Expressions for Source Control Management Systems

Abstract: In the last decades, many standards were established to increase productivity during Software Lifecycle Management. All these techniques and methodologies promise a higher success rate in software projects which could affirm themselves in the case the involved protagonists are willing to follow the instances recommended. Semantic Versioning, for example, addresses the information leak between functional changes, BugFixes and compatibility of existing and future releases of artifacts. Diving deeper into the daily craftsmanship of software projects enables us to identify the Source Control Management Systems (SCM) as a big treasure box. Much information can be extracted from these repositories, which are currently ignored for project analyzing. Expressions on SCM Commit Messages represent a new formalism that is both human-readable and machine-processable. Such a standard also forms a bridge between the code base and the requirements management and release management, since these activities are identified by a freely expandable vocabulary in the SCM. Another advantage of this strategy is the clear and compact expressiveness for development teams. A very practical aspect of my proposal is the easy applicability of the presented solution in real software development projects. As with the Semantic Versioning methodology already mentioned, there are no additional technical requirements to be met, since commit messages are a fundamental function of SCM systems. This paper discuss the option to improve data collection for controlling software projects and knowledge sharing in collaborative teams.

To cite this article: Marco Schulz. Expressions for Source Control Management Systems. American Journal of Software Engineering and Applications. Vol. 11, No. 2, 2022, pp. 22-30. doi: 10.11648/j.ajsea.20221102.11

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1. Introduction

Thinking about SCM systems we have to keep in mind, that since the first roll out of CVS in the early 1990‘s and today, many things have changed. Searching the free online encyclopedia Wikipedia, presents a page ”Comparison of Version Control Software” which contains an overview of version control software of more than 30 SCM tools. This gives an idea why software companies usually have around three or more different SCM systems in work – of course the real amount depends on how many years they are in business.

The possibility to attach every revision in SCM Systems with a commit message allows the developer to inform other users with a short explanation of his work. This feature is extremely helpful by browsing the history manually in search of special code changes. If these commit messages well structured there exist a possibility to grab automated information of project growth. In this paper on expressions is introduced as solution for structured commit messages which could processed by software and also helps developers to resume their work more efficient.

The list of research on SCM is quite overwhelming and covers multiple aspects. The work of Walter F. Tichy on RCS [2] presents a deep fundamental insight into technical aspects of SCM systems. Abdullah Uz Tansel et al. gives in his research a brief history and builds a bridge to nowadays SCM systems [11]. The paper of Christian Bird et al. describes the ideas why companies deal with various SCM solutions [12]. Many existing papers like the one from Filip Van Rysselberghe and Serge Demeyer already identified SCM repositories as a significant information storage [5], which contains more than a simple history of source code. The approach from Louis Glassy to observe the growth of students in the software development process by using SCM techniques [6] demonstrates another method to grab implicit information from SCM. Alongside the fundamental research in software engineering, there exists a great resource of Blogs, articles and books from people who are directly involved in the topic. They describe experiences and best practice to make the next release come true, as referred towards the web resources in the footnotes. A small selection of related practitioners books is also included in the reference list.

Let us take a closer look at how processes for SCM could be improved. For this reason, section II defines the terminology of this paper and talks in detail about merging and branching strategies. Section III remind some basic knowledge on SCM and gives a simple idea about how complex build and deploy pipelines interact. Following this quick journey, section IV draws a picture about real problems that occur in software development projects and explains possible Points of Interest (POI) inside an SCM repository. These fundamentals allow a definition of the vocabulary we introduce in section V. A real world example will demonstrate in VI the cardinality of the expression and gives ideas about its usage. After all, section VII will reflect and summarize these thoughts. The last section talks about ideas how future work could be continued.

Figure 1. Branch and Merge.

Figure 1: Branch and Merge.

2. Definitions

The definitions in this section are based on the English dictionary Merriam Webster with a contextual relation to SCM systems. The term Source Control Management System (SCM) is applied in this paper to describe tools like CVS, Subversion (SVN) or Git. Many other names have appeared over the years in literature for this type of tools. All these terms like Version Control System (VCS) or Revision Control System (RCS) are considered as equal to each other.

Artifact “A USUALLY SIMPLE OBJECT (SUCH AS A TOOL OR ORNAMENT) SHOWING HUMAN WORKMANSHIP OR MODIFICATION AS DISTINGUISHED FROM A NATURAL. OBJECT; “ESPECIALLY: AN OBJECT REMAINING FROM A PARTICULAR PERIOD”. In the context of SCM, an artifact is a binary result of the build process. Artifacts can be libraries, applications and so on.

Repository “A PLACE, ROOM, OR CONTAINER WHERE IS DEPOSITED OR STORED”. In software engineering a repository denotes a managed storage. We can distinguish repositories for source code and for binary artifacts.

Revision “A CHANGE OR A SET OF CHANGES THAT CORRECTS OR IMPROVES SOMETHING”. Each successful commit from a user to the SCM represents a change of the internal state in the SCM. These different states are revisions. Subversion for example increments an internal number after each commit [18]. This unique identifier is called revision number. Git on the other hand manages the revision number smarter and creates SHA-1 Hashes from each commit as an identifier [15]. This brings more flexibility for dealing with branches.

Release “TO GIVE PERMISSION FOR PUBLICATION, PERFORMANCE, EXHIBITION, OR SALE OF; ALSO: TO MAKE AVAILABLE TO THE PUBLIC”. A release defines a set of functional assertions for an artifact. When all functions are implemented, a test procedure is started to exclude as many failures as possible. After the termination of testing and corrections, the artifact gets packed for delivery. To distinguish the different versions of an artifact, it gets labeled by a unique version number. By convention, it is not allowed to have more than one artifact with the same version number.

Tag “A DESCRIPTIVE OR IDENTIFYING EPITHET”. -A Tag is a label to a special revision, like a release, and is used as bookmark.

Trunk “THE CENTRAL PART OF ANYTHING”. A trunk is a common convention and means the main branch, where the current development happens [17]. In Git this branch is called master for the local repository and orgin in the remote repository. Branching and Merging is one of the major feature in SCM systems and also a high sophisticated operation. It is not so unusual that developers and also Configuration Managers struggle with this. The paper of Shaun Phillips et al. contains a developer comment about the dealing with SCM and the pain of merging [10].

“We are a team of four senior developers (by which I mean we’re all over 40 with 20+ years each of development experience) and not one of us has had a positive experience in the past with branching the mainline… The branch is easy – it’s the merge at the end that’s painful.”

This shows that even persons with many years of experience need a detailed explanation of a seemingly trivial procedure. A simple understanding how branches typically have to be used and how they represent the evolution of a real software project is of high relevance for this paper. Figure 1 explains the optimal interaction between branches and the trunk which is described by Chuck Walrad and Darrel Strom as Branch by Release Model [3]. In addition to the context of branching and merging there is a version tree sample graph explained by Yongchang Ren et al. in their paper [8].

In order to give a comprehensive explanation of the process we assume a simple Java library project. As build tool Apache Maven is chosen which is successfully used for years by many different commercial and Open Source projects. Maven defines many standards for the software development process and implements them. Its success feature is a highly efficient dependency management.

