What are the best practices for designing robust, error-free software?

Clarify the requirements, test assumptions with stakeholders, challenge them to understand relative importance of requirements. Cover also non-functional ones from the very beginning. Decompose the problem to smaller blocks with clearly defined single responsibility and interface - that will help with testability and robustness. Use top down system approach. Follow industry best practices (e.g. SOLID) Be a minimalist - simpler designs are easier to evolve over product lifetime. During implementation, do also design reviews to detect early that new insights or requirements invalidated original assumptions.

Since developers are going to eventually implement these requirements. The requirements clarification phase needs to close the gap between what the customer needs and what the developer expects to implement. This is an iterative phase of: 1. Speaking with stakeholders, and domain experts 2. Gaining domain knowledge and being able to speak the same domain language 3. Focusing on the customer pain point and what is the problem before jumping into solutions 4. Researching competitor products and compare how they solve that problem 5. Have a quick prototype to showcase whether there are gaps or more questions to be answered

There is no such thing as error-free software, understanding the failure states of the product you are building should be considered part of the requirements. Software can be robust in presence of errors, if the system is able to self-heal and recover. This has been greatly explored in "Making reliable distributed systems in the presence of software errors" by Joe Armstrong. A soft real-time telephony switch will have different tolerance for errors than a hard real-time fly-by-wire system in an airplane.

Apart from correctness, error-free and robust software must have fault tolerance and testability built into it. Fault tolerance is the ability to recover from error conditions so that the overall system continues to function, despite a sub-system failing. This is possible by having independent error-handling harnesses at various levels of the system. The ability to diagnose and isolate errors is also critical. A necessary condition for this is to have the ability to test any part of the system in relative isolation. To achieve this, the system must be divided into modules that have clearly defined boundaries and interfaces.

This is the most important phase and make sure we are delivering value out of these requirements. At times we build some features but due to the lack of certain aspects, it doesn't add value to the stakeholders. So it is important to communicate about value delivered through this MVP.

1. Clearly define requirements. 2. Use a systematic development process. 3. Conduct regular code reviews. 4. Implement automated testing. 5. Prioritize robust error handling. 6. Validate user inputs and data. 7. Conduct performance testing. 8. Follow security best practices. 9. Maintain thorough documentation. 10. Use version control (e.g., Git). 11. Gather user feedback. 12. Monitor system performance. 13. Foster continual improvement. 14. Identify and manage risks.

1. Clear Requirements 2. Modular Design 3. Code Reviews 4. Testing 5. Automated Testing 6. Error Handling 7. Logging 8. Input Validation 9. Version Control 10. Documentation 11. Performance Monitoring 12. Security Audits 13. Scalability 14. User Feedback 15. Backup and Recovery 16. Code Refactoring 17. Dependency Management 18. Cross-Browser/Platform Testing 19. Compliance 20. CI/CD

Designing an error-free software, not only should it involve the perfect coding, but also the user experience. Requirement collection should be detailed enough for the soln to be used even without user training b'se it resonates with the problem/requirement. Development/Coding shouldn't be just to get things done but to get the experience right. Eg. - Naming of front-end data input labels/fields with words users are familiar with (speaking user's language). - Verification and Validations to ensure right inputs and avoid journey iterations. - Understandable and customized responses upon actions to make user understand. - Unbreakable coding (Proper Exception handling) Proper +ve&-ve testing. (SIT & UAT)

1. We're supposed to have a clear understanding of the requirements. 2. Identify the areas where design principles and patterns should be applied. 3. Write fully covered unit test cases. 4. Expert code review. 5. To prevent unintentional system vulnerabilities, conduct a thorough impact analysis of the code modifications. 6. Use a precise replica of the production environment to conduct both functional and non-functional testing. 7. Have a pipeline setup to follow the standard build process and also integration with additional tasks. e.g. code quality checker, vulnerabilities analysis tools, etc. 8. Update the dependencies on time to prevent vulnerabilities. 9. Setup an environment that is both highly available and scalable.

