For more than a decade, it has been widely accepted that computer systems should be kept up to date. Those who regularly install updates reduce the risk of having security gaps on their computer that could be misused. Always in the hope that manufacturers of software always fix in their updates also security flaws. Microsoft, for example, has imposed an update requirement on its users since the introduction of Windows 10. Basically, the idea was well-founded. Because unpatched operating systems allow hackers easy access. So the thought: ‘Latest is greatest’ prevailed a very long time ago.
Windows users had little leeway here. But even on mobile devices like smartphones and tablets, automatic updates are activated in the factory settings. If you host an open source project on GitHub, you will receive regular emails about new versions for the libraries used. So at first glance, this is a good thing. However, if you delve a bit deeper into the topic, you will quickly come to the conclusion that latest is not always the best.
The best-known example of this is Windows 10 and the update cycles enforced by Microsoft. It is undisputed that systems must be regularly checked for security problems and available updates must be installed. That the maintenance of computer systems also takes time is also understandable. However, it is problematic when updates installed by the manufacturer paralyze the entire system and a new installation becomes necessary because the update was not sufficiently tested. But also in the context of security updates unasked function changes to the user to bring in I consider unreasonable. Especially with Windows, there are a lot of additional programs installed, which can quickly become a security risk due to lack of further development. That means with all consequence forced Windows updates do not make a computer safe, since here the additionally installed software is not examined for weak points.
If we take a look at Android systems, the situation is much better. However, there are enough points of criticism here as well. The applications are updated regularly, so the security is actually improved significantly. But also with Android, every update usually means functional changes. A simple example is the very popular Google StreetMaps service. With every update, the map usage becomes more confusing for me, as a lot of unwanted additional information is displayed, which considerably reduces the already limited screen.
As a user, it has fortunately not yet happened to me that application updates on Android have paralyzed the entire phone. Which also proves that it is quite possible to test updates extensively before rolling them out to users. However, this does not mean that every update was unproblematic. Problems that can be observed here regularly are things like an excessively increased battery consumption.
Pure Android system updates, on the other hand, regularly cause the hardware to become so slow after almost two years that you often decide to buy a new smartphone. Although the old phone is still in good condition and could be used much longer. I have noticed that many experienced users turn off their Android updates after about a year, before the phone is sent into obsolescence by the manufacturer.
How do you get an update muffler to keep his systems up to date and secure? My approach as a developer and configuration manager is quite simple. I distinguish between feature update and security patch. If you follow the semantic versioning in the release process and use a branch by release model for SCM systems like Git, such a distinction can be easily implemented.
But I also dedicated myself to the question of a versionable configuration setting for software applications. For this, there is a reference implementation in the project TP-CORE on GitHub, which is described in detail in the two-part article Treasue Chest. After all, it must be clear to us that if we reset the entire configuration made by the user to factory settings during an update, as is quite often the case with Windows 10, quite unique security vulnerabilities can arise.
This also brings us to the point of programming and how GitHub motivates developers through emails to include new versions of the libraries used in their applications. Because if such an update is a major API change, the problem is the high migration effort for the developers. This is where an also fairly simple strategy has worked for me. Instead of being impressed by the notifications about updates from GitHub, I regularly check via OWASP whether my libraries contain known risks. Because if a problem is detected by OWASP, it doesn’t matter how costly an update can be. The update and the associated migration must be implemented promptly. This also applies to all releases that are still in production
However, one rule of thumb applies to avoid update hell from the start: Only install or use what you really need. The fewer programs are installed under Windows and the fewer apps there are on the smartphone, the fewer security risks there are. This also applies to program libraries. Less is more from a security perspective. Apart from that, we get a free performance measurement by dispensing with unnecessary programs.