The information about the artifact version number is managed in the pom.xml, the Maven build file. For this reason the POM has our special attention. In the context of Maven a versions number is labeled SNAPSHOT while it is still under development. This convention allows in collaborative teams the sharing of non official published artifacts. After removing the label SNAPSHOT the artifact is released. By convention it is not possible to have more than one artifact with the same version number. In section III this topic is discussed in more detail. For the moment it is necessary to know that this convention takes effect in collaborative processes. The correct way to share artifacts is the usage of a Repository Manager. The most common Repository Manager is Sonatype Nexus OSS which is used for Maven Central [19] to deliver dependencies. Nexus will refuse the request if a developer tries to publish an already existing release of an artifact. With this infrastructure it is not necessary to transfer binary artifacts to the SCM. This tool chain is a simple example for a highly complex infrastructure to build and deliver software in large companies.

In figure 1 the development starts with version 1.0-SNAPSHOT. After the release of this version, the development of the next version 1.1-SNAPSHOT continues in trunk. The revision of the released version 1.0 gets branched to fix some bugs. The branch will not be created automatically during the release, rather it gets created when there is a need, for example BugFixes. The branch will be named by its minor version 1.0 to stay flexible for further corrections. After a correct BugFix the changes get merged back to trunk and so on. It is very important to keep in mind, that after a release, no new functionality can be added to the versions 1.0.X, only corrections are allowed.

The merging of failure corrections can lead to complications if there already exist deployed versions. When a bug is detected down to an existing version it will be necessary to fix all following versions and increment their version number as part of the correction. For example if there exist released versions 1.0.2, 1.1.1, 1.2.3 & 2.0.1. and the fix has been done in version 1.0.2 it will have to be renamed 1.0.3 for release. The merge direction is always from the lower to the higher version which means that the version numbers of all following involved artifacts have to be increased. By this it can be assured that only fixes will be exchanged and no functionality is moving form an higher to a lower version within the merging process.

In this model the case of parallel feature development is missing. This happens when a very complex functionality is planned and the implementation cannot be finished in one release cycle. This especially often occurs in agile projects with a short time line between releases. Feature Branches address this requirement as well. The process is a simple extension of the Branch by Release Model. The Feature Branch will be created from the trunk and will be named like the feature. To test compatibility this branch at least needs to be merged from the trunk after each release. A merge can also be performed if the trunk provides important new features – whenever necessary.

A very useful advanced usage of branches is the stash command, that comes as build-in with Git. Indeed this feature is not so common but simple and powerful. Imagine a developer is working on some implementation with the urgency of having to deliver a BugFix for another release. He needs to switch his workspace to this branch but the current work needs to be saved without a direct commit to the trunk. The solution is create a branch and check in the current work and hence switch the branch for the fix. After all is done he will have to switch to the stashed branch, finish the work and merge the result to the trunk. An often observed procedure for developers are simultaneous checkouts of different branches and just switching the IDE workspace. By experience in large companies, this is very time consuming and error prone. By the law of Murphy, the only needed branch is the one not present in a local checkout collection.

To get in touch with branch models more profoundly, the website of the Git SCM [20] presents different branching workflows. Also at [21] exists a very detailed explanation for Git branch and merge best practices.

3. Quick Survey on SCM Basics

As described, there exists a huge amount of Source Control Management solutions. Even just picking out the most popular systems, we are able to identify many differences in detail. These may be the reasons why some tools have become more popular than others. Naturally, all of these systems do the job and are based on common ideas. A very early and fundamental work on SCM systems done by Tichy gives a deep insight about the Theory on how an SCM should be constructed [2]. Today, based on the approach of how things are done, we can classify them. Directory and file based systems, like Microsoft Visual Source Safe, are part of the less effective group of SCM. In commercial environments this group has low relevance because quite often it causes inconsistencies of the repository. This leads us to the category of Client-Server solutions. Client-Server SCM systems have two manifestations: centralizedand distributed. SVN is the most famous representative for centralized solutions. In new projects the choice of the day will very often be Git, a very popular distributed SCM tool. In “Transition from Centralized to Decentralized Version Control Systems” the authors describes why decentralized SCM systems are favored by developers [12]. Interviews of developers have shown the benefits and risks of applicated SCM systems. They deliver a well elaborated explanation why distributed SCM has a higher learning curve. This finding is a important principle for dealing with SCM.

SCM systems are designed to handle plain text files, like those used for source code. After a file has undergone configuration management and had an initial transfer into the repository, the system stores only a delta of the changes for every new transaction. With this requirement the repository is more efficient and needs less disk storage. This implies binary files like office documents should not be stored in SCM repositories because the system cannot calculate a delta and will always store a complete new copy of the file, if it has been changed. A solution for dealing with binaries, like dependencies or third party libraries, are Repository Managers which were introduced in section II.

Figure 2. Changes in the POM, based on Semantic Versioning.

Figure 2: Changes in the POM, based on Semantic Versioning.

At this point some performance issues for SCM have to be taken in consideration. This is of outstanding importance, because it defines how a repository should be organized. Large projects with a code repository up to 1 GB take a long time for a checkout, even though there is only a small subset of files that are chosen. 20 minutes and more are very common. The reason for this effect is the size of the repository itself. When it contains a lot of files it takes more time to calculate the internal tree. The best solution for a high performance repository is: Only text files and just one independent project or module per repository.

In continuation surges question how files are represented in a SCM. As an example we remember the small Java library project with the Maven build logic. The build logic is represented as an XML file and contains the entry <version>. This entry defines the version number of the artifact and starts with an initialization of 1.0.0-SNAPSHOT. The procedure to increase the version number strictly follows the Semantic Versioning. Figure 2 visualizes several steps between two releases. For each revision a label describes the process and the version number show the value in the POM file. This graphic is an extension with a detailed view of figure 1.

In reality things are never like explained in theory. Initial assumption often create a big dilemma in automation processes when it comes to execution. It is very easy to claim, that in a repository, the entry for version in the POM for releases is unique. For example, it means that there should not exist two revisions with a released version 1.0. But where humans work, mistakes will happen. For this reason we have the option to create tags into the SCM. Every revision in the SCM which represents a deployed release, will be tagged with the correct version number. Deployed releases are defined by a successful transfer of the binary artifact into the Repository Manager for collaborative usage.

4. Scenarios on Real Problems

We should focus our activities on special points in respect to the evolution of software projects. It is not useful to pay attention on each single revision. Let us highlight the Points of Interest (POI) and why they are special. In real projects with collaborative teams, it is quite common that a developer breaks the current build. The good news are: when Continuous Integration (CI) is applied in the process, these kind of problems will be detected very quickly and can be solved at the instance of them appearing [16]. But how a developer is able to break a build? This occurs when the changes get committed into the repository and some files are not included in the commit. A repair can easily and fast be done by adding a new commit with the missing files needed. In this case it is very important to realize that only the one who delivered an incomplete package is able to add the missing parts. Problems arise when this happens on a Friday evening and the person responsible is leaving the office for vacations the next two or tree weeks without checking that everything is in order, causing unnecessary pain in the continuation of the project. These things happen much more often than anyone would expect.

Another effect is called fast shots. These small and often repeated commits typically change only a few lines in just one or two files. This happens when a user for some reason is not able to test his code or settings locally on his own machine. A simple scenario could be the manipulation of the CI Server build output without direct access.

A work flow for developers is the usage of particular commits in order to preserve intermediate steps of the work and allow an easy rollback. This procedure is only applicable in distributed systems or in environments without collaboration. The effect is quit similar. It will produce many revisions inside the SCM, which could get summarized to a single revision.