To understand the requirements, one must strive to understand the domain better than the experts. When eliciting requirements, you will get someone's perspective of the domain in a language they believe you will understand and you will then interpret it through the lens of your own cognitive bias. It's best to question (not dismiss) what you are told and find ways to reaffirm the expressed need, which may be a subset of the actual feature requirement or an overstatement of what is actually needed at that moment.

A good software design is based on understanding, tradeoffs and justification across multiple moving factors like 1. Functional requirements (features that address customer asks) 2. Non functional requirements (Security, scalability, availability, etc…) 3. Budget: Not every customer needs a top tier software, especially at the beginning. They may prefer faster market penetration with expectations to change and adapt the software further down the road 4. Team skills: While software design is expected to be technology agnostic, architects preferably have some technology choices in mind while building the design. These choices better align with what the team is familiar with unless there is a strong reason not to take that into consideration

A software architect must be well-versed in various system architectures, choosing the right one based on the problem, functional and non-functional premises, strategic definitions, and client demands. An often-overlooked aspect is the architecture's technological and functional longevity. A robust system should be technologically updatable, aligning with tool evolution, allowing for modernization and quick adaptation to vulnerabilities. Additionally, it should evolve functionally without compromising architectural principles. Architecture selection is a balance of trade-offs, and the architect's role is to steer choices, considering most variables to find the best path for system development.

In my experience creating a correct architecture is a two way (cyclic) process - top down, and bottom up. You should think about the overall picture and the building blocks involved including the ties between them And, simultaneously, you should look at each building block separately, understand its nature and its ability to interact as planned with the 'overall' picture

always put in your mind that 1-There's no one-size-fits-all solution, and chances are, others have faced similar challenges before 2-Learn from what worked before. It's like using blueprints for a house that's similar to yours. It saves time and makes sure your software is stable. 3-your software will evolve. What works today might need changes tomorrow. So, pick a flexible design that can grow with your needs

Architecture has many perspectives ; one leading to other, influencing or advising other. In my experience, a logical view of architecture is best to start with. It provides structure to system i.e key components that form the system, their role and dependencies. This perspective avoids technical/technology expressions or assumptions. Keeps the discussions more fundamental, open and unbiased. This provides bottom up approach towards building modular and cohesive architecture. Complementary yet non overlapping components. The logical architecture helps elicits the technical and non functional requirements for components and integrations. That is foundation for creating technical, integration and deployment/infrastructure architectures.

Architecture can be make very complex or very simple. The challenge I have seen most of the architects try to design the system based on their previous work. It id always a good idea to break your system in multiple modules for maintainability and future isolation, each subsystem ideally should be communicated with each other through well defined interfaces and always look for worst case scenarios that sub-systems will fail but overall system can degrade on the performance but still working. The other aspect of design I think needs major attention is sub-systems should have enough verbosity for troubleshooting.

Try multiple proof of concept apps to get a better understanding of potential architecture. Go for the battle tested mature tech stack,capable of satisfying the majority of known specifications. Avoid reinventing the wheel. A modular,loosely coupled, layered architecture generally works. Follow divide and rule policy. Deployment environment shall be considered while taking key decisions. Identify and address non-functional requirements early. Do as much as brain storming possible as a team for key decisions.

To choose an appropriate architecture for error-free software, it's crucial to understand both functional and non-functional requirements, analyze the system's scope and complexity, and prioritize quality attributes like scalability, security, and maintainability. Consider various architectural patterns such as microservices or layered designs. Involving the team in the decision-making process is essential for gathering diverse input. Ensure the architecture is flexible for future changes, and once chosen, document and communicate it clearly. Always be open to reviewing and iterating the architecture as the project evolves, ensuring a fit-for-purpose and adaptable strategy.

