What is the time complexity of Count Tested Devices After Test Operations?

The optimal solution has a time complexity of O(n).

What data structures are used to solