What are the best practices for designing software that is easy to maintain over time?

None of the things you list are particularly important. If you really want to evolve incrementally over time and have easily maintainable code, the critical things are to mirror the domain of the problem in your architecture, write the simplest possible code to solve the problem at hand, and right self documenting code.

Seguir los principios de SOLID es fundamental para diseñar software fácil de mantener a lo largo del tiempo. Estos principios SOLID (Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation y Dependency Inversion) promueven la modularidad, la cohesión y la flexibilidad del código. Al adherirse a SOLID, se crea un diseño claro, donde cada componente tiene una responsabilidad única y puede ser modificado sin afectar otras partes del sistema. Esto simplifica la depuración, actualización y ampliación del software, asegurando su adaptabilidad a medida que éste evoluciona.

I think Modularity is the most important factor when it comes to design a software. Breaking down the software into smaller modules or components and each module should have a clear and one purpose, making it easier to understand, modify, and test. Also SOLID principles are very important, documentation. Unit Testing is one of the important factors that will make sure to keep you code always clean and maintainable.

I always found below method very useful. Keep the interface (sometimes private functions) as independent as possible. Once functions are independent, it is better to make them static as once can test without any instance. Write test cases to cover all practical and theoretical scenarios ( practically, it is not possible to withdraw money if PIN is not authenticated, theoretical there is a interface to do so ). Make sure all test cases pass and keep on improving your code. Hope this helps.

Three main practices really: 1. Keep it is simple. 2. Document your design and code and take the necessary time to to keep your documentation updated. 3. Take some time to think before you code, or if you are used to think while coding (its individual) prepare to throw the first version away - don't patch a bad design because it is already written.

Designing and Building high-quality software relies on employing appropriate design patterns. Prior to coding, it's essential to establish a well-defined framework using appropriate design patterns, such as patterns for handling UI communication with the backend, inter-service communication, and data access patterns. Leveraging suitable design patterns facilitates code reusability and mitigates the need for significant refactoring when transitioning software to higher environments.

There is no evidence that design patterns improve maintainability. When used as programming conventions to support language-specific idioms such as reference counting, they make programming less painful. In an ideal world the GoF patterns should hardly ever be used because the programming language already supports what they do. It is only in portable assembly languages like C and Java that you need them. Be attentive to historic architecture-level patterns of the business domain, such as Hanmer's telecommunications patterns. These are a general blueprint that to encapsulate change as best as possible in a system that is "deeply intertwingled." Sometimes the division of code is obvious. Just do it: patterns just complicate things.

Design patterns are great and should be considered as and when appropriate. The key, though, is to use them when appropriate rather than trying to find a way to use a particular pattern.

Patterns are a shorthand that gets everyone on the same page quickly. If I say “event actor model” or “repository pattern,” you can look them up to know what I mean without me having to explain in detail. Everything you will write likely has a pattern. Instead of designing or writing from scratch, look for a pattern matching your use case. Learn from the mistakes and successes of others. Maybe it’s the right pattern. Maybe it’s the wrong pattern. At the very least, you will know what not to do. How else will you know a Singleton is generally a bad idea if you don’t know what it is? You’ll just make it and not know its name. By writing code ignorant of patterns, you’re more likely to repeat mistakes others have already learned from them.

Maintainability has a forward relationship with design partners and following solid principles Bus also using microservices instead of monolith I a good idea

Clear and detailed documentation is one of the essential factor for a long run well maintained software. I have witnessed many failure upgrades due to lack to clear documentation and successful/clean enrollments due to the presence of well structured SRS document.

I encourage my team to write clean code. For me, clean code is code that is easy to read, understand, and modify. It follows consistent naming conventions, uses meaningful comments, avoids unnecessary complexity, and adheres to coding standards and best practices. It is to some extent, self documenting.

The best documentatation of the design is the code itself. Yet, there are aspects of the design you cannot understand from the code: thread structures, cross-cutting concerns, auditing strategies, and so forth. These girders of the architecture should be documented. This is where patterns and pattern languages shine, though to do them well takes substantial investment.

