1. Internship Programs for High School Students

Some companies and organizations offer structured internship programs specifically designed for high school students. These programs usually provide supervised training, allowing students to work on real projects, collaborate with professionals, and gain foundational knowledge about technology careers.

Examples of Internship Programs

• Google Computer Science Summer Institute (CSSI): An intensive program focusing on computer science fundamentals, CSSI is an excellent opportunity for students who want to learn from experts at one of the world’s top tech companies.
• Microsoft High School Internship Program: Microsoft offers high school internships that allow students to work directly with teams in a variety of departments, including software engineering and UX design.
• NASA STEM Engagement Program: For students interested in space and science, NASA offers internships and learning programs where participants can work on projects related to space exploration, engineering, and software development.

These programs are competitive, so students should prepare by building a strong resume, showing enthusiasm for technology, and highlighting any prior experience in coding, problem-solving, or teamwork.

2. Coding Camps and Hackathons

Many tech companies, universities, and non-profit organizations host coding camps and hackathons for high school students. Although they may not offer long-term internships, these events provide practical experience and exposure to real-world challenges. They’re also excellent networking opportunities, as students can connect with peers, mentors, and industry professionals.

Recommended Coding Camps and Hackathons

• CodeDay: A weekend event where students work on projects in teams, learning new skills and developing creative tech solutions.
• MIT LaunchX: A selective summer entrepreneurship program at the Massachusetts Institute of Technology, where students can work on tech startups.
• Major League Hacking (MLH): MLH offers hackathons across the globe, and many of them are open to high school students. They can join a team, work on a tech project, and gain valuable skills along the way.

Participating in these events can strengthen a student’s portfolio and provide experience with collaborative software development, teamwork, and problem-solving.

3. Tech Apprenticeships and Job Shadowing

If internships aren’t readily available, apprenticeships or job shadowing opportunities can also be valuable. Apprenticeships allow students to learn directly from experienced professionals by observing their daily tasks, asking questions, and gradually taking on responsibilities under supervision.

Where to Find Apprenticeship Opportunities

• Local Tech Companies: Startups and small businesses often welcome students who want to shadow professionals or work as assistants on IT projects. Students can contact local companies to inquire about shadowing or apprenticeship opportunities.
• Community Centers and Non-Profits: Organizations that focus on youth development may offer apprenticeships in IT fields or have partnerships with local tech companies.
• School Career Centers: Some high schools have career centers or partnerships with local businesses to provide job shadowing opportunities for students.

These apprenticeships or shadowing opportunities are often informal but can provide students with insights into what working in tech entails and how they can prepare for a career in this field.

4. Online Internship Platforms and Job Boards

There are online platforms dedicated to connecting high school students with internships and early career opportunities. These platforms allow students to search for remote internships, project-based roles, and volunteer work in various tech fields.

Platforms to Find Opportunities

• Internship.com: This platform lists internships across industries and allows students to filter by field, location, and eligibility.
• LinkedIn: LinkedIn is a valuable tool for finding internships, especially since many companies post internship openings on their profiles. High school students should create a LinkedIn profile to network with industry professionals and follow companies they’re interested in.
• Handshake: Handshake connects students with employers offering internships and entry-level jobs. Although more common for college students, some high school opportunities appear here.

By exploring these platforms, students can find a range of opportunities from virtual internships to project-based work, allowing them to work from home while still gaining practical experience.

5. Non-Profit and Community Tech Organizations

Non-profit organizations often offer tech programs and internships geared toward high school students. These programs are designed to be inclusive and provide accessible learning opportunities. Many focus on teaching coding, software development, and data science basics, while allowing students to contribute to meaningful projects.

Recommended Organizations

• Girls Who Code: This organization offers summer immersion programs for female high school students interested in technology and provides coding classes and community-building opportunities.
• FIRST Robotics: While not strictly an internship, FIRST Robotics involves students in tech projects and encourages them to solve real-world engineering challenges. It’s ideal for students who want hands-on experience with robotics, programming, and teamwork.
• Code.org: Known for its beginner-friendly coding programs, Code.org also has partnerships with companies that sometimes offer internships or mentorship opportunities for high school participants.

Non-profit organizations are great for students who may not have access to traditional internships or who are still building foundational skills and are looking for an entry point into IT.

6. University and College Programs for High School Students

Many universities offer pre-college tech programs during the summer, which can serve as alternatives to internships. These programs often cover coding, data science, artificial intelligence, and other key IT skills while giving students a taste of the college experience. Some of these programs even partner with tech companies, providing students with industry exposure.

Examples of University Programs

• Stanford Pre-Collegiate Summer Institutes: A range of tech courses that allow high school students to dive deep into subjects like computer science, AI, and cybersecurity.
• Carnegie Mellon’s Summer Academy for Math and Science (SAMS): A program for underrepresented students interested in STEM, where students get hands-on experience in programming and other tech skills.
• University of California’s COSMOS: A summer program focused on STEM fields, where students engage in intensive tech coursework and projects.