The Continuous Delivery approach for modern Web Applications is a quite different method compared to the classical release process [14]. This technique requires special strategies like the Feature Toggle Pattern [22] and a highly automated deploy pipeline. Also the usage of the SCM system is very advanced. Each feature is developed in its own branch and the Configuration- or Build Manager creates for each deployment a proper Integration Branch. The biggest challenge in this methodology are fast responses towards urgent problems arising. In the worst case it could be necessary to push out very quickly a new deployment with a full or partial rollback. During deployments database changes are very critical. This aspect could be discussed in a further paper. Databases are not implicitly part of the SCM, but there also exist techniques [23] to keep them under configuration management.

Figure 3. Structure of a commit naming.

Figure 3: Structure of a commit naming.

As mentioned before, a release R inside an SCM is defined by several commits to the SCM. These commits are identified by the revision r. The lowest amount of revisions between two release is one, but there is no limit concerning to the upper boundary. Special Points of Interests inside an SCM are released revisions which can formally defined by (2).

  • R := {r 1, r 2, r 3, r n+1,…, r x } (1)
  • POI:= ∆ Release (R; R + 1) (2)

By this interpretation we are able to develop metrics which show a real project growth and do not just produce an output [13]. The paper of P. Kaur and H. Singh contains a collection of metrics related to their VVCT SCM [9]. An adapted suggestion for possibilities to compare project evolution is:

  1. the amount of BugFix releases in a minor branch,
  2. an count of revisions between two release,
  3. the growth between minor and major release (e.g. Line of Codes),
  4. a direct comparison between the current trunk and a previous release,
  5. two selected releases,
  6. a comparison of an release R and its replacement.

For example the amount of BugFix releases for a minor release allows a conclusion about the quality situation of a project. It is very important to understand the reasons to improve program stability and reduce the number of BugFixes. A classification for changes is described by Swanson [1]. An overview of the project based on these classifications of BugFixes should detect the issues that have to be changed to accomplish high quality.

5. A Vocabulary for SCM Commit Messages

In the early times SCM systems were used for synchronizing source code between developers. Typically users were not paying too much attention to write well formulated explanations about their changes. In many instances they were not leaving any description about what they did. Another extreme was that comments like update build logic frequently appeared in the history. An explanation of everything and nothing without saying what was changed or why. It could either be a version update of an existing library or the addition of a new dependency leading to a heavy time-consuming work in order to identify the points of interest in the commit history. Manual checks between the version with a Diff Tool would be necessary to locate the Line of Code that may have to be changed again. Guidelines have been introduced on how to write a well formulated commit message to solve this problems. A short selection of these guides published on the internet: [24, 25, 26] It was discovered by companies that the approach to apply well formulated descriptions of SCM revisions can improve productivity in teams. By exploring new projects on Source Code Hosting Services like GitHub or Sourceforge the quality of commit messages was increasing in the last years.

Based on these recommendations and the experience gained as of today, a vocabulary should be introduced for writing easier and more efficient commit messages. This simple-to-use standardization could help to visualize the evolution of a project more clearly. By very precise and short explanation of every revision readers do not get flooded with information. This allows analysts to see patterns of process leaks more quickly and increases the team productivity. The usage of a defined structure also allows an automatism to parse the commit messages. The result can generate programmatic presentations of diagrams readable by humans. Naturally this approach is not only limited to SCM. Another usage could be for communication in meetings with strict time limitations, for example in the agile method Scrum.

The vocabulary for SCM Commit Messages follows a defined structure which is shown in figure 3. The composition contains a mandatory first line and includes a FunctionID, label and a short specification. The second and third line is optional and contains the TaskID from the Issue Management System and a description of the more detailed explanation. Our suggestion for the vocabulary covers most SCM work flows. It may will be that some companies need adoptions to implement this solution in their processes. For this reason the definition is flexible and allows extensions.

  • #INIT – the repository or a release.
    • repro:documentation / configuration…
    • archetype:jar / war / ear / pom / zip…
    • version:<version>
  • #IMPLEMENT – a functionality.
    • function:<clazz>
  • #CHANGE – a functionality.
    • function:<clazz>
  • #EXTEND – a functionality.
    • function:<clazz>
    • attach:<clazz>
  • #BUGFIX – a functionality.
    • priority:critical / medium / low / design
  • #REVIEW – an implementation.
    • refactor:<function>
    • analyze:<quality>migrate:<function>
    • format:<source>
  • #RELEASE – an artifact.
    • version:<version>
  • #REVERT – a commit.
    • commit:<id>
  • #BRANCH – create.
    • create:<name>
    • stash:<branch>
  • #MERGE – from another branch.
    • from:<branch>
    • to:<branch>
  • #CLOSE – a branch.
    • branch:<name>

As first entry a FunctionID is recommended and not the TaskID of the Issue Management. This decision is based on the experience that functionality could spread in different tasks. In longtime projects it could happen that for some reason the Issue Management System needs to be replaced by another one. Not all projects are connected to Issue Management, especially when they are small or just a prototype. These circumstances proved to be decisive to define the TaskId as optional and move it to the second line. With a FunctionID it is easier to identify parts that should be linked. Sometimes there exist transfers into the repository that cannot be assigned to a dedicated function. These commits are often related to activities of the Build- and Configuration Manager. As best practice an ID should be established which corresponds to these activities. Some examples related to the defined labels are:

  • [CM-00] INIT;
  • [CM-10] REVIEW;
  • [CM-20] BRANCH;
  • [CM-30] MERGE;
  • [CM-40] RELEASE;
  • [CM-50] build management.

The mightiness of this approach is its simplicity and how it can be included in existing projects. The rule set does not contain any additional complexity and the process is quite easy to understand. A short example will demonstrate the usage and a full example is provided in section VI. A change in the POM file to update the version of the test framework could be commented as follows:

[CM-50] #CHANGE ’function:pom’
{Change version number of the dependency JUnit from 4 to 5.0.2}

6. Release Process

The sample project in section II is not only fictive. The Together Platform (TP) available on GitHub [26] was initiated to study techniques on real conditions. Hence Git is the SCM tool of the choice. As client SmartGit is recommended because of platform independence and it offers plentiful advanced functionality.

For better comprehension of our approach of writing commit expressions we use the TP-CORE project, from initialization of the repository to its first release. No TaskIDs for the revisions exist due to the project not being connected to an Issue Management System. We use an excerpt of TP-CORE to demonstrate the approach because between the initial commit and the first published release 1.0.2 exist over 70 revisions in the repository. The project also contains a set of 12 functions which do not need to be included completely in our sample. Only three functions were selected for demonstration:

  • CORE-01 Logger;
  • CORE-02 genericDAO;
  • CORE-05 ApplicationConfiguration.

This cuts the revisions in half and shows enough complexity avoiding readers falling asleep.