By documenting design decisions we create a shared understanding among team members with ensuring the maintainability of software systems, especially as they grow in complexity At my workplace, we invest time in creating comprehensive design documentation, including architectural diagrams, API specifications, and inline comments. This documentation serves as a valuable reference for developers joining the project and helps us avoid misunderstandings and inconsistencies in the codebase. Additionally, maintaining up-to-date documentation facilitates smoother knowledge transfer and onboarding of new team members, ensuring continuity in software maintenance efforts.

Although software documentation is crucial, a common challenge lies in maintaining its accuracy over time. Standalone diagrams or documents often become outdated rapidly

Is this correct? Seems to me that refactoring refers to existing (design or software product), so you can't apply 'refactoring' to the first iteration of a new thing.

Em softwares multiplaforma criar uma camada em código comum e modularizado com toda a lógica que for possível. Na plataforma o código precisa ser o mínimo possível quebrando as funcionalidades em suas menores porções. Já tivemos ganhos na produtividade reduzindo o tempo de uma funcionalidade de uma semana para metade de um dia para fazer o mesmo trabalho.

You can apply refactoring to green field and legacy software. One rule of thumb I like to follow is that every method should fit on one screen. Once it goes beyond that, it's doing too much. Split that long method out into multiple methods and look for ways to simplify it.

Software is never done, it is just abandoned. The approach of 'getting something working', then as part of the first 'release' refactoring the code in a process of iterative development, can definitely contribute to more maintainable code. Even renaming to better reflect what things really do helps that future developer when they need to understand and change the code

It is the nature of development that in order to meet deadlines, the most efficient code isn't necessarily produced as function is the highest priority. Time should be allocated to revisit these known areas and refactor as necessary. As requirements grow over time and more functionality is required, more opportunities will arise to implement common code where common functionality is identified - this is when the temptation to use the copy and paste method of code reuse should be resisted and refactoring applied to produce common reusable code.

Many of the answers here extol the virtues of leveraging every single possible piece of testing to create software that is maintainable. This is fundamentally not true. Testing does not create better software. It is simply a brittle insurance policy that any modifications to the software may or may not break the rule setup in the test. Test do not magically make your software easier to read or better structured. You must do that work yourself.

Unit and integration tests are crucial to organise teamwork. Testing the code for behaviour can open up ways to keep all the developers' coding styles without interfering with each other's work negatively. The separation of concerns between modules can be translated into the separation of jobs of team members and this kind of organisation of the workload can be fruitful. As long as their code is well tested for behaviour and also well documented it can be easy to work together by relying on each other. We can trust as much in each other as much as our code is reliable. The key is not in the implementation details but in the loose coupling and testing well the behaviour. Well-tested code can easily be refactored without breaking anything.

Implement any kind of tests possible. From TDD, for unit testing and BDD to test the different features and scenarios to be implemented. This will provide a live documentation of what has been implemented as well as precious regression tests when the project grows in years and it is more likely to introduce code that breaks other implementations. Write feature files that are understandable for everyone, from stakeholders, project managers and of course developers. Always keep in mind the problem domain and how the cooperation of each part of the design should communicate with each other. Apply all SOLID principles during development to ensure every part of code is testable. Projects that are properly tested long live.

Testing is not the process of veryifying that your software meets requirements. There are important tests that you can apply that directly bode for maintainability. Consider two design alternatives to a problem. Evaluate how each one will cut across internal APIs. Counting those cutpoints is a test. The one with the fewer cutpoints generally leads to a more maintainable solutions. Test how much of your code is used in the field — it's typically about 40%. Delete the rest. A small system is a maintainable system. The more tests you have, the larger the code mass to maintain. Since (good) tests are coupled to the code (if they aren't, they really don't depend on what the code does), more tests means higher coupling and lower cohesion.