These programs are an excellent way for students to gain valuable IT skills, meet like-minded peers, and prepare for further education and internships.

7. Networking Through Online Tech Communities

Joining tech communities online can also lead to internship and project opportunities. Many companies and tech professionals share opportunities through social media or online communities dedicated to coding, software development, and data science. Platforms like GitHub, Stack Overflow, and Reddit have active communities where students can connect with professionals, ask questions, and learn about potential internship opportunities.

Popular Online Communities

• GitHub: By contributing to open-source projects on GitHub, students can gain practical experience and connect with professionals who might offer mentorship or internship opportunities.
• Stack Overflow: Known for Q&A in coding and development, Stack Overflow also has job boards and discussions where internship opportunities are shared.
• Reddit: Subreddits like r/csCareerQuestions or r/learnprogramming are filled with students and professionals in tech, and sometimes include internship postings or advice on finding opportunities.

Networking through these platforms can help students find valuable learning opportunities and make connections that may lead to internships.

Finding an internship in IT as a high school student may seem challenging, but there are a wide range of options available. Whether it’s a structured internship program, a coding bootcamp, or job shadowing at a local business, there are multiple pathways to gain hands-on experience. By leveraging online resources, networking, and staying proactive, students can build their skills, explore career paths, and make meaningful connections in the IT industry.

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Copywriter

This is such a specialist who writes content for websites, posts in social networks and other places where it is needed. The main task of a copywriter is to write unique content – this means that we can’t just take and copy text from some website and paste it to ourselves. Content must be invented from 0, well, or as 95% of copywriters do – retell in writing. It would seem that we learn to retell in school and there is nothing difficult about it. Everyone can retell one article, but to write on a regular basis about 10000 characters without spaces (zbp) every day … Not everyone can cope with this. In addition to uniqueness it is important to comply with technical requirements, you need to know a little SEO to organically (naturally) insert keywords and not only.

SEO specialist

Engaged in the promotion of websites and mobile applications. For example, if you type in Google – “buy pink cactus”, then this search engine throws you variants of sites where you can satisfy your request. The task of seoshniki to make sure that the promoted site was in the so-called TOP, that is, on the 1-10th place. Why? Because, often users do not go below the top 10. SEO is very interesting and limitless. There are recommendations of search engines and there are SEO optimizers who implement all these recommendations, and some even bypass them – virtuosos! Disadvantages of the profession: SEO is a pseudoscience and there are no clear rules as in programming, there are only recommendations of search engines. Sometimes one project can be promoted, the other does not work – the client is dissatisfied, the bosses too, and the blame will be naturally you.

SEM specialist or marketer

Knows SEO, PPC and all marketing tools. This IT specialty is suitable for web business owners or those who are just starting to launch their website. If you want to work in an IT company, you should know that the demand for general marketers is not that big. Disadvantages of the profession: in some situations, you can’t go anywhere without in-depth knowledge and the project may just freeze at one point. If you will be working in an IT company, then the demand for a general marketer is not that big. It’s almost impossible to get a job in freelancing – you need in-depth knowledge.

PPC specialist or contextologist

In this position, you set up advertising on search engines, social media. The basics can be learned on the Internet. You can even get a job without experience, but whether you will keep you for long is a big question. The value of contextologist depends on deep knowledge, which you can not find on the Internet. Without knowing them you can very quickly drain the entire budget, and a specialist who does not know how to save money – will not stay long.

SMM specialist

Leads projects in various social networks, makes posts, builds up an audience. There is little creative development here. Most promote projects in social networks unfairly, using for example bots. The result in the end is like dust in the eyes, like the numbers are there, but the use of them little. To move the project honestly need to make titanic efforts, know a lot of chips.

Web designer

Mostly draws in such software as Figma. For designers it is good to see the world is not like everyone else. A good designer has a share of innovation as he is sure that his vision is the best. You have to realize that NOT designers will always find something to pick on designers about. So either you are a narcissist and confident in your vision or you will just be morally run over. Disadvantages of the profession: due to the subjectivity of the industry, your work may not always be to the customer’s liking.

QA tester

With the help of special software, he tests software. Nothing complicated and the pay is good. Do not rush to become a tester at once – there are nuances… Monotony in work. Because of the ease of mastering the profession, there is a lot of competition. In IT companies that work for domestic projects, testing is not widespread.

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What is Memoization?

Memoization is essentially a targeted caching approach that saves results of functions based on specific input parameters. When a memoized function is called with certain inputs, the function first checks if the result for those inputs has already been computed. If so, it retrieves the stored result instantly, rather than recalculating. This process is highly beneficial for web applications because it can significantly reduce redundant processing, saving both time and server resources.

For example, let’s say your application frequently fetches data from an external API with the same request parameters. Memoizing these responses allows your app to serve stored data without repeatedly reaching out to the API. This results in a faster response time for users and reduced load on external services.