The condition for a first release was the implementation of all 12 functionalities. The overall test coverage has reached more than 85%. Code smells detected with checks by Findbugs, Checkstyle, PMD et cetera have been removed. For an facilitate explanation, we add a revision number before the FunctionID. TP-CORE Commit Messages:

01[CM-00] #INIT ’archtype:jar’
{Initial the repository for Java JAR library.}
02[CORE-01] #IMPLEMENT ’function:Logger’
{Application wide standard logger.}
{Generic Data Access Object Pattern for centralized database access.}
04[CORE-05] #IMPLEMENT ’function:AppConfigDO’
{Domain Object for application configuration.}
05[CM-10] #REVIEW ’analyze:quality’
{Formatting, fix Checkstyle hints, JavaDoc & test coverage}
06[CORE-05] #IMPLEMENT ’function:ConfigurationDAO’
{Add the ConfigurationDAO implementation.}
07[CORE-05] #EXTEND ’attach:tests’
{Create test cases for Bean Validation.}
08[CORE-01] #EXTEND ’function:Logger’
{Add new Method to detect the configured LogLevel.}
09[CORE-05] #EXTEND ’function:AppConfigDO’
{Change Primary Key to UUID and extend tests.}
10[CORE-05] #CHANGE ’function:AppConfigDO’
{Rename to ConfigurationDO and define table indexes.}
11[CORE-02] #EXTEND ’function:GenericDAO’
{Add flushTable, countEnties and optimize.}
12[CORE-05] #EXTEND ’attach:tests’
{Update test cases for application configuration.}
13[CORE-05] #EXTEND ’function:ConfigurationDAO’
{Update the implementation for ConfigurationDAOImpl.}
14[CORE-01] #EXTEND ’function:Logger’
{Add method for exception handling.}
15[CORE-05] #EXTEND ’function:ConfigurationDO’
{Add field mandatory.}
16[CM-10] #REVIEW ’migrate:JUnit’
{Migrate Test cases from JUnit4 to JUnit5.}
17[CM-10] #REVIEW ’analyze:quality’
{Fix JavaDoc, Checkstyle & Findbugs.}
18[CM-50] #EXTEND ’function:POM’
{Update SCM connection to GitHub.}
19[CM-50] #EXTEND ’attach:APIguards’
{Attach annotation for API documentation.}
20[CORE-05] #REVIEW ’refactor:ConfigurationDO’
{FindBugs: optimize constructor parameters.}
21[CORE-02] #BUGFIX ’priority:design’
{Fix FindBugs hint: visible modifier.}
22[CM-50] #EXTEND ’attach:site’
{Extend MVN site configuration.}
23[CORE-02] #BUGFIX ’priority:high’
{Fix spring DAO configuration.}
24[CORE-05] #IMPLEMENT ’function:ConfigurationService’
{Implement basic functionality for
25[CM-10] #REVIEW ’analyze:quality’
{Remove all compiler warnings, FindBugs,
Checkstyle & PMD Hits.}
{Add JGiven test scenarios.}
27[CM-40] #RELEASE ’version:1.0’
{Release artifact to version 1.0}
28[CM-40] #RELEASE ’version:1.0.1’
{Change POM GroupId to Maven Central conventions.}
29[CM-00] #INIT ’version:1.1’
{Start implementation of version 1.1.0.}
30[CM-50] #MERGE ’from:1.0.1’
{Integrate GAV POM changes to trunk.}
31[CM-40] #RELEASE ’version:1.0.2’
{Include PGP signing.}
32[CM-20] #CHANGE ’function:Constraints’
{Add Constraints.VERSION to 1.1}
33[CORE-01] #EXTEND ’function:Logger’
{Default loader for logback.xml configuration files in the application DIR.}

Considering the previous example, we see that a limitation to around 80 – 100 characters for the first line is recommendable. Displaying the history with any client could get very messy if the first line has no size restrictions. The log output of the commit messages does not display the branch and tag operation, a behavior of Git. These revisions do not appear in any history list by browsing GitHub. Revision 28 is a branch based on revision 27. The branch is named as 1.0. Releases are published in consonance with the convention to be labeled, revision 31 tagged as Release 1.0.2. The revisions 28 and 31 are part of branch 1.0.

In this constellation we are able to see an important detail for dealing with branches. A branch will only be created when it is necessary. Usually BugFix branches do not have their own build plans on CI Servers and are managed manually. The primary arguments for this practice are to reduce the administrative overhead for the CI Servers. Companies that orchestrate their applications by web services or modules loose capacities by binding their recourses in this kind of activities.

7. Conclusion

“There is nothing permanent except change.” – Heraclitus

The whole infrastructure of commercial software projects contains a lot of independent fragments which share information over all development cycle. In projects we are overloaded by documentation production processes. The high amount of all this information inhibits profoundly comprehension and handling capabilities. Applications are getting more complex and bigger resulting in the necessity to establish more efficient ways to deal with information accumulation. There exists a giant overhead of managing documents like release notes, release plan, issue management, quality reports, statistics & metrics, documentation, architectural documents and BugFix lists. Typically each tool stores its data in its own structure. This makes changes to other tools, that might fit better, risky and expensive.

Companies know the effect that developers feel uncomfortable having to track their work in Issue Management tools like JIRA resulting in them trying to hide their part of the work flow as much as possible. Tasks will be opened up when they are almost done or already finished. The information on how many project days were spent for a function covers more the expectations and less the reality with the intent that developers can escape a bit from the daily pressure of productivity. Often developers are forced to spend their time with data acquisition for management controlling instead of programming resulting in low cost efficiency of a project and even additional and unplanned costs. Developers dislike this kind of activities because it keeps them away from their actual work: development. This is what makes the simple approach towards human readable and machine processable commit messages attractive and more convenient. The most important fact is that no extra costs are generated applying this method to existing processes.

We are enabled to generate several reports based on real data if SCM repositories can be populated with additional information. Impact assessments could be more efficient and accurate when they are created by facts and not emotionally blended.

Future Work

The idea to make information inside SCM systems more transparent is not just limited to commit messages. Another obvious point for future research is the history command. In the paper of Abram Hindle and Daniel M. German a query language for source control is introduced [7]. The idea of SCM Language could be picked up and transformed applying it to a specific solution. This work would use the Domain Driven Development paradigm to model an own SCM language based on Domain Specific Language (DSL) concepts – leading to the discovery of real world DSL solutions allowing for quick construction of a viable prototype or application based upon certain specifications.

Also a point which boldly comes to mind after reading the paper of Fischer et al., is the inclusion of released information into SCM [4]. This approach should not fully be automated due to its requirement of an advanced knowledge about branching and merging. A small self written extension could be a probable solution. A short tutorial 17 for Git suggests certain possibilities.


Special thanks to Joachim Reiter and Harald Kaufmann for spending their time to review this document. Their feedback was very productive.


[1] E. Burton Swanson, 1978, The Dimension of Maintenance.
[2] Walter F. Tichy, 1985, RCS – A System for Version Control.
[3] Chuck Walrad and Darrel Strom, 2002, The Importance of Branching Models in SCM.
[4] Michael Fischer, Martin Pinzger, Harald Gall, 2003, Populating a Release History Database from Version Control and Bug Tracking Systems.
[5] Filip Van Rysselberghe and Serge Demeyer, 2004, Mining Version Control Systems for FACs (Frequently Applied Changes).
[6] Louis Glassy, 2005, Using version control to observe student software development processes.
[7] Abram Hindle and Daniel M. German, 2005, SCQL: a formal model and a query language for source control.
[8] Yongchang Ren, Tao Xing, Qiang Quan, Ying Zhao, 2010, Software Configuration Management of Version Control Study Based on Baseline.
[9] Parminder Kaur and Hardeep Singh, 2011, A Model for Versioning Control Mechanism in Component- Based Systems
[10] Shaun Phillips, Jonathan Sillito, Rob Walker, 2011, Branching and merging: an investigation into current version control practices.
[11] Abdullah Uz Tansel and Ali Koc, 2011, A Survey of Version Control Systems.
[12] Christian Bird et al., 2014, Transition from Centralized to Decentralized Version Control Systems A Case Study on Reasons, Barriers, and Outcomes.
[13] Norman E. Fenton and Shari Lawrence Pfieeger, 1997, PWS Publishing Company, Software Metrics – A Rigorous and Practical Approach 2nd Edition, ISBN O·534·95425·1.
[14] Jez Humble and David Farley, 2010, Addison-Wesley, Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation, ISBN 0-321-60191-2.
[15] Scott Chacon and Ben Straub, 2014, Apress, Pro Git 2nd Edition, ISBN 978-1-4842-0077-3.
[16] Mike Clark, 2004, The Pragmatic Bookshelf, Pragmatic Project Automation, ISBN 0-9745140-3-9.
[17] Dave Thomas and Andy Hunt, 2003, The Pragmatic Bookshelf, Pragmatic Version Control with CVS, ISBN 0-9745140-0-4.
[18] Mike Mason, 2010, The Pragmatic Bookshelf, Pragmatic Guide to Subversion, ISBN 1-934356-61-1.