Depois de entendido o que queremos, partimos para o como, com o escopo definido dos requisitos conseguimos definir se vamos usar apenas um monólito ou se vamos adotar microserviços, se iremos aumentar a complexidade usando eventos, qual o tipo de dados teremos se usaremos SQL ou NoSQL, quais linguagens vamos utilizar levando em consideração o know-how do time e tudo isso com idéia de custos e tempo.

Your target herr shouldn't be completenes, instead, I found that this works best: - create two draft designs, one for how you want it to look like in 1 or 2 years, let's call it the vision, and one for the first iteration: - The vision: think of how it will evolve, what capabilities do you want it to have, resiliency, scale, visibility and developer experience. - For the first phase design, think of what is most important to deliver in your given timeframe, focus on that and more importantly, think how you can evolve from here to your vision. This practice focuses on evolvability, starting from a design that can be evolved as easy as possible into your vision, allowing you to focus on what is more important, rather than over-engineering.

Modules breakdown is the essence of why we have design decisions. They are the parts about which both the product and the team work in parallel. A good architectures identifies the integration point and tackle them first so that teams can further break down their individual pieces in parallel and work on them independently. Software architecture doesn’t need to solve every single detail ahead of time. It just needs to set the high level blueprint, direction and the module breakdown. It sets the standard for testing, non functional requirements that guide the teams. It considers evolution of individual modules to happen independently and assumes continuous change in requirements, modules and minimizes the friction points accordingly.

I've discovered that a valuable practice involves establishing precise boundaries for components, employing techniques such as Domain Driven Design. This strategy ensures that functionalities remain within their designated domains, preventing any unintended encroachment into other areas. By clearly defining these boundaries, we maintain clarity and cohesion, optimizing the efficiency and effectiveness of each component's role within the system."

Unpopular opinion: Modularity does not reduce complexity, it (almost) always increases it. It should always a balance between how modular you want to make one solution and the complexity this actually introduces. Take for example the Microservices design paradigm. If we are talking about a small system, a small team and no real plans of extending the system beyond a simple scope, then the modularity of a Microservices solution, orchestration, communication and integration is too much complexity.

In my experience ensure clear requirements and applying good testing exercises by implementing various testing methodologies (unit, integration, system, etc.) to validate different aspects of the software. In addition to Automation of tests can aid in continuous testing and early detection of issues.

Also, it is important to challenge a design. Involve the team members to design a system. Once the design is complete, show the design to other members of the Org and ask them to challenge it. Whenever a design is challenged, it becomes complete.

Keeping the system modular and optimally componentized is a critical part of software design. An efficiently done component breakdown allows the managers and developers to get a clear top-level view of the system. It also helps the developers debug and fix issues faster. Having a clear component breakdown philosophy brings a lot of predictability among large teams. Each component should have the following characteristics: - The components should be as independent as possible from each other. - Each component should bear a clear and fixed responsibility - It should facilitate reuse - It should be independently upgradeable - It should be independently maintainable - It should be independently scalable

It’s important to establish the distinct purpose to each component, this guides the process of breakdown of business journeys / processes into parts that are delivered through and housed into multiple collaborating components. Typically domain / capability model provides the sound approach towards identifying purpose to components. Domains could be functional or technical domains. Then components should abstract it’s behaviour/ functionality and data using well defined domain driven APIs. Domain driven hence reusable APIs avoid point to point / many to many dependencies, reducing software complexity. Interfaces also build abstraction while both producer and consumer iterate independently towards their evolution.

A program is essentially a language but a formal one. Think about a situation where somebody asked you, “Where is the best restaurant around?” and you went, “Pizza, salad, the one on the corner, and by the way, why not try Mexican food? I wanna add taking a train or taxi or walk if needed.” And the person says W…” and you say, well, add testing and architecture, and... and it would make sense? This means having modular, well-written code speaks for itself.