Certainly, for many private users the question of system updates is hardly relevant. Only new unwanted functions in existing programs, performance degradations or now and then shot operating systems cause more or less strong displeasure. In the commercial surrounding field quite fast substantial costs can develop, which can affect also the straight implementing projects negatively. Companies and people who develop software can improve user satisfaction considerably if they differentiate between security patches and feature updates in their release publications. And a feature update should then also contain all known security updates.
If you whish to discover a way how to turn negative vibes between testers and developers into something positive – here is a great solution for that. The thing I like to introduce is quite old but even today in our brave new DevOps world an evergreen.
Many years ago in the world wide web I stumbled over a PDF called Bug Fix Bingo. A nice funny game for IT professionals. This little funny game originally was invent by the software testing firm K. J. Ross & Associates. Unfortunately the original site disappeared long ago so I decided to conserve this great idea in this blog post.
I can recommend this game also for folks they are not so deep into testing, but have to participate in a lot of IT meetings. Just print the file, bring some copies to your next meeting and enjoy whats gonna happen. I did it several times. Beside the fun we had it changed something. So let’s have a look into the concept and rules.
Bug Fix Bingo is based on a traditional Bingo just with a few adaptions. Everyone can join the game easily without a big preparation, because its really simple. Instead of numbers the Bingo uses statements from developers in defect review meetings to mark off squares.
Rules:
Bingo squares are marked off when a developer makes the matching statement during bug fix sessions.
Testers must call “Bingo” immediately upon completing a line of 5 squares either horizontally, vertically or diagonally.
Statements that arise as result of a bug that later becomes “deferred”, “as designed”, or “not to fixed” should be classified as not marked.
Bugs that are not reported in an incident report can not be used.
Statements should also be recorded against the bug in the defect tracking system for later confirmation.
Any tester marks off all 25 statements should be awarded 2 weeks stress leave immediately.
Any developer found using all 25 statements should be seconded into the test group for a period of no less than 6 months for re-education.
“It works on my machine.”
“Where were you when the program blew up?”
“Why do you want to do it in that way?”
“You can’t use that version on your system.”
“Even thought it doesn’t work, how does it feel.”
“Did you check for a virus on your system?”
“Somebody must have changed my code.”
“It works, but it hasn’t been tested.”
“THIS can’t be the source of that module in weeks!”
“I can’t test anything!”
“It’s just some unlucky coincidence.”
“You must have the wrong version.”
“I haven’t touched that module in weeks.”
“There is something funky in your data.”
“What did you type in wrong to get it to crash?”
“It must be a hardware problem.”
“How is that possible?”
“It worked yesterday.”
“It’s never done that before.”
“That’s weird …”
“That’s scheduled to be fixed in the next release.”
“Yes, we knew that would happen.”
“Maybe we just don’t support that platform.”
“It’s a feature. We just haven’t updated the specs.”
“Surly nobody is going to use the program like that.”
The BuxFix Bingo Gamecard
Incidentally, developers have a game like this too. They score points every time a QA person tries to raise a defect on functionality that is working as specified.
Many ideas are excellent on paper. However, people often lack the knowledge of how to implement brilliant concepts into their everyday work. This short workshop aims to bridge the gap between theory and practice and demonstrates the steps needed to achieve a stable API in the long term.
(c) 2021 Marco Schulz, Java PRO Ausgabe 1, S.31-34
When developing commercial software, many people involved often don’t realize that the application will be in use for a long time. Since our world is constantly changing, it’s easy to foresee that the application will require major and minor changes over the years. The project becomes a real challenge when the application to be extended is not isolated, but communicates with other system components. This means that in most cases, the users of the application also have to be adapted. A single stone quickly becomes an avalanche. With good avalanche protection, the situation can still be controlled. However, this is only possible if you consider that the measures described below are solely intended for prevention. But once the violence has been unleashed, there is little that can be done to stop it. So let’s first clarify what an API is.