Sin duda intentar blindar tu software al máximo practicando cualquier tipo práctica de pruebas es una buena decisión pero ninguna de ellas será ni la mitad de eficaz que enfrentar cara a cara el software a un test con el usuario final. Ni las mentes más privilegiadas son capaces de imaginar los escenarios a los que son sometidos nuestros desarrollos por los usuarios.

I am playing accessibility champ role for my team for many years now. I understand this is hard for developers to change mindset to use this aspects, but they are very important. Many companies got sued for not complying with these aspects.

Ethical considerations are vital in software design, encompassing concerns like privacy, security, accessibility, fairness, and sustainability. By prioritizing ethical aspects, software designers contribute to creating products that are respectful, responsible, and beneficial for users and society. This approach helps mitigate legal, reputational, and moral risks associated with software. Considering ethical dimensions not only aligns with societal values but also ensures the long-term positive impact of software on individuals and the environment. It reflects a commitment to responsible technology development, fostering trust and goodwill among users and stakeholders.

Customer privacy is key and should be treated as critical. Mechanisms need to be set in place (automated testing etc) to make sure that customer sensitive data isn't exposed and customers are asked before for their data. Additionally auditers should be in place to check for compliance and projects that work with sensitive data shouldn't begin before an auditer approval.

Some of what is presented here is nonsense. I sat through a PhD trying to refactor a simple enumerated list to allow for extension without requiring customers to relink. After hours of applying design patterns, he ended up with a massive class diagram he couldn't explain (despite it being his ex in a course he charged $1k for 3 days) and which didn't meet his requirement. It took me two minutes to show him how to do this. Documentation is important, especially for design choices. Testing is important. I'd suggest TDD, but as long as there is automated testing, there's a measure of security. You mentioned ethics. Excuse me? By all means, designs should protect privacy. Fairness? Sustainability? Fairness to whom?

This is becoming more and more important to consider as we start building more solutions with AI. What happens to any personal data that is used in an AI prompt? Who does the data used to train AI models belong to? How do we prevent AI models from being biased? How sustainable is AI considering the computing resources required to support it?

Build something to be only as complicated as necessary. Do not over-engineer everything in an attempt to pre-solve or accommodate all imagined future requirements changes, because every mistake will likely only further complicate something that becomes ever more difficult to replace as it becomes ever harder to understand. Design as much as possible to be throw-away, with interfaces/contracts that support that goal. Not that you pre-decide to throw it all away, but if for example your current transmogrifier component no longer fits your needs, it may well be easier to completely replace it with something simple that does, than to grow and twist the original code in a way it wasn't intended to be.

The single most important consideration is to have a model for how the market is likely to evolve. Your domain model can encapsulate the areas of change, and you can choose a suitable design paradigm that will carve up your system into parts that are likely to evolve gracefully. There is no silver bullet here and our crystal balls are cloudy, but domain experience almost always creates traditional design boundaries and entities that history has evolved to support maintainability (as well as other -ilities). Don't turn to an architect to dicate the APIs, but do turn to domain experts — both inside and outside the software membrane — to advise about patterns of change.

With software design, having enough context is more important than having a detailed solution. We will never have enough information before implementation to design a perfect solution. Inevitably, what we know will change. We learn from the implementation done so far. Outside factors change requirements, schedules, or resources. A detailed design is too rigid to allow the team to adapt. Too much up-front design-decision slows projects down. It limits flexibility for the people implementing it. Prefer lightweight design processes that enable autonomy. We want just enough information for stakeholders to move forward. We don't need a perfect solution — only a good enough one. We don't need a detailed solution — only just enough of one.

Documenting the design is very helpful, but more important than that is to document the actual code. That means FIRST comment your code in plain language stating what you want the code to do, then write it in the actual programming language. The comments in your code should be readable understandable by non-programmers! This cannot be stressed too much - just do it.

Use fewer technologies. 80% is often good enough. The more technologies in the mix, the more spread-out the knowledge and the harder it is to get enough people to the expert level needed to get the most out of each technology.