System modularization, regardless of the architecture used, is a core task for software architects. Dividing the system into single-responsibility modules, each responding to one actor, enhances testability, maintenance, and refactoring due to easier test construction. It also aids in human understanding - as humans are still, for the time being, the main agents in coding and architectural design, a role gradually being shared with AI algorithms. Modular design improves the overall comprehension of the system's context and documentation. It allows for more effective software lifecycle management, significantly extending longevity, facilitating bug elimination, and enabling quick deliveries of code updates or changes.

Break down the overall system into smaller functional units or components. Each component should encapsulate a specific set of functionalities or responsibilities. This initial decomposition lays the foundation for a modular design. Use encapsulation to hide the internal details of a component and expose only the necessary interfaces. Abstraction helps define clear boundaries and allows components to interact through well-defined interfaces, reducing dependencies. Clearly define the interfaces for each component, specifying how they interact with other components. Well-defined interfaces make it easier to understand how each component fits into the overall system and promotes easy integration.

Understanding system requirements is key to establishing a robust architecture and developing effective modules. In our agile teams, we bring together software engineers, quality engineers, and business experts right from the project's kickoff. This approach fosters a deep, shared understanding of the solution's needs and nuances. With a clear grasp of the requirements, software construction becomes more accurate, minimizes rework, and allows for more precise and continuous deliveries.

One thing that has helped is assertions! It is a lost art now as lot of these have moved to unit tests. Every function had assumptions, invariants clearly identified in code. This helped in quickly learn about the interface. Some languages provides guards at the signatures as well which is very much appreciated.

Robust and error controlled software is created when developers pay attention to the details. All methods need robust parameter checks not only for valid ranges but also logic patterns. All code must be wrapped in try/catch logic that applies to your language of choice. Internet connection failures, controller board failures, memory corruption are real and happen in the real world. Developers that anticipate these events will create the most robust and reliable code.

I've seen examples of APIs that don't communicate their SLAs nor provide consistent behavior against failure conditions. Adding these items to the interface contract allows the consumer to code for edge cases. Conversely consumers must consider the interface output can be empty, or delayed, causing processing errors in workflows.

To design clear interfaces one needs to understand the system requirements, since interfaces are contracts. Interface specifications should cover happy cases and errors too.

Again the role of the software architect to get the understanding of the needs through brainstorming. To build Interface and data model

As I said, if you have a good, easy to understand design, it will give you a clear and consistent interface. You have that, and you're good to use any application for integration.

The integrations between systems is critical. It’s important to understand the user journey through each integration. Only then you are able to build more robust requirements such as error handling, proper API call/response and payload.

I think this one is obvious. Using the correct data structure is a must. What is also very important is to use state of the art software engineering techniques. When querying huge data from the database, you should optimize performance from the go. A small example: Using IQueryables in C# for building the queries (for example with LINQ).

Some best practices as per my experience- 1. Good to start with logical view of data structures used in interfaces & then add tech/physical perspectives on top. Typically functional and technical experts are different, it keeps the process inclusive and well informed. 2. Use the right DB technology as per demands of problem. Do not default to RDBMS always… I.e. polyglot persistence … 3. Algorithms are at the heart of solution, it serves well to keep them modular, independently testable and replaceable 4. Components based architecture are distributed, along with in process representations of data it is important to have optimal design for distribution .. wire protocols, message formats, payload optimisation, lazy loading etc.. are important

Algorithms need to be understandable to the initial implementation team and to future developers. Arcane approaches that may be better in some metrics do not lend themselves to long term support. The KISS principle should be applied when choosing algorithms.

The scale of the data that you are working with should determine the data structures and algorithms you should be using. It is important to understand the complexity of data being modelled, the operations that need to be performed. The throughput requirements for read and writes, the typical patterns of retrieval, manipulations etc. If you can't do all of that in advance, make sure what you build is easy to change and refactor (unit tests!!).

Additionally to using the appropriate data structures and algorithms, developers need to make sure they add the proper documentation: both commenting the code to explain the complex parts, and including the rationale of how they chose these data structures / algorithms in a dedicated wiki page / readme (or what other form of documentation your team uses). Without this step, the code might be hard to use, hard to expand, or even incorrect / lacking. As team members leave and join the team / company, knowledge is lost and no matter how smart the initial code was, it can rapidly become a weak point.