Marco Schulz, also kown by his online identity Elmar Dott is an independent consultant in the field of large Web Application, generally based on the JavaEE environment. His main working field is Build-, Configuration- & Release-Management as well as software architecture. In addition his interests cover the full software development process and the discovery of possibilities to automate them as much as possible. Over the time of the last ten years he has authored a variety of technical articles for different publishers and speaks on various software development conferences. He is also the author of the book “Continuous Integration with Jenkins” published 2021 by Rheinwerk.

Der grüne Punkt – Mythos Wiederverwendung

Als mir im Studium die Vorzüge der objektorientierten Programmierung mit Java schmackhaft gemacht wurden, war ein sehr beliebtes Argument die Wiederverwendung. Dass der Grundsatz „write once use everywhere“ in der Praxis dann doch nicht so leicht umzusetzen ist, wie es die Theorie suggeriert, haben die meisten Entwickler bereits am eigenen Leib erfahren. Woran liegt es also, dass die Idee der Wiederverwendung in realen Projekten so schwer umzusetzen ist? Machen wir also einen gemeinsamen Streifzug durch die Welt der Informatik und betrachten verschiedene Vorhaben aus sicherer Distanz.

(c) 2022 Elmar Dott, Java akuell Ausgabe 2, S.55 – 57

Wenn ich daran denke, wie viel Zeit ich während meines Studiums investiert habe, um eine Präsentationsvorlage für Referate zu erstellen. Voller Motivation habe ich alle erdenklichen Ansichten in weiser Voraussicht erstellt. Selbst rückblickend war das damalige Layout für einen Nichtgrafiker ganz gut gelungen. Trotzdem kam die tolle Vorlage nur wenige Male zum Einsatz und wenn ich im Nachhinein einmal Resümee ziehe, komme ich zu dem Schluss, dass die investierte Arbeitszeit in Bezug auf die tatsächliche Verwendung in keinem Verhältnis gestanden hat. Von den vielen verschiedenen Ansichten habe ich zum Schluss exakt zwei verwendet, das Deckblatt und eine allgemeine Inhaltsseite, mit der alle restlichen Darstellungen umgesetzt wurden. Die restlichen 15 waren halt da, falls man das künftig noch brauchen würde. Nach dieser Erfahrung plane ich keine eventuell zukünftig eintreffenden Anforderungen mehr im Voraus. Denn den wichtigsten Grundsatz in Sachen Wiederverwendung habe ich mit dieser Lektion für mich gelernt: Nichts ist so beständig wie die Änderung.

Diese kleine Anekdote trifft das Thema bereits im Kern. Denn viele Zeilen Code werden genau aus der gleichen Motivation heraus geschrieben. Der Kunde hat es noch nicht beauftragt, doch die Funktion wird er ganz sicher noch brauchen. Wenn wir in diesem Zusammenhang einmal den wirtschaftlichen Kontext ausblenden, gibt es immer noch ausreichend handfeste Gründe, durch die Fachabteilung noch nicht spezifizierte Funktionalität nicht eigenmächtig im Voraus zu implementieren. Für mich ist nicht verwendeter, auf Halde produzierter Code – sogenannter toter Code – in erster Linie ein Sicherheitsrisiko. Zusätzlich verursachen diese Fragmente auch Wartungskosten, da bei Änderungen auch diese Bereiche möglicherweise mit angepasst werden müssen. Schließlich muss die gesamte Codebasis kompilierfähig bleiben. Zu guter Letzt kommt noch hinzu, dass die Kollegen oft nicht wissen, dass bereits eine ähnliche Funktion entwickelt wurde, und diese somit ebenfalls nicht verwenden. Die Arbeit wird also auch noch doppelt ausgeführt. Nicht zu vergessen ist auch das von mir in großen und langjährig entwickelten Applikationen oft beobachtete Phänomen, dass ungenutzte Fragmente aus Angst, etwas Wichtiges zu löschen, über Jahre hinweg mitgeschleppt werden. Damit kommen wir auch schon zum zweiten Axiom der Wiederverwendung: Erstens kommt es anders und zweitens als man denkt.

Über die vielen Jahre, genauer gesagt Jahrzehnte, in denen ich nun verschiedenste IT- beziehungsweise Softwareprojekte begleitet habe, habe ich ein Füllhorn an Geschichten aus der Kategorie „Das hätte ich mir sparen können!“ angesammelt. Virtualisierung ist nicht erst seit Docker [1] auf der Bildfläche erschienen – es ist schon weitaus länger ein beliebtes Thema. Die Menge der von mir erstellten virtuellen Maschinen (VMs) kann ich kaum noch benennen – zumindest waren es sehr viele. Für alle erdenklichen Einsatzszenarien hatte ich etwas zusammengebaut. Auch bei diesen tollen Lösungen erging es mir letztlich nicht viel anders als bei meiner Office-Vorlage. Grundsätzlich gab es zwei Faktoren, die sich negativ ausgewirkt haben. Je mehr VMs erstellt wurden, desto mehr mussten dann auch gewertet werden. Ein Worst-Case-Szenario heutzutage wäre eine VM, die auf Windows 10 basiert, die dann jeweils als eine .NET- und eine Java-Entwicklungsumgebung oder Ähnliches spezialisiert wurde. Allein die Stunden, die man für Updates zubringt, wenn man die Systeme immer mal wieder hochfährt, summieren sich auf beachtliche Größen. Ein Grund für mich zudem, soweit es geht, einen großen Bogen um Windows 10 zu machen. Aus dieser Perspektive können selbsterstellte DockerContainer schnell vom Segen zum Fluch werden.

Dennoch darf man diese Dinge nicht gleich überbewerten, denn diese Aktivitäten können auch als Übung verbucht werden. Wichtig ist, dass solche „Spielereien“ nicht ausarten und man neben den technischen Erfahrungen auch den Blick für tatsächliche Bedürfnisse auf lange Sicht schärft.