A Matter of Negotiation
A software project consists of various components, each with its own specialized tasks. The most important are source code, configuration, and persistence. We’ll be focusing primarily on the source code area. I’m not revealing anything new when I say that implementations should always be against interfaces. This foundation is already taught in the introduction to object-oriented programming. In my daily work, however, I often see that many developers aren’t always fully aware of the importance of developing against interfaces, even though this is common practice when using the Java Standard API. The classic example of this is:
List<String>collection=newArrayList<>();
This short line uses the List interface, which is implemented as an ArrayList. Here we can also see that there is no suffix in the form of an “I” to identify the interface. The corresponding implementation also does not have “Impl” in its name. That’s a good thing! Especially with the implementation class, various solutions may be desired. In such cases, it is important to clearly label them and keep them easily distinguishable by name. ListImpl and ListImpl2 are understandably not as easy to distinguish as ArrayList and LinkedList. This also clears up the first point of a stringent and meaningful naming convention.
In the next step, we’ll focus on the program parts that we don’t want to expose to consumers of the application, as they are helper classes. Part of the solution lies in the structure of how the packages are organized. A very practical approach is:
my.package.path.business: Contains all interfaces
my.package.path.application: Contains the interface implementations
This simple architecture alone signals to other programmers that it’s not a good idea to use classes from the helper package. Starting with Java 9, there are even more far-reaching restrictions prohibiting the use of internal helper classes. Modularization, which was introduced in Java 9 with the Jingsaw project [1], allows packages to be hidden from view in the module-info.java module descriptor.
Separatists and their Escape from the Crowd
A closer look at most specifications reveals that many interfaces have been outsourced to their own libraries. From a technological perspective, based on the previous example, this would mean that the business package, which contains the interfaces, is outsourced to its own library. The separation of API and the associated implementation fundamentally makes it easier to interchange implementations. It also allows a client to exert greater influence over the implementation of their project with their contractual partner, as the developer receives the API pre-built by the client. As great as the idea is, a few rules must be observed to ensure it actually works as originally intended.
Example 1: JDBC. We know that Java Database Connectivity is a standard for connecting various database systems to an application. Aside from the problems associated with using native SQL, MySQL JDBC drivers cannot simply be replaced by PostgreSQL or Oracle. After all, every manufacturer deviates more or less from the standard in their implementation and also provides exclusive functionality of their own product via the driver. If you decide to make extensive use of these additional features in your own project, the easy interchangeability is over.
Example 2: XML. Here, you have the choice between several standards. It’s clear, of course, that the APIs of SAX, DOM, and StAX are incompatible. For example, if you want to switch from DOM to event-based SAX for better performance, this can potentially result in extensive code changes.
Example 3: PDF. Last but not least, I have a scenario for a standard that doesn’t have a standard. The Portable Document Format itself is a standard for how document files are structured, but when it comes to implementing usable program libraries for their own applications, each manufacturer has its own ideas.
These three small examples illustrate the common problems that must be overcome in daily project work. A small rule can have a big impact: only use third-party libraries when absolutely necessary. After all, every dependency used also poses a potential security risk. It’s also not necessary to include a library of just a few MB to save the three lines required to check a string for null and empty values.
Model Boys
If you’ve decided on an external library, it’s always beneficial to do the initial work and encapsulate the functionality in a separate class, which you can then use extensively. In my personal project TP-CORE on GitHub [2], I’ve done this in several places. The logger encapsulates the functionality of SLF4J and Logback. Compared to the PdfRenderer, the method signatures are independent of the logging libraries used and can therefore be more easily exchanged via a central location. To encapsulate external libraries in your own application as much as possible, the following design patterns are available: wrapper, facade, and proxy.
Wrapper: also called the adaptor pattern, belongs to the group of structural patterns. The wrapper couples one interface to another that are incompatible.
Facade: is also a structural pattern and bundles several interfaces into a simplified interface.
Proxy: also called a representative, also belongs to the category of structural patterns. Proxies are a generalization of a complex interface. They can be understood as complementary to the facade, which combines multiple interfaces into a single one.