Extend the built in architecture as much as possible before creating new objects which require their own maintenance and data life cycle. Define the data life cycle of each new object. Created, updated, and deleted. Define the security of the new object, who can create, update, read, delete. (CRUD)

Often, efficiency and performance are dependent on how you structure your data. Consider whether you're working in parallel (e.g. on a graphics card), or in serial (a single CPU embedded system), and whether there's a possibility you might switch in the future.

Also is necessary as a BA or expert to learn about the best languages that can be used. And if there is a new language or a very old always include a a manual

The most critical part of good design, underpins everything from data integrity, data quality, adaptive business rules and system processes and consumer and operational reporting.

Un aspecto muy importante acá es integrar en el diseño buenas prácticas para la gestión y protección de los datos. Esto debería ser una condición explicita que asegure una gestión apropiada de los datos, su seguridad y protección, atendiendo el acceso y uso previsto.

Podemos reforçar uma correlação direta entre uma estratégia abrangente de testes de software e a significativa redução de bugs em produção. A implementação precoce de testes de unidade estabelece um alicerce sólido, identificando e corrigindo erros antes que evoluam para problemas mais complexos. A automação, especialmente em testes de desempenho, contribui para a identificação antecipada de potenciais falhas, reduzindo a probabilidade de bugs prejudiciais na fase de produção. O comprometimento contínuo com práticas de teste robustas não apenas eleva a confiabilidade da aplicação, mas também minimiza significativamente a incidência de bugs em ambientes de produção, proporcionando uma experiência mais estável para os usuários.

A) Efficient logging- most of the time efficient logging is confused with quantity of logs. There are two harms with logging unnecessary 1. Performance overhead 2. Increased cost B) Observability - Implement observability as it helps proactive identification of future problems C) Monitoring - Efficient monitoring helps troubleshoot the problems quickly.

A useful way to ensure high quality tests is to look at the existing test mechanisms/test cases you have and ask yourself "If the production code is implemented/modified wrongly in any possible ways, will the test be able to catch it?"

Developing test scripts alongside coding is essential. An early lesson from a client highlighted the significance of utilizing testing frameworks like Selenium or Cucumber to verify the functionality of your code. Though it presented initial difficulties, our team soon embraced the practice and realized its benefits.

In my experience the CI/CD process and using tools like IDE’s with debuggers, test environments, Postman, Jenkins and/or Gitlab with pull requests and merges with manual approvals to run pipeline deployments to production along with tests is key to cleaner and mostly bug-free code (provided relevant unit and integration tests with max code coverage are part of the pipeline integration/deployment process).

Having an easy and quick testing tool is necessity for a robust software. More the testing, more the robustnes. Testing should be there at the component level so that the component can be tested after any change before the integration

TDD is the most under-rated skill in any software product development. The best way is to enforce the TDD so that all the features and units are tested even with the release of Nth feature/unit.

To ensure a system is error-free, engineers should implement multiple layers of tests: - Unit Testing - Integration Testing These tests should cover end-to-end scenarios and run during the build process. To make the system robust, additional testing layers such as: - Load/Performance Testing - Security Testing or Vulnerability Scanning These groups of tests should run regularly in a real environment(Prod) to detect and address any potential performance and security issues. For effective debugging of these issues, it's essential to have good descriptive logging. Alternatively, tools like New Relic, Dynatrace, or AppDynamics can be employed for enhanced monitoring and analysis.

Building quality in is a key aspect of high-quality software, but what does that mean? A major aspect of this is to shift away from thinking of tests as a way to verify behavior, but instead viewing them as a way to specify desired behavior. This is fundamental to the various test-first methodologies and serves to ensure that the modules of the software are appropriately focused on the capabilities they support. Clear and effective tests are so important, that the need to step through the code to debug it is often an indicator of ineffective test coverage. Finally, it is critical to give your code a field test by testing it not just at the unit level, but also at the component, and in many cases end-to-end.