Gerade bei Docker bin ich aus persönlicher Erfahrung dazu übergegangen, mir die für mich notwendigen Anpassungen zu notieren und zu archivieren. Komplizierte Skripte mit Docker-Compose spare ich mir in der Regel. Der Grund ist recht einfach: Die einzelnen Komponenten müssen zu oft aktualisiert werden und der Einsatz für jedes Skript findet in meinem Fall genau einmal statt. Bis man nun ein lauffähiges Skript zusammengestellt hat, benötigt man, je nach Erfahrung, mehrere oder weniger Anläufe. Also modifiziere ich das RUN-Kommando für einen Container, bis dieser das tut, was ich von ihm erwarte. Das vollständige Kommando hinterlege ich in einer Textdatei, um es bei Bedarf wiederverwenden zu können. Dieses Vorgehen nutze ich für jeden Dienst, den ich mit Docker virtualisiere. Dadurch habe ich die Möglichkeit, verschiedenste Konstellationen mit minimalen Änderungen nach dem „Klemmbaustein“-Prinzip zu rchestrieren. Wenn sich abzeichnet, dass ein Container sehr oft unter gleichen Bedienungen instanziiert wird, ist es sehr hilfreich, diese Konfiguration zu automatisieren. Nicht ohne Grund gilt für Docker-Container die Regel, möglichst nur einen Dienst pro Container zu virtualisieren.

Aus diesen beiden kleinen Geschichten lässt sich bereits einiges für Implementierungsarbeiten am Code ableiten. Ein klassischer Stolperstein, der mir bei der täglichen Projektarbeit regelmäßig unterkommt, ist, dass man mit der entwickelten Applikation eine eierlegende Wollmilchsau – oder, wie es in Österreich heißt: ein Wunderwutzi – kreieren möchte. Die Teams nehmen sich oft zu viel vor und das Projektmanagement versucht, den Product Owner auch nicht zu bekehren, lieber auf Qualität statt auf Quantität zu setzen. Was ich mit dieser Aussage deutlich machen möchte, lässt sich an einem kleinen Beispiel verständlich machen.

Gehen wir einmal davon aus, dass für eigene Projekte eine kleine Basisbibliothek benötigt wird, in der immer wiederkehrende Problemstellungen zusammengefasst werden – konkret: das Verarbeiten von JSON-Objekten [2]. Nun könnte man versuchen, alle erdenklichen Variationen im Umgang mit JSON abzudecken. Abgesehen davon, dass viel Code produziert wird, erzielt ein solches Vorgehen wenig Nutzen. Denn für so etwas gibt es bereits fertige Lösungen – etwa die freie Bibliothek Jackson [3]. Anstelle sämtlicher denkbarer JSON-Manipulationen ist in Projekten vornehmlich das Serialisieren und das Deserialisieren gefragt. Also eine Möglichkeit, wie man aus einem Java-Objekt einen JSON-String erzeugt, und umgekehrt. Diese beiden Methoden lassen sich leicht über eine Wrapper-Klasse zentralisieren. Erfüllt nun künftig die verwendete JSON-Bibliothek die benötigten Anforderungen nicht mehr, kann sie leichter durch eine besser geeignete Bibliothek ersetzt werden. Ganz nebenbei erhöhen wir mit diesem Vorgehen auch die Kompatibilität [4] unserer Bibliothek für künftige Erweiterungen. Wenn JSON im Projekt eine neu eingeführte Technologie ist, kann durch die Minimal-Implementierung stückweise Wissen aufgebaut werden. Je stärker der JSONWrapper nun in eigenen Projekten zum Einsatz kommt, desto wahrscheinlicher ist es, dass neue Anforderungen hinzukommen, die dann erst umgesetzt werden, wenn sie durch ein Projekt angefragt werden. Denn wer kann schon abschätzen, wie der tatsächliche Bedarf einer Funktionalität ist, wenn so gut wie keine Erfahrungen zu der eingesetzten Technologie vorhanden sind?

Das soeben beschriebene Szenario läuft auf einen einfachen Merksatz hinaus: Eine neue Implementierung möglichst so allgemein wie möglich halten, um sie nach Bedarf immer weiter zu spezialisieren.

Bei komplexen Fachanwendungen hilft uns das Domain-driven Design (DDD) Paradigma, Abgrenzungen zu Domänen ausfindig zu machen. Auch hierfür lässt sich ein leicht verständliches, allgemein gefasstes Beispiel finden. Betrachten wir dazu einmal die Domäne einer Access Control List (ACL). In der ACL wird ein Nutzerkonto benötigt, mit dem Berechtigungen zu verschiedenen Ressourcen verknüpft werden. Nun könnte man auf die Idee kommen, im Account in der ACL sämtliche Benutzerinformationen wie Homepage, Postadresse und Ähnliches abzulegen. Genau dieser Fall würde die Domäne der ACL verletzen, denn das Benutzerkonto benötigt lediglich Informationen, die zur Authentifizierung benötigt werden, um eine entsprechende Autorisierung zu ermöglichen.

Jede Anwendung hat für das Erfassen der benötigten Nutzerinformationen andere Anforderungen, weshalb diese Dinge nicht in eine ACL gehören sollten. Das würde die ACL zu sehr spezialisieren und stetige Änderungen verursachen. Daraus resultiert dann auch, dass die ACL nicht mehr universell einsatzfähig ist.

Man könnte nun auf die Idee kommen, eine sehr generische Lösung für den Speicher zusätzlicher Nutzerinformationen zu entwerfen
und ihn in der ACL zu verwenden. Von diesem Ansatz möchte ich abraten. Ein wichtiger Grund ist, dass diese Lösung die Komplexität der ACL unnötig erhöht. Ich gehe obendrein so weit und möchte behaupten, dass unter ungünstigen Umständen sogar Code-Dubletten entstehen. Die Begründung dafür ist wie folgt: Ich sehe eine generische Lösung zum Speichern von Zusatzinformationen im klassischen Content Management (CMS) verortet. Die Verknüpfung zwischen ACL und CMS erfolgt über die Benutzer-ID aus der ACL. Somit haben wir gleichzeitig auch zwischen den einzelnen Domänen eine lose Kopplung etabliert, die uns bei der Umsetzung einer modularisierten Architektur sehr behilflich sein wird.

Zum Thema Modularisierung möchte ich auch kurz einwerfen, dass Monolithen [5] durchaus auch aus mehreren Modulen bestehen können und sogar sollten. Es ist nicht zwangsläufig eine Microservice-Architektur notwendig. Module können aus unterschiedlichen Blickwinkeln betrachtet werden. Einerseits erlauben sie es einem Team, in einem fest abgegrenzten Bereich ungestört zu arbeiten, zum anderen kann ein Modul mit einer klar abgegrenzten Domäne ohne viele Adaptionen tatsächlich in späteren Projekten wiederverwendet werden.

Nun ergibt sich klarerweise die Fragestellung, was mit dem Übergang von der Generalisierung zur Spezialisierung gemeint ist. Auch hier hilft uns das Beispiel der ACL weiter. Ein erster Entwurf könnte die Anforderung haben, dass, um unerwünschte Berechtigungen falsch konfigurierter Rollen zu vermeiden, die Vererbung von Rechten bestehender Rollen nicht erwünscht ist. Daraus ergibt sich dann der Umstand, dass jedem Nutzer genau eine Rolle zugewiesen werden kann. Nun könnte es sein, dass durch neue Anforderungen der Fachabteilung eine Mandantenfähigkeit eingeführt werden soll. Entsprechend muss nun in der ACL eine Möglichkeit geschaffen werden, um bestehende Rollen und auch Nutzeraccounts einem Mandanten zuzuordnen. Eine Domänen-Erweiterung dieser hinzugekommenen Anforderung ist nun basierend auf der bereits bestehenden Domäne durch das Hinzufügen neuer Tabellenspalten leicht umzusetzen.

