¿Cómo se siguen los estándares y convenciones de los algoritmos?

One thing that I have found helpful is using both meaningful and descriptive names for both variables and functions. Depending on the language and acceptable community standards as usual.

Pseudocode is a widely accepted notation for describing algorithms. It uses natural language and basic programming structures for readability and clarity. Here's a concise example of pseudocode to find the maximum number in an array: function findMaximum(array) max = array[0] for i from 1 to length(array) - 1 if array[i] > max max = array[i] end for return max end function

When creating pseudocode, of the way in which the algorithms function, breaking them down to basic atomic or immutable variables before starting he process of writing an algorithm will help in making things easier. Notations, flowcharts, nomenclature all can be learned but an algorithm typically should be able to be understood by a 5 year old as well

I have consistently found value in beginning my problem-solving process with pen and paper. This involves approaching the problem in a straightforward manner, breaking it down into distinct steps, and continually refining the solution. For instance, sorting grades for students, I start by visually grouping and arranging the data on paper. This might entail creating categories like 'Grade A' (80 to 100 marks), and, 'Grade B' (70 to 79) Then, I sequentially examine the data and place each entry into the appropriate category based on its score. This approach allows me to gain a deep understanding of the problem, refine my solution iteratively, and finally translate it into pseudocode, making it a versatile and language-independent notation.

While choosing notation for writing a good algorithm, we need to keep in mind that in real world scenario, the standards may vary depending on team/project requirements, hence it is necessary to well-document the choosen notation system with all standard symbols, naming conventions and formulas. This ensures the algorithm's uniformity and consistency when things are taken over by other engineers.

function findMaximum(array) max = array[0] for i from 1 to length(array) - 1 if array[i] > max max = array[i] end for return max end function

Studying algorithms in university, a tool we utilized was trace tables, where you track every variables state as the algorithm iterates, you can see the change over time in conjunction with the pseudo code.

To keep pseudocode simple, readable and less complex, its a good idea to write it into multiple modules I.e. break the logical problem into multiple pseudocode sections. Try to keep each module operation atomic and focused towards solving particular logical scenario.

For me the best pseudo code has been comments laying out my general approach/steps to solve the problem. For example, in the context of a technical interview, let’s say they asked you to find any of the characters with the highest frequency by count: // create a hashmap: char -> freq/count // iterate over each character // update the hashmap // find the character with the highest count // return result This’s a powerful way to communicate your intent to your interviewer. Also, it will help you catch any edge cases that you may not have thought of before (ex. What if there are multiple characters with the same frequency?) And even outside interviews, it’s good to lay out a plan, execute, test, and finally cleanup/refactor :)

When we convert our algorithm to pseudo code along with that we should also mention its time and space complexity. This helps in understanding the performance of your algorithm. We should also prove the space and time complexity.

Comments are extremely valuable but often are used as alternative to a clear and understandable code, which instead should always be the priority. Writing clear and understandable code, very often, helps to entirely remove the need for comments in the first place. As golden rule, It's much better to spend time refactoring the code following the KISS and the single responsibility principles than writing comments. Of course there are exceptions, for example: - comments are extremely useful for code that can't be simplified because of the requirements - they are useful as well when the code depends on external components / apis, it's particularly helpful pointing out why one api / call is used in place of another if multiple are available

Excessive commenting within the code can convey the impression that the code itself lacks clarity. In my view, when appropriate naming is chosen and methods and variables are labeled in a manner that clearly indicates their respective purposes, the need for extensive comments elucidating individual method functions diminishes.

Usually meaningful (not lengthier) variables and method names will convey a lot more than comments But comments play a major role in making the third person to understand why you have introduced a method. Commenting on the actual focus of the method, parameters needed and the return value to expect. Also comments are useful while using complex flow, like recursive calls, templates or using many conditional checks, etc..

Comments make sense when the intent is not as clear or the logic is particularly tricky. I actually disagree with excessive comments (e.g. commenting every line of code). The goal of a comment is to remove ambiguity. If something is obvious like a variable assignment then we shouldn't add noise by commenting that line. It would be a distraction.

Using descriptive names (Nouns for classes, verbs for functions etc) for variables and functions can reduce the need for excessive comments. Opt for nouns when naming variables and verbs for functions. Comments are frequently employed to elucidate intricate logic, document temporary fixes, outline future tasks denoted by #TODO, or mark the deprecation of specific code sections.

The phase of result verification in software development demands a broad spectrum of test scenarios, including those that account for invalid and irrelevant data. It's common for developers to inadvertently introduce bugs when writing test cases that closely mirror the implementation. To mitigate this, it's crucial to approach test case creation independently, focusing on the expected output for a given set of data without being influenced by specific implementation details.

Unit testing is helpful. You can even think of edge cases before hand and run your algorithm against those edge cases as you develop it. This helps to build a certain level of confidence around your algorithm. The level of confidence needed depends on the domain of course. For algorithms that are used in software that end up being a matter of life and death (e.g. medical software, missile systems), more scrutinized testing and mathematical proofs may be needed.

Design and development of unit testing scripts can be done in side with algorithm design and development Starting with basic, higher level cases and development to handle that, then narrowing the cases and introducing different flow cases step by step and accordingly improvising the algorithm and flow. But this definitely will need time and can be practiced starting with some mini functionalities needed. So for time sensitive ones, we will automatically start our design with such a test driven development

Tests of base cases and corner cases not only validate the accuracy of the code that implements the algorithm, but also serve as an explanation of the details of the algorithm itself. Validating an algorithm's implementation by stepping through the code in a debugger can tremendously aid understanding of both the algorithm and how the implementation interacts with the rest of the system. Such exploration is especially valuable if you are not the original author of the code. However, this validation step is not a substitute for unit tests: because it is a one-time task, it does not guard against future regressions in the code due to maintenance.

Adhering to algorithm standards and conventions for verifying results involves several key practices. First, I ensure thorough testing with diverse datasets to validate accuracy and robustness. Second, I implement peer review processes, where colleagues evaluate the code and logic for potential flaws. Third, I follow established guidelines and best practices, such as maintaining clear documentation and adhering to coding standards. Finally, I use version control systems to track changes and enable easy rollback if needed. These steps collectively ensure the reliability and validity of the algorithm's outcomes.

For example see this sample: // Algorithm to find the largest number in a list Input: A list of numbers Output: The largest number in the list 1. Initialize a variable "largest" to the first number in the list. 2. Iterate over the list, starting from the second number. 3. For each number in the list, if it is greater than "largest", assign it to "largest". 4. Return "largest". This algorithm is clear, concise, and consistent. It also uses standard conventions for naming variables and writing pseudocode.

We can commence by crafting test cases prior to implementing the solutions. These test cases, covering diverse input scenarios and expected outcomes, ensure the highest level of correctness. Subsequently, when the code is written, these test cases can be employed to validate the results.

Break up solutions into smaller helper functions. This way it's easier to logically think about how to solve the solution by breaking a big problem up into pieces, and it normally would help with reducing duplicate code.

In an ideal world conventions would be unified. Everyone would be using the same kind of variable names or commenting style and frequency. However, in practical life such standards can change from team to team and project to project. Reading code and having the patience to read non-standard code are important skills to have for real-life projects. Most projects won't have you writing new algorithms from scratch. Most of your time will be spent finding inefficiencies in the codebase and rewriting them to be more efficient.

Code reviews should be performed with an emphasis on comments as well. Collaborate with your team members to discuss the comments. Leverage the documentation generators or commenting tools like Javadoc for Java to maintain consistency while writing precise comments, throughout your code.