Benefits of Memoization for Web Applications

1. Enhanced Performance

By reducing redundant computations, memoization improves response times, especially for operations that involve heavy calculations or complex logic. The reduction in processing time allows your web application to handle requests faster, enhancing overall performance.

2. Reduced Server Load

Memoization minimizes unnecessary calculations, which, in turn, reduces the load on servers. This is particularly useful for high-traffic applications where efficient resource management is essential to handle large volumes of user requests.

3. Cost Efficiency

For applications that rely on paid external APIs or intensive database operations, memoization can lower costs by reducing the frequency of these calls. By caching results, applications avoid extra charges from repeated API calls and help optimize server usage costs.

4. Improved User Experience

Faster load times and quicker responses lead to a smoother and more satisfying user experience. Memoization’s role in reducing latency translates to users spending less time waiting, which can directly improve engagement and satisfaction.

Common Use Cases for Memoization in Web Applications

1. Caching API Responses

Memoization is highly effective when dealing with repeated API requests, such as fetching weather data, stock prices, or other external data that remains consistent over short periods. By memoizing these responses, you avoid sending repeated requests to the API, reducing latency and potentially saving on API costs if you’re using a paid service.

2. Optimizing Database Queries

Database queries, especially complex ones, can be resource-intensive. Memoizing specific query results can allow your application to deliver cached data for common requests instead of re-running the same query. This approach works well in cases where you serve frequent requests for static or rarely updated data, such as reports or analytics.

3. Dynamic Content Rendering

In applications that generate content dynamically based on user input or preferences, memoizing frequently requested data can improve loading times and reduce server workload. For example, if an application generates custom templates or loads personalized dashboards, caching results for similar inputs helps in serving users more quickly.

4. Resource-Intensive Calculations

Applications that involve computationally heavy tasks, such as those used for recommendations or data analysis, can benefit from memoization by storing results of specific calculations. This is especially valuable when the calculation involves intensive algorithms or large datasets. Storing previously computed results reduces the need for recalculations, thus speeding up response times.

Implementing Memoization in Web Applications

Memoization can be implemented in various layers within a web application, including client-side, server-side, and middleware. Each layer has unique benefits, and the choice of where to implement memoization depends on the specific needs of the application.

Client-Side Memoization

On the client side, memoization can be used to avoid unnecessary calls to the server by storing results locally in the user’s browser. This approach is effective for data that is reusable across multiple components or views within the application, such as user profile data or recent user actions.

Server-Side Memoization

On the server side, memoization can be used to store cached responses or computed results, making it ideal for applications with high traffic. Server-side memoization is particularly effective for caching database queries, API responses, and other recurring tasks. In a high-demand environment, server-side caching can reduce processing requirements and improve performance significantly.

Middleware-Level Memoization

Middleware is another ideal place to apply memoization in web applications. This approach is especially useful for caching responses at the routing level, where memoization can intercept and serve cached responses for common routes. This layer is highly effective for applications that have multiple instances of similar requests, such as frequently accessed API endpoints or data-intensive resources.

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One way to control loops is to use conditional statements, such as if statements, that check for loop exit conditions. For example, you can use a condition to exit the loop when a certain value is reached or when a condition is met.

You can also use a counter to limit the number of iterations of a loop. The counter is incremented at each iteration, and when it reaches a certain value, the loop is terminated.

Another way to control loops is to use exceptions. If an error occurs while the loop is executing, you can use an exception to terminate the loop.

Overall, loop control is a part of program code development that should be considered when writing any loop in a program. But it is important to avoid infinite loop execution to avoid program crashes and resource leaks.

Loop optimization

Loop optimization is an important part of optimizing software code in general, because loops can consume a lot of resources and slow down program execution. Here are some methods of loop optimization:

• Reducing the number of iterations. If you know in advance how many times you need to execute a loop, you can use a for loop to reduce the number of iterations;
• Using more efficient operations. When working with arrays of data, it is better to use special operations, methods optimized for searching, adding or removing elements;
• Avoiding unnecessary calculations. You can avoid repeated calculations by saving the results and using them again, rather than recalculating each time;
• Memory operations. You can reduce the number of memory operations by using more efficient algorithms and data structures that minimize the number of memory writes and reads;
• Use of parallelism. You can use parallel computing to execute loops on multiple processor cores at the same time, greatly speeding up program execution.

Loops are an important element in programming and can make data processing much easier. However, improper use of loops can lead to performance problems and even infinite code execution.

Understanding the basic principles of loops and using them in real projects is an important component of programmer literacy.

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• Acquisition of skills. Participation in the Olympiad allows you to develop and improve skills related to a specific subject area. This can be useful when looking for a job, as employers value professionals with practical knowledge and skills;
• Recognizability. Success in Olympiads can increase a person’s recognizability and reputation in academic and professional settings. This can attract the attention of potential employers and create additional opportunities for further career development;
• Broader Opportunities. Participation in Olympiads can open up a wide range of opportunities for participants, such as invitations to seminars and conferences, study abroad or study scholarships. It can help to make contacts and network with experts in the field, which can be useful in future job searches;
• Confirmation of competencies. Success in an Olympiad can provide evidence of high competence and proficiency in your chosen field. This can be an important indicator for employers when making hiring decisions;
• Developing teamwork skills. Many Olympiads require teamwork, which allows you to develop skills in collaboration, leadership, communication and problem solving in a group. These skills can be very valuable in the workplace, where collaboration and effective communication with colleagues is often required to succeed.