Die bisher aufgeführten Beispiele beziehen sich ausschließlich auf die Implementierung der Fachlogik. Viel komplizierter verhält sich das Thema Wiederverwendung beim Punkt der grafischen Benutzerschnittelle (GUI). Das Problem, das sich hier ergibt, ist die Kurzlebigkeit vieler chnologien. Java Swing existiert zwar noch, aber vermutlich würde sich niemand, der heute eine neue Anwendung entwickelt, noch für Java Swing entscheiden. Der Grund liegt in veraltetem Look-and-Feel der Grafikkomponenten. Um eine Applikation auch verkaufen zu können, darf man den Aspekt der Optik nicht außen vor lassen. Denn auch das Auge isst bekanntlich mit. Gerade bei sogenannten Green-Field-Projekten ist der Wunsch, eine moderne, ansprechende Oberfläche anbieten zu können, implizit. Deswegen vertrete ich die Ansicht, dass das Thema Wiederverwendung für GUI – mit wenigen Ausnahmen – keine wirkliche Rolle spielt.

Lessons Learned

Sehr oft habe ich in der Vergangenheit erlebt, wie enthusiastisch bei Kick-off-Meetings die Möglichkeit der Wiederverwendung von Komponenten in Aussicht gestellt wurde. Dass dies bei den verantwortlichen Managern zu einem Glitzern in den Augen geführt hat, ist auch nicht verwunderlich. Als es dann allerdings zu ersten konkreten Anfragen gekommen ist, eine Komponente in einem anderen Projekt einzusetzen, mussten sich alle Beteiligten eingestehen, dass dieses Vorhaben gescheitert war. In den nachfolgenden Retrospektiven sind die Punkte, die ich in diesem Artikel vorgestellt habe, regelmäßig als Ursachen identifiziert worden. Im Übrigen genügt oft schon ein Blick in das Datenbankmodell oder auf die Architektur einer Anwendung, um eine Aussage treffen zu können, wie realistisch eine Wiederverwendung tatsächlich ist. Bei steigendem Komplexitätsgrad sinkt die Wahrscheinlichkeit, auch nur kleinste Segmente erfolgreich für eine Wiederverwendung herauslösen zu können.


Non-Functional Requirements: Quality

published also on DZone 02.2020

published also on DZone 02.2020

By experience, most of us know how difficult it is to express what we mean talking about quality. Why is that so?  There exist many different views on quality and every one of them has its importance. What has to be defined for our project is something that fits its needs and works with the budget. Trying to reach perfectionism can be counterproductive if a project is to be terminated successfully. We will start based on a research paper written by B. W. Boehm in 1976 called “Quantitative evaluation of software quality.” Boehm highlights the different aspects of software quality and the right context. Let’s have a look more deeply into this topic.

When we discuss quality, we should focus on three topics: code structure, implementation correctness, and maintainability. Many managers just care about the first two aspects, but not about maintenance. This is dangerous because enterprises will not invest in individual development just to use the application for only a few years. Depending on the complexity of the application the price for creation could reach hundreds of thousands of dollars. Then it’s understandable that the expected business value of such activities is often highly estimated. A lifetime of 10 years and more in production is very typical. To keep the benefits, adaptions will be mandatory. That implies also a strong focus on maintenance. Clean code doesn’t mean your application can simply change. A very easily understandable article that touches on this topic is written by Dan Abramov. Before we go further on how maintenance could be defined we will discuss the first point: the structure.

Scaffolding Your Project

An often underestimated aspect in development divisions is a missing standard for project structures. A fixed definition of where files have to be placed helps team members find points of interests quickly. Such a meta-structure for Java projects is defined by the build tool Maven. More than a decade ago, companies tested Maven and readily adopted the tool to their established folder structure used in the projects. This resulted in heavy maintenance tasks, given the reason that more and more infrastructure tools for software development were being used. Those tools operate on the standard that Maven defines, meaning that every customization affects the success of integrating new tools or exchanging an existing tool for another.

Another aspect to look at is the company-wide defined META architecture. When possible, every project should follow the same META architecture. This will reduce the time it takes a new developer to join an existing team and catch up with its productivity. This META architecture has to be open for adoptions which can be reached by two simple steps:

  1. Don’t be concerned with too many details;
  2. Follow the KISS (Keep it simple, stupid.) principle.

A classical pattern that violates the KISS principle is when standards heavily got customized. A very good example of the effects of strong customization is described by George Schlossnagle in his book “Advanced PHP Programming.” In chapter 21 he explains the problems created for the team when adopting the original PHP core and not following the recommended way via extensions. This resulted in the effect that every update of the PHP version had to be manually manipulated to include its own development adaptations to the core. In conjunction, structure, architecture, and KISS already define three quality gates, which are easy to implement.

The open-source project TP-CORE, hosted on GitHub, concerns itself with the afore-mentioned structure, architecture, and KISS. There you can find their approach on how to put it in practice. This small Java library rigidly defined the Maven convention with his directory structure. For fast compatibility detection, releases are defined by semantic versioning. The layer structure was chosen as its architecture and is fully described here. Examination of their main architectural decisions concludes as follows:

Each layer is defined by his own package and the files following also a strict rule. No special PRE or POST-fix is used. The functionality Logger, for example, is declared by an interface called Logger and the corresponding implementation LogbackLogger. The API interfaces can detect in the package “business” and the implementation classes located in the package “application.” Naming like ILogger and LoggerImpl should be avoided. Imagine a project that was started 10 years ago and the LoggerImpl was based on Log4J. Now a new requirement arises, and the log level needs to be updated during run time. To solve this challenge, the Log4J library could be replaced with Logback. Now it is understandable why it is a good idea to name the implementation class like the interface, combined with the implementation detail: it makes maintenance much easier! Equal conventions can also be found within the Java standard API. The interface List is implemented by an ArrayList. Obviously, again the interface is not labeled as something like IList and the implementation not as ListImpl .

Summarizing this short paragraph, a full measurement rule set was defined to describe our understanding of structural quality. By experience, this description should be short. If other people can easily comprehend your intentions, they willingly accept your guidance, deferring to your knowledge. In addition, the architect will be much faster in detecting rule violations.

Measure Your Success

The most difficult part is to keep a clean code. Some advice is not bad per se, but in the context of your project, may not prove as useful. In my opinion, the most important rule would be to always activate the compiler warning, no matter which programming language you use! All compiler warnings will have to be resolved when a release is prepared. Companies dealing with critical software, like NASA, strictly apply this rule in their projects resulting in utter success.

Coding conventions about naming, line length, and API documentation, like JavaDoc, can be simply defined and observed by tools like Checkstyle. This process can run fully automated during your build. Be careful; even if the code checkers pass without warnings, this does not mean that everything is working optimally. JavaDoc, for example, is problematic. With an automated Checkstyle, it can be assured that this API documentation exists, although we have no idea about the quality of those descriptions.

There should be no need to discuss the benefits of testing in this case; let us rather take a walkthrough of test coverage. The industry standard of 85% of covered code in test cases should be followed because coverage at less than 85% will not reach the complex parts of your application. 100% coverage just burns down your budget fast without resulting in higher benefits. A prime example of this is the TP-CORE project, whose test coverage is mostly between 92% to 95%. This was done to see real possibilities.