It is certainly important in theory to separate these different scenarios in order to describe them correctly. In practice, however, it is not critical if hybrid forms of the design patterns presented here are used to encapsulate external functionality. For anyone interested in exploring design patterns in more depth, we recommend the book “Design Patterns from Head to Toe” [3].
Class Reunion
Another step toward a stable API is detailed documentation. Based on the interfaces discussed so far, there’s a small library that allows methods to be annotated based on the API version. In addition to status and version information, the primary implementations for classes can be listed using the consumers attribute. To add API Gaurdian to your project, you only need to add a few lines to the POM and replace the ${version} property with the current version.
Marking up methods and classes is just as easy. The @API annotation has the attributes: status, since, and consumers. The following values are possible for status:
DEPRECATED: Deprecated, should not be used any further.
EXPERIMENTAL: Indicates new features for which the developer would like feedback. Use with caution, as changes can always occur.
INTERNAL: For internal use only, may be discontinued without warning.
STABLE: Backward-compatible feature that remains unchanged for the existing major version.
MAINTAINED: Ensures backward stability for the future major release.
Now that all interfaces have been enriched with this useful meta information, the question arises where the added value can be found. I simply refer you to Figure 1, which demonstrates everyday work.
Figure 1: Suggestion in Netbeans with @API annotation in the JavaDoc
For service-based RESTful APIs, there is another tool called Swagger [4]. This also follows the approach of creating API documentation from annotations. However, Swagger itself scans Java web service annotations instead of introducing its own. It is also quite easy to use. All that is required is to integrate the swagger-maven-plugin and specify the packages in which the web services reside in the configuration. Subsequently, a description is created in the form of a JSON file for each build, from which Swagger UI then generates executable documentation. Swagger UI itself is available as a Docker image on DockerHub [5].
Figure 2: Swagger UI documentation of the TP-ACL RESTful API.
Versioning is an important aspect for APIs. Using semantic versioning, a lot can be gleaned from the version number. Regarding an API, the major segment is significant. This first digit indicates API changes that are incompatible with each other. Such incompatibility includes the removal of classes or methods. However, changing existing signatures or the return value of a method also requires adjustments from consumers as part of a migration. It’s always a good idea to bundle work that causes incompatibilities and publish it less frequently. This demonstrates project stability.
Versioning is also recommended for Web APIs. This is best done via the URL by including a version number. So far, I’ve had good experiences with only incrementing the version when incompatibilities occur.
Relationship Stress
The great advantage of a RESTful service, being able to get along well with “everyone,” is also its greatest curse. This means that a great deal of care must be taken, as many clients are being served. Since the interface is a collection of URIs, our focus is on the implementation details. For this, I’ll use an example from my TP-ACL project, which is also available on GitHub.
This is a short excerpt from the try block of the fetchRole method found in the RoleService class. The GET request returns a 404 error code if a role is not found. You probably already know what I’m getting at.
When implementing the individual actions GET, PUT, DELETE, etc. of a resource such as a role, it’s not enough to simply implement the so-called HappyPath. The possible stages of such an action should be considered during the design phase. For the implementation of a consumer (client), it makes a significant difference whether a request that cannot be completed with a 200 failed because the resource does not exist (404) or because access was denied (403). Here, I’d like to allude to the telling Windows message about the unexpected error.
Conclusion
When we talk about an API, we mean an interface that can be used by other programs. A major version change indicates to API consumers that there is an incompatibility with the previous version. This may require adjustments. It is completely irrelevant what type of API it is or whether the application uses it publicly or internally via the fetchRole method. The resulting consequences are identical. For this reason, you should carefully consider the externally visible areas of your application.
Work that leads to API incompatibility should be bundled by release management and, if possible, released no more than once per year. This also demonstrates the importance of regular code inspections for consistent quality.
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:
Don’t be concerned with too many details;
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.
Conclusion
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.
Checklist
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
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