However, it should be noted that winning the Olympiad does not automatically guarantee a job or career success. It is also important to develop other aspects such as practical experience, social skills and networking. Olympiads can only be one part of a successful career and future job.

Strengthening academic skills through Olympiads

Olympiads provide a unique opportunity for students to strengthen their academic skills. Here are a few ways that Olympiads help in this regard:

• Knowledge Expansion. Olympiads go beyond the standard curriculum and address a variety of topics and tasks that are not encountered routinely in school. Participating in an Olympiad can broaden a student’s horizons and help them acquire new knowledge;
• Developing Critical Thinking. Olympiads often require participants to analyze and solve complex problems, which promotes critical thinking. Students learn the skill of analyzing, synthesizing information, and making informed decisions;
• Developing problem-solving skills. Olympiad problems are often non-trivial and require careful analysis and development of a solution strategy. Participation in the Olympiad helps students develop problem solving skills and find creative approaches to problem solving;
• Improving communication and representation skills. The Olympiad involves not only finding the correct answer to a problem, but also the ability to explain their solutions and present them in front of a jury. Participation in the Olympiad helps to improve oral and written communication skills;
• Increased confidence. Participation in the Olympiad requires students to step outside of their comfort zone and demonstrate their knowledge and skills in a competitive environment. This practice helps students increase their confidence in their abilities and improves their self-esteem.

Regular participation in Olympiads allows students to continually improve their academic skills and prepare for high standards of education. They can also serve as a good motivational tool for students who strive for success and excellence in the learning environment.

Demonstrating a competitive spirit through the Olympiad

The Olympics are a great example of demonstrating competitive spirit. At the Olympics, athletes from all over the world gather in one place to compete and determine who is the best in their discipline.

This competitive spirit can be seen at every stage of the Olympic process, from qualifying and preliminaries to final matches and victory ceremonies. Athletes and teams give their all on the battlefield to prove their excellence.

The Olympics is not just a competition in sports, it is a battle for the honor, pride and glory of one’s country. Athletes put in many hours of training and preparation to reach the pinnacle of their skill. They overcome their physical and emotional limitations in an effort to win.

The Olympics also attracts a huge amount of attention from spectators and sports fans. Millions of people around the world look forward to the event to see the wonderful performances of the athletes and to show their support.

All this competitive spirit at the Olympic Games creates the atmosphere of a great battle, where only the strongest will achieve victory. And this inspires not only the athletes, but also all those who watch the competition. They see that through hard work, perseverance and dedication one can achieve great results and overcome oneself.

Overall, the demonstration of competitive spirit at the Olympics shows that the desire to win and excel is an important aspect of human nature. The Olympics not only showcases athletic skills and capabilities, but also inspires all of us to fight for our dreams and goals.

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1. Stay Calm and Assess the Problem Thoroughly

When faced with an unusual problem, it’s essential to stay calm and approach it systematically. These problems can appear overwhelming initially, so start with a clear, level-headed assessment:

• Identify the Problem’s Requirements: Look closely at what the problem is asking. Try to rephrase the problem in simpler terms to ensure you understand the goal and constraints fully.
• Analyze the Inputs and Outputs: Carefully consider the input format, size, and constraints, as well as the expected output. This helps clarify the scope of the solution and can provide hints about which approaches might be suitable.

Taking this preliminary step can prevent misunderstandings and allows you to focus on creative problem-solving without being overwhelmed.

2. Break Down the Problem into Smaller Parts

Unusual problems are often complex, so breaking them down into manageable sub-tasks can be incredibly helpful. Here’s how to approach this:

• Identify Core Tasks: Look for distinct parts of the problem that you can work on individually. For example, if the problem involves multiple steps or transformations, try to solve each one separately.
• Think Step-by-Step: Outline the sequence of actions or computations you believe will lead to the solution. Treat each step as a mini-problem and aim to understand how each part contributes to the final outcome.
• Solve Simplified Versions: Start with a simpler version of the problem by using smaller inputs or limiting the scope. Solving a smaller problem can reveal patterns or provide insights that will help with the full version.

By deconstructing a problem, you’re less likely to feel overwhelmed and can concentrate on each part, increasing the chances of a breakthrough.

3. Use a Variety of Problem-Solving Techniques

When standard methods fall short, it’s essential to have a repertoire of alternative techniques. Here are some versatile strategies to consider:

• Pattern Recognition: Unconventional problems often have underlying patterns. Examine sample inputs and outputs to identify recurring themes or structures. Once you spot a pattern, you can leverage it to build a solution.
• Reverse Engineering: If the problem allows, work backward from the output to the input. This approach can uncover dependencies or constraints that may not be obvious at first glance.
• Trial and Error with Hypotheses: Sometimes, attempting different methods can provide insights. Try a brute-force solution, simulate the problem with basic logic, or use random test cases to see if they reveal any useful trends.

