the F1 and F4 releases when circumstances change. However, the isolated development of F1, F2, F3, and F4 causes complex, time-consuming merges into Integration, and Trunk requires regression testing as it can differ from Integration – such as F4 being merged into Integration + F2 + F3 + F2.1 + F1 and Trunk + F2 + F2.1 + F3. The Company Accounts Service team might have used Promiscuous Integration on feature release to reduce the complexity of merging into Integration, but there would still be a need for regression testing on Trunk.

If the Company Accounts Service team used Type 3 Integration Feature Branching the use of feature release branches between Integration and Trunk could reduce the complexity of merging into Trunk, but regression testing would still be required on Trunk to garner confidence in a production release. Type 3 Integration Feature Branching also makes the version control strategy more convoluted for developers, as highlighted by Adam Ruka criticising Git Flow’s ability to “create more useless merge commits that make your history even less readable, and add significant complexity to the workflow“.

The above example shows how Integration Feature Branching adds a costly, unpredictable phase into software development for little gain. The use of an Integration branch in Type 1 creates wasteful activities such as Integration merges and Trunk regression testing, which insert per-feature variability into delivery schedules. The use of feature branches in Type 2 discourages collaborative design and refactoring, leading to a gradual deterioration in codebase quality. The use of feature release branches in Type 3 lengthens feedback loops, increasing rework and lead times when defects occur.

Integration Feature Branching is entirely incompatible with Continuous Integration. Continuous Integration requires every team member to integrate and test their code on Trunk at least once a day in order to minimise feedback loops, and Integration Feature Branching is the polar opposite of this. While Integration Feature Branching can involve commits to Integration on a daily basis and a build server constantly verifying both Integration and Trunk integrity, it is vastly inferior to continuously integrating changes into Trunk. As observed by Dave Farley, “you must have a single shared picture of the state of the system… there is no point having a separate integration branch“.

The Version Control Strategies series

1. Organisation Antipattern – Release Feature Branching
2. Organisation Pattern – Trunk Based Development
3. Organisation Antipattern – Integration Feature Branching
4. Organisation Antipattern – Build Feature Branching

Release Feature Branching dramatically increases development costs and risk

Feature Branching is a version control practice in which developers commit their changes to a branch of a source code repository before merging to trunk at a later date. Popularised in the 1990s and 2000s by centralised Version Control Systems (VCS) such as ClearCase, Feature Branching has evolved over the years and is currently enjoying a resurgence in popularity thanks to Distributed Version Control Systems (DVCS) such as Git.

The traditional form of Feature Branching originally promoted by ClearCase et al might be called Release Feature Branching. The central branch known as trunk is considered a flawless representation of all previously released work, and new features for a particular release are developed on a long-lived branch. Developers commit changes to their branch, automated tests are executed, and testers manually verify the new features. Those features are then released into production from the branch, merged into trunk by the developers, and regression tested on trunk by the testers. The branch can then be earmarked for deletion and should only be used for production defect fixes.

Consider an organisation that provides an online company accounts service, with its codebase maintained by a team practicing Release Feature Branching. Two epics – E1 Corporation Tax and E2 Trading Losses – begin development on concurrent feature branches. The E1 branch is broken early on, but E2 is unaffected and carries on regardless.

In month 2, two more epics – E3 Statutory Accounts and E4 Participator Loans – begin. E3 is estimated to have a low impact but its branch is broken by a refactoring and work is rushed to meet the E3 deadline. Meanwhile the E4 branch is broken by a required architecture change and gradually stabilised.

In month 3, E3 is tested and released into production before being merged into trunk and regression tested. The E2 branch becomes broken so progress halts until it is fixed. The E1 branch is tested and released into production before the merge and regression testing of trunk + E3 + E1.

In month 4, E2 is tested and released into production but the subsequent merge and regression testing of trunk + E3 + E1 + E2 unexpectedly fails. While the E2 developers fix trunk E4 is tested and released, and once trunk is fixed the merge and regression testing of trunk + E3 + E1 + E2 + E4 is performed. Soon afterwards a critical defect is found in E4, so a E4.1 fix is also released.

At this point all 4 feature branches could theoretically be deleted, but Corporation Tax changes are requested for E1 on short notice and a trunk release is refused by management due to the perceived risk. The dormant E1 branch is resurrected so E1.1 can be released into production and merged into trunk. While the E1 merge was trunk + E3 the E1.1 merge is trunk + E3 + E2 + E4.1, resulting in a more complex merge and extensive regression testing.

In this example E1, E2, E3, and E4 enjoyed between 1 and 3 months of uninterrupted development, and E4 was even released into production while trunk was broken. However, each period of isolated development created a feedback delay on trunk integration, and this was worsened by the localisation of design activities such as the E3 refactoring and E4 architectural change. This ensured merging and regression testing each branch would be a painful, time-consuming process that prevented new features from being worked on – except E1.1, which created an even more costly and risky integration into trunk.

This situation could have been alleviated by the E1, E2, E3, and/or E4 developers directly merging the changes on other branches into their own branch prior to their production release and merge into trunk. For instance, in month 4 the E4 developers might have merged the latest E1 changes, the latest E2 changes, and the final E3 changes into the E4 branch prior to release.

Martin Fowler refers to this process of directly merging between branches as Promiscuous Integration, and promiscuously integrating E1, E2, and E3 into E4 would certainly have reduced the complexity of the eventual trunk + E3 + E1 + E2 + E4 merge. However, newer E1 and E2 changes could still introduce complexity into that merge, and regression testing E4 on trunk would still be necessary.

The above example shows how Release Feature Branching inserts an enormously costly and risky integration phase into software delivery. Developer time must be spent managing and merging feature branches into trunk, and with each branch delaying feedback for prolonged periods a complex merge process per branch is inevitable. Tester time must be spent regression testing trunk, and although some merge tools can automatically handle syntactic merge conflicts there remains potential for Semantic Conflicts and subtle errors between features originating from different branches. Promiscuous Integration between branches can reduce merge complexity, but it requires even more developer time devoted to branch management and the need for regression testing on trunk is unchanged.

Since the mid 2000s Release Feature Branching has become increasingly rare due to a greater awareness of its costs. Branching, merging, and regression testing are all non-value adding activities that reduce available time for feature development, and as branches diverge over time there will be a gradual decline in collaboration and codebase quality. This is why it is important to heed the advice of Dave Farley and Jez Humble that “you should never use long-lived, infrequently merged branches as the preferred means of managing the complexity of a large project“.