In my experience, adhering to testing strategy at each development phase is essential to deliver error-free software. Special mention for Peer/Scrum testing which is instrumental for us to discover several issues before delivering the Solution to Integration/QA testing. Moreover, in addition to testing cycle our emphasis on stringent code review and code walkthrough with team continues to help us discover potential issues beforehand, improving code quality and knowledge sharing within the team.

If you want to create software that is robust and has a low defect rate, you need to ignore the structure of the advice that's been presented here. While we certainly we want to understand the requirements, establish a good architecture, etc., the advice is presented as a series of disjoint waterfall steps, e.g., "The first step...", "The second step...". If you wish to wish to understand the requirements, you need to build against them and iterate your understanding of what to build and how to build. Most of the learning is in the doing. Design, and therefore architecture, is an ongoing process of hypothesis and refinement. You're never done. So, if you're reading this, now is a good time to stop reading and spend your time more wisely.

Highly difficult to consider that a software is error-free as long as there are no systematic ways of proving the absence of errors. Having that in mind, designing error-free software would require, beyond all good aspects above, a well defined and implemented error handling strategy: exceptions handling, recovery states, error codes, logging and debugging, when to interrupt a communication or just completly stop execution, etc.

There is no software which is error free but they can be made robust and low error prone. A few points to consider for are 1. Understand the requirements clearly to avoid any edge case. 2. Use unit testing options to avoid basic coding mistakes. 3. Focus on system integration testing to check any issue before release. 4. Follow design principles whatever you are using. 5. Keep working on the feedback from users.

Choice of the right technology that includes tools, hosting platforms, 3rd party software is crucial. This should be considered upfront not only for performance, enhancements and scalability but also for pricing and budgeting.

Write it in Rust /s (but for real, you probably should write it in Rust) None of the 6 things above are particularly insightful or ground-breaking strategies. They're just the basic requirements of a software engineering role. If you're already a SWE, don't waste your time sifting through these comments looking for wisdom. Work with people, source quality feedback, and retrospect on past projects to continue to improve yourself. If you're trying to become a SWE or land your first job, then just recognize these things as requirements of the job, know what they mean, and move on. Your time is valuable, you should spend it elsewhere.

- Ensure that technical debt is kept at a minimum by refactoring and optimizing the codebase regularly, and by addressing any technical debt as soon as it is identified. - Conduct regular code audits to identify areas of the code that can be improved, and use the findings to make necessary changes to the software. - Implement robust error handling and logging mechanisms to provide developers with useful information in the event of an error, allowing them to diagnose and fix any issues quickly. - automate every aspect where human error can be present.

Implement robust ci/cd (continuous integration and delivery) pipeline to perform unit test, functional test, integration and performance test.

If you want to build good software you need a good fundament for it. Since software is using libraries and since it is built and tested using tools, the tools and libraries need to be qualified. Listen to the Validas podcast about tool and library qualification to learn how to create confidence into your fundament or ask me for a free strategy talk.

In my opinion, the most important thing it is all team and related teams have clear how to build software with intrinsic quality. It doesn't matter if it is fast or easy to develop and deploy, it has to be the exactly requirements with all test and demos needed to demonstrate it will be the best feature or product our client or customer needs.

Implement CI/CD: Enhance your CI/CD pipeline by incorporating Software Composition Analysis, License Compliance, Static Application Security Testing (SAST), Container Scanning, Secret Detection, Dynamic Application Security Testing (DAST), and Scheduled Pipelines. These tools and practices help identify vulnerabilities, enforce security standards, and ensure compliance throughout the development lifecycle. Enforce Gated Commits: Use Merge Requests or Pull Requests as gated commits. Require that all changes pass a thorough code review with at least two team members (four-eye principle) before being merged into the main branch. This practice ensures that code is vetted for quality and security, reducing errors and improving maintainability.