Experimenting with these methods can help you find a starting point or reveal aspects of the problem that lead to an effective solution.

4. Draw Diagrams or Use Visual Aids

Visualizing the problem can be an incredibly effective tool, especially when dealing with complex or unconventional challenges. Here’s how to use visual aids:

• Flowcharts and Diagrams: Draw a flowchart to visualize the steps of your algorithm or a diagram to illustrate data structures. Visuals can help you see the “big picture” and understand the relationships between different parts of the problem.
• Graphs and Grids: For problems involving networks, grids, or paths, sketching the layout or relationships can make the solution clearer. For example, graph problems often benefit from visual representation, as it shows how nodes and edges connect.
• Trace Small Test Cases: Write down a few small test cases and trace them manually. Observing how data changes at each step can uncover patterns, identify errors, or clarify the solution’s logic.

Using visual aids is especially helpful when the problem involves complex relationships or requires tracking multiple variables.

5. Consider Combining Known Techniques

Unconventional problems often require a combination of techniques. When one approach isn’t enough, consider mixing strategies. Here are a few common combinations:

• Dynamic Programming and Greedy Algorithms: In some cases, a problem that appears to be greedy may need an additional layer of optimization with dynamic programming to ensure the best solution.
• Graph Theory and Search Algorithms: For problems involving connections, networks, or paths, combining graph structures with depth-first search (DFS) or breadth-first search (BFS) may reveal an efficient solution.
• Mathematical Formulas with Simulations: If the problem has a numeric aspect, use mathematical formulas to simplify parts of it, then simulate the remaining steps for accuracy.

By blending techniques, you can tackle different aspects of the problem more effectively and arrive at a solution that might not be obvious through a single approach.

6. Experiment and Iterate

For unconventional problems, don’t hesitate to experiment. Testing different approaches can provide new perspectives or reveal simpler solutions. Here’s how to experiment effectively:

• Implement a Basic Solution First: Even if you know it’s not optimal, writing a basic solution can help clarify the problem. This initial solution can serve as a benchmark for more refined attempts.
• Use Random Inputs for Testing: Create random inputs and analyze the outputs. This can help identify potential issues and edge cases or suggest patterns in the data that weren’t initially obvious.
• Iterate on Your Solution: Once you have a working solution, look for parts that can be optimized or simplified. Often, the best solutions emerge after refining initial attempts based on observed results.

Experimenting and iterating allows you to learn from each attempt, gradually improving and optimizing your solution.

7. Stay Open to Alternative Approaches

For unconventional problems, flexibility is key. Don’t lock yourself into a single approach if it’s not working. Here are a few ways to maintain an open mindset:

• Explore Different Data Structures: Experiment with less common data structures like heaps, tries, or disjoint sets. Sometimes the right data structure is the key to unlocking a tricky problem.
• Look for Analogous Problems: Think of similar problems you’ve solved before, even if they were in different contexts. Sometimes, a technique from a seemingly unrelated problem can inspire a new approach.
• Review the Problem Requirements Again: Occasionally, revisiting the problem statement with a fresh perspective can reveal details you initially overlooked or misinterpreted.

Keeping an open mind encourages creative solutions and helps you adapt when initial attempts don’t work out.

8. Practice Developing Your Problem-Solving Skills

Dealing with unexpected problem types becomes easier with practice. Regular exposure to a variety of problem types and challenging scenarios will strengthen your ability to handle surprises. Here are some practical steps to boost your skills:

• Solve a Variety of Problems: Try problems from different categories (e.g., graph theory, dynamic programming, string manipulation). Diverse problem types prepare you for unconventional challenges.
• Participate in Coding Contests: Many contests include unusual or creative problems. This exposure helps you adapt quickly and develop resilience when facing unexpected challenges.
• Learn from Solutions: After solving a problem or reviewing an editorial, reflect on why specific approaches worked. This retrospective learning helps you recognize patterns and techniques for future problems.

Building a strong foundation through consistent practice makes it easier to navigate new and unexpected problems.

Unconventional problems in programming require creativity, flexibility, and a systematic approach. By remaining calm, breaking down the problem, combining techniques, and staying open to new approaches, you can turn a seemingly unsolvable problem into an achievable challenge. With practice and the right mindset, you’ll build confidence in tackling any type of problem—no matter how unusual or complex.

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Also, code reviews can be unpredictable in estimating when exactly a new feature will hit the release. Next, we’ll look at how to make going through a code review easy and simple for you and your team members with simple rules.

Why Code Review is difficult and time-consuming

The entry point for a review is the Pull Request (PR). A PR is a request to merge your code changes, which are located in a separate branch, into the main project branch. It is in this request that your code is reviewed, so the ease and quality of Code Review depends not only on the code itself, but also on how the PR is designed.

The first problem that complicates the code review process is poor description of the pool-requests. Since the process itself starts by opening the PR page, it’s obvious to assume that your coworker will end up on a blank page. If there is no description in the PR, it will take longer for the reviewer to understand the context of the problem you were solving.

In addition to the first problem, it is important to emphasize the development history. Any large project eventually grows legacy solutions that new programmers work with. One of the ways to understand why a certain code was written is to look at who it was written by and what pool-request it was injected into the code base (Blame view). It is very sad in this case to come across empty PRs with titles like “Small fix” or “Update module”.

The second problem of Code Review is, of course, the code itself. We won’t talk about the quality and depth of the solution itself, as it depends on the developer’s qualification, but even a well-written code can stay on the review for a long time for such reasons as: a large number of changes (20-30 or more files), unnecessary code refactoring, non-compliance with the code design style and so on.

Let’s consider below the main rules and tips, following which you can speed up Code Review and at the same time simplify the work of yourself and your colleagues.

The main rule – value the reviewer’s time

This rule covers all the main tips and it even sounds obvious. Very often we think of reviewers as people who owe you, forgetting to take care of the proper quality of code and description of the pool-request.

The first reviewer is you
Just imagine as if you are reading your changes for the first time. To do this, just open the git diff changes tab (supported by most popular IDEs).

More often than not, we send commits and create PRs at the end of the day. So, read your code again in the morning. This often helps catch obvious bugs and places where you can improve your code.

Format the Pull Request

It’s important to give maximum context about the task to your colleague before they start looking at the solution. You have a Title and PR Descriptions at your disposal,

Title
The title should clearly describe why you are adding the change. More often than not, it is convenient to duplicate the task title.

Most version control systems support integration with project management services (jira, etc). Use this to include the task ID in the title, which will automatically become a link, allowing you to go to the task in one click and find out more information about the work you’ve done

Minimize changes

A single Pull Request should contain as few code and file changes as possible. This will allow your coworkers to check it out more quickly, without missing critical bugs or losing context. It’s also easier to inject such code into the codebase, as you’re less likely to run into merge conflicts.

If the PR contains an incomplete solution to your problem, be sure to mention it in the description. That way you won’t get obvious questions that will waste your time. And in case the final solution will be realized by future tasks, then add references to those tasks or just describe them.

Make refactoring a separate task

The most tempting thing is to fix bugs and code issues that are not relevant to solving the problem in the file you are working with. Avoid this. Create a task for such changes and move on – you’ll bring more value to your team that way.

A reviewer may even on the simplest and most obvious code refactoring ask to cover the code with tests, which will delay your release indefinitely. And the reviewer will be absolutely right. That’s why it’s better to plan refactoring, or even better – to plan to cover the code with tests if it hasn’t been covered earlier and then create a task for refactoring.

Separate important development steps into separate commits

When working on complex tasks, it is useful to separate logical changes into separate commits. This simplifies code review when there are a large number of changes in PR.

But you don’t want to create commits for every new file or small fix. Use Squash or Amend for this, overwriting old commits with new changes or merging multiple commits into one.

Don’t mix code style changes with functional changes

This point may seem complicated, but it is obvious. It’s very difficult to review code changes where 1-2 lines have been made, resulting in a shift of the entire function by one indent. Such a change will look like you deleted the entire function and rewrote it.

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1. Understanding the Role of Constraints

Constraints specify the size limits for inputs and outputs, the range of values for variables, and sometimes even the expected behavior of the solution. These limitations help you understand what your algorithm can handle and allow you to determine if certain solutions will be feasible. Here’s why they’re essential:

• Determine Algorithm Complexity: Constraints guide your choice of algorithm by indicating acceptable time and space complexity.
• Filter Out Infeasible Approaches: Large constraints, for instance, often eliminate the possibility of using brute-force solutions.
• Guide Edge Case Considerations: Constraints inform you of the minimum and maximum input values, helping you identify potential edge cases.

By keeping constraints in mind, you ensure that your approach is both feasible and efficient for the given problem.

2. Analyze Time Complexity Based on Constraints

When tackling a problem, understanding the acceptable time complexity based on the constraints is crucial. Here’s a general rule of thumb for different constraint ranges and corresponding algorithm complexities:

• Small Constraints (e.g., n≤100n \leq 100n≤100): Algorithms with O(n3)O(n^3)O(n3) or O(n4)O(n^4)O(n4) complexity may be feasible for very small input sizes.
• Moderate Constraints (e.g., n≤10,000n \leq 10,000n≤10,000): Here, O(nlog⁡n)O(n \log n)O(nlogn) or O(n2)O(n^2)O(n2) solutions are generally manageable.
• Large Constraints (e.g., n≤106n \leq 10^6n≤106 or higher): For large input sizes, you should aim for O(n)O(n)O(n) or O(nlog⁡n)O(n \log n)O(nlogn) complexity at most. More complex algorithms may exceed the time limit.

Estimating complexity from constraints helps you focus on algorithms that will complete within the allowed time. For example, a problem with n≤105n \leq 10^5n≤105 generally requires an O(nlog⁡n)O(n \log n)O(nlogn) solution or better, while O(n2)O(n^2)O(n2) algorithms are unlikely to run within the limit.

3. Consider Space Complexity Limits

Besides time, memory constraints play a vital role in algorithm design. Memory limitations can restrict the type of data structures you use or the way data is stored. Here’s how to approach space optimization:

• Avoid Unnecessary Data Storage: Only store what is essential for solving the problem. For example, use in-place modification when possible to avoid extra space.
• Use Lightweight Data Structures: Opt for efficient data structures like arrays instead of hash maps when possible, as hash maps can consume more memory.
• Limit Recursion Depth: Deep recursion can lead to stack overflow, especially in languages that don’t handle recursion optimally. Consider iterative solutions if recursion depth is an issue.

Understanding space limitations helps you avoid memory overflows and optimize the data structures you choose, which is crucial for large inputs.

4. Optimize by Avoiding Redundant Operations

Once you’ve analyzed the constraints, you can refine your solution by reducing redundant computations. Here’s how:

• Use Caching or Memoization: If your algorithm recalculates the same values multiple times (e.g., in recursive solutions), caching results can save time. Memoization is particularly effective for dynamic programming problems.
• Precompute Results: For tasks that involve repeated calculations (like factorials or combinations), consider precomputing values. This is especially useful for algorithms with overlapping subproblems.
• Skip Unnecessary Computations: Avoid processing parts of the data that don’t contribute to the final result. For instance, if you’re only interested in the maximum or minimum values, don’t store or sort the entire dataset.

These optimizations can help reduce both time and space usage, making your solution feasible under tight constraints.

5. Divide and Conquer Techniques

If the problem constraints allow it, divide-and-conquer algorithms like binary search or merge sort can help you manage large inputs efficiently. Here’s how to apply them based on constraints:

• Binary Search: If the input constraints suggest that a linear search will be too slow, binary search (with O(log⁡n)O(\log n)O(logn) complexity) is a great alternative. It’s ideal for sorted data or when you can structure the problem to make sorted segments.
• Divide and Conquer Algorithms: For problems that involve large datasets, divide-and-conquer methods break down data into smaller segments, allowing you to handle each part independently and combine results.

These approaches are highly effective in problems where a direct solution is too slow and can meet tight time constraints.

6. Identify Edge Cases and Boundary Conditions

Constraints help you anticipate edge cases, such as minimum or maximum possible input sizes, extreme values, or special conditions that could break your algorithm. To prepare for these cases:

• Test Minimum and Maximum Inputs: If nnn can be as small as 1 or as large as 10610^6106, make sure your solution handles these cases without crashing or taking excessive time.
• Consider Special Values: Problems with values like zero, negative numbers, or very large numbers can have unique properties that need special handling.
• Look for Unusual Input Patterns: Input patterns like sorted data, repeated values, or input symmetry can sometimes affect how your algorithm behaves. Use these patterns to simplify your solution when possible.

Checking for edge cases before coding can prevent runtime errors and ensure that your solution meets the problem requirements in all scenarios.

7. Use Constraints to Simplify the Problem

Constraints often give hints about possible shortcuts or simplifications. For example:

• Constraints on Variable Values: If a problem restricts values to a small range (e.g., numbers between 1 and 100), it may be solvable with techniques like frequency counting or direct indexing.
• Fixed Number of Operations: Some problems limit the number of operations (like exactly two moves or five steps). This often suggests a brute-force approach might be feasible or that certain strategies, like trying all combinations, could work within the constraint.
• Symmetry or Predictable Patterns: When constraints suggest a pattern, use it to simplify calculations or reduce the amount of data you need to consider.

By recognizing these simplifications, you can reduce the complexity of your approach, making it easier to implement and faster to execute.

Constraints are powerful tools in problem-solving, offering crucial insights into the solution’s requirements and guiding your approach to achieve optimal performance. By carefully analyzing constraints, you can:

1. Choose the best algorithm based on time complexity limits.
2. Optimize space usage with efficient data storage.
3. Anticipate edge cases to handle all possible inputs.
4. Identify shortcuts or special patterns that can simplify the solution.

Using constraints effectively ensures that your solution is not only correct but also efficient, a critical skill in competitive programming and advanced problem-solving.

The post Working with Problem Constraints: How to Use Constraints for Solution Optimization appeared first on Ioi2012.

1. Thoroughly Read the Problem Statement

The first step to solving any problem is to read the problem statement carefully. It might seem obvious, but it’s easy to misinterpret requirements or miss critical details under pressure. Here’s how to approach this step:

• Identify the Goal: Pinpoint what exactly the problem is asking you to do. Is it about finding the maximum or minimum value, arranging items in a certain way, or identifying patterns? Look for the specific outcome expected by the problem.
• Underline Key Details: Take note of specific conditions or constraints mentioned. These details often contain clues about which approach or algorithm will work best.
• Check the Input and Output Requirements: Identify the format of inputs and the expected output. Understanding these helps in planning how to structure the solution and avoid formatting errors later on.
• Identify Edge Cases: Consider extreme or unusual cases mentioned in the problem. If not explicitly given, think of possible edge cases based on the input constraints.

2. Break Down the Problem Statement into Steps

Once you have a clear understanding of what the problem entails, break it down into smaller steps. This approach helps you tackle complex tasks incrementally and avoid overwhelming yourself.

a. Identify Subtasks or Milestones

Many problems can be broken down into smaller subtasks. Think of these as mini-goals that guide you through the solution. For instance, if the problem requires processing data and then outputting specific results, you could structure your approach as follows:

1. Data Parsing: Identify how to receive and parse input data.
2. Core Computation: Determine the main operation, such as searching, sorting, or iterating over elements.
3. Output Formatting: Ensure the results match the output requirements of the problem.

Breaking it down into such tasks will make your approach more organized and reduce potential mistakes.

b. Visualize with Examples

Create your own test cases with small, manageable inputs, and solve these manually. This is a great way to visualize the problem and better understand the transformations needed to reach the correct solution. Working through examples can help in:

• Spotting patterns in the problem.
• Validating the logic you plan to use.
• Identifying any missing steps before you begin coding.

c. Determine Data Structures and Algorithms Needed

Once you’ve broken down the problem into steps, identify the most suitable data structures and algorithms for each part. For example, if the problem involves frequent lookups, a hash map (dictionary in Python) might be efficient. Or if the problem involves sequences in a specific order, a stack or queue could be appropriate.

3. Understand the Constraints and Optimize Accordingly

Constraints play a crucial role in deciding your approach. They hint at the most efficient algorithms to use and help prevent overly complex solutions. For example:

• Time Constraints: Determine the maximum number of operations your solution should perform. A problem with large input constraints may require an O(nlog⁡n)O(n \log n)O(nlogn) or O(n)O(n)O(n) approach, while a smaller input constraint could allow an O(n2)O(n^2)O(n2) solution.
• Memory Constraints: Make sure your data structures won’t exceed the memory limits, especially if the input data is large. If there are constraints on memory usage, consider using in-place operations or avoiding unnecessary lists or arrays.

4. Plan the Solution Approach

Now that you’ve broken down the problem and assessed the constraints, create a rough plan for your solution. This plan doesn’t need to include every detail but should outline the main approach. Consider the following points:

a. Choose the Core Strategy

There are several standard approaches in competitive programming. Depending on the problem, decide whether it calls for:

• Greedy Algorithms: If you can make a sequence of choices that leads to an optimal solution.
• Dynamic Programming: If the problem has overlapping subproblems or can be broken down into smaller, reusable subproblems.
• Divide and Conquer: If the problem can be divided into independent subproblems that can be solved separately.
• Graph Algorithms: If the problem involves navigating between nodes, such as finding shortest paths or detecting cycles.

b. Outline the Flow of the Solution

Outline the sequence of steps your code will follow, from receiving the input to producing the output. This high-level outline serves as a map for coding and can save you from confusion during implementation.

5. Anticipate Edge Cases and Plan How to Handle Them

Edge cases are special conditions that might cause your solution to fail if not handled properly. Identifying edge cases in advance allows you to build safeguards into your code, ensuring it handles all possible scenarios. Some common edge cases to watch for are:

• Empty Inputs: How does your solution behave with an empty input? Does it gracefully handle cases with no data?
• Minimum and Maximum Values: If the problem involves numerical values, consider the smallest and largest possible inputs and outputs.
• Duplicate or Repeated Values: If the problem allows repeated values, check if they might affect the outcome.
• Boundary Cases: Always consider inputs at the boundaries, such as the smallest or largest allowed size of an array or string.

6. Review and Verify Your Plan Before Coding

Take a moment to review your approach. Mentally walk through each step of your solution, ensuring that each part is necessary and efficient. Reviewing your plan allows you to spot potential errors or inefficiencies before you start coding, saving you time in the long run.

Checklist Before You Start Coding

1. Have You Considered All Requirements? Double-check that your plan addresses every aspect of the problem statement.
2. Do You Have a Solution for Edge Cases? Make sure you have safeguards in place for unusual inputs.
3. Is Your Solution Optimized for the Constraints? Ensure that your approach will complete within the time and memory limits.

Analyzing a problem effectively before coding is crucial to solving complex challenges in competitive programming. By carefully reading the problem statement, breaking down the tasks, and planning an optimized approach, you set yourself up for success. With a clear structure and thought-out solution, you’re ready to code with confidence and tackle a variety of programming problems with greater efficiency and accuracy.

The post How to Analyze a Problem Before Starting to Solve It: What to Look For and How to Break It Down appeared first on Ioi2012.