As already explained, the business layer contains just interfaces, defining the API. This layer is explicitly excluded from the coverage checks. Another package is called internal and it contains hidden implementations, like the SAX DocumentHandler. Because of the dependencies the DocumentHandler is bound to, it is very difficult to test this class directly, even with Mocks. This is unproblematic given that the purpose of this class is only for internal usage. In addition, the class is implicitly tested by the implementation using the DocumentHandler. To reach higher coverage, it also could be an option to exclude all internal implementations from checks. But it is always a good idea to observe the implicit coverage of those classes to detect aspects you may be unaware of.

Besides the low-level unit tests, automated acceptance tests should also be run. Paying close attention to these points may avoid a variety of problems. But never trust those fully automated checks blindly! Regularly repeated manual code inspections will always be mandatory, especially when working with external vendors. In our talk at JCON 2019, we demonstrated how simply test coverage could be faked. To detect other vulnerabilities you can additionally run checkers like SpotBugs and others more.

Tests don’t indicate that an application is free of failures, but they indicate a defined behavior for implemented functionality.

For a while now, SCM suites like GitLab or Microsoft Azure support pull requests, introduced long ago in GitHub. Those workflows are nothing new; IBM Synergy used to apply the same technique. A Build Manager was responsible to merge the developers’ changes into the codebase. In a rapid manner, all the revisions performed by the developer are just added into the repository by the Build Manager, who does not hold a sufficiently profound knowledge to decide about the implementation quality. It was the usual practice to simply secure that the build is not broken and always the compile produce an artifact.

Enterprises have discovered this as a new strategy to handle pull requests. Now, managers often make the decision to use pull requests as a quality gate. In my personal experience, this slows down productivity because it takes time until the changes are available in the codebase. Understanding of the branch and merge mechanism helps you to decide for a simpler branch model, like release branch lines. On those branches tools like SonarQube operate to observe the overall quality goal.

If a project needs an orchestrated build, with a defined order how artifacts have to create, you have a strong hint for a refactoring.

The coupling between classes and modules is often underestimated. It is very difficult to have an automated visualization for the bindings of modules. You will find out very fast the effect it has when a light coupling is violated because of an increment of complexity in your build logic.

Repeat Your Success

Rest assured, changes will happen! It is a challenge to keep your application open for adjustments. Several of the previous recommendations have implicit effects on future maintenance. A good source quality simplifies the endeavor of being prepared. But there is no guarantee. In the worst cases the end of the product lifecycle, EOL is reached, when mandatory improvements or changes cannot be realized anymore because of an eroded code base, for example.

As already mentioned, light coupling brings with it numerous benefits with respect to maintenance and reutilization. To reach this goal is not that difficult as it might look. In the first place, try to avoid as much as possible the inclusion of third-party libraries. Just to check if a String is empty or NULL it is unnecessary to depend on an external library. These few lines are fast done by oneself. A second important point to be considered in relation to external libraries: “Only one library to solve a problem.” If your project deals with JSON then decide one one implementation and don’t incorporate various artifacts. These two points heavily impact on security: a third-party artifact we can avoid using will not be able to cause any security leaks.

After the decision is taken for an external implementation, try to cover the usage in your project by applying design patterns like proxy, facade, or wrapper. This allows for a replacement more easily because the code changes are not spread around the whole codebase. You don’t need to change everything at once if you follow the advice on how to name the implementation class and provide an interface. Even though a SCM is designed for collaboration, there are limitations when more than one person is editing the same file. Using a design pattern to hide information allows you an iterative update of your changes.


As we have seen: a nonfunctional requirement is not that difficult to describe. With a short checklist, you can clearly define the important aspects for your project. It is not necessary to check all points for every code commit in the repository, this would with all probability just elevate costs and doesn’t result in higher benefits. Running a full check around a day before the release represents an effective solution to keep quality in an agile context and will help recognizing where optimization is necessary. Points of Interests (POI) to secure quality are the revisions in the code base for a release. This gives you a comparable statistic and helps increasing estimations.

Of course, in this short article, it is almost impossible to cover all aspects regarding quality. We hope our explanation helps you to link theory by examples to best practice. In conclusion, this should be your main takeaway: a high level of automation within your infrastructure, like continuous integration, is extremely helpful, but doesn’t prevent you from manual code reviews and audits.


  • Follow common standards
  • KISS – keep it simple, stupid!
  • Equal directory structure for different projects
  • Simple META architecture, which can reuse as much as possible in other projects
  • Defined and follow coding styles
  • If a release got prepared – no compiler warnings are accepted
  • Have test coverage up to 85%
  • Avoid third-party libraries as much as possible
  • Don’t support more than one technology for a specific problem (e. g., JSON)
  • Cover foreign code by a design pattern
  • Avoid strong object/module coupling

Computer Science Library – My personal Top 10 IT Books

As I considered to write an article about my top 10 books, related to computer science and software engineering, I thought it will be an easy going task. In all the years over the last two decades, tons of great books fallen into my hands. This was the thing who made the job difficult. What should be the rules to put an title on the list? Only one title per author, different thematics, more than a hype and easy to understand, are the criterias for my own selection. Some of these books are really old. I suggest this is a good sign for stability. The ordering is a completely personal preference. So I hope you will enjoy my recommendations.

  • Effective Java 3. nd Edition, Joshua Bloch,(2017) ISBN: 0-134-68599-7
  • Peopleware: Productive Projects and Teams, Tom DeMarco, (2013), ISBN: 0-321-93411-3
  • Head First Design Pattern, Eric & Elisabeth Freeman, (2004) ISBN: 0-596-00712-4
  • Behind Closed Doors, J. Rothman & E. Derby, (2005) ISBN: 0-9766940-2-6
  • PHP Sicherheit 3 Auflage (German), C.Kunz · S. Esser · P. Prochaska (2010) ISBN: 978-3-89864-535-5
  • Mastering Regular Expressions 3rd Edition, Jeffrey E. F. Friedl, (2006) ISBN: 0-596-52812-4
  • GOD AND GOLEM, Inc. 7. th Edition, Norbert Wiener, (1966) ISBN: 0-262-73011-1
  • Java Power Tools, John F.Smart, (2008) ISBN: 978-0-596-52793-8
  • Advanced PHP Programming, George Schlossnagle, (2004) ISBN: 0-672-32561-6
  • Ich habe das Internet gelöscht! (German, Novell), Philipp Spielbusch, (2017) ISBN: 3-499-63189-X

As you can see is on top of my list, a book about JAVA programming. Well, it was the first title who gave me a giant change in the way of coding. Of course now exist much more brilliant titles who address this topic. My way to thinking in architecture starts like for the most architects with coding skills. But to do a great job you have to increase your knowledge about project management. The best way to start to understand how projects get successful done is read: Peopleware. A big surprise to me was find out that my favorite book about web security is written in German. It addresses solutions for the PHP Programming language, but the authors did a really great job to describe very detailed background information. For this reasons is this book extremely useful for all web developers who take care about security. But its not just technology between all. With God and Golem I remind a very old and critic philosophical text, I like to recommend to read. In the case you like this kind of topics check titles of Josef Weizenbaum, Noam Chomsky or Isaac Asimov. Java Power Tools was the first publication who covers DevOps Ideas. And last but not least a short funny novel about the experience of an IT Consultant with his clients. Lightweight and nice to read for relax. And don’t forget to smile. Feel free to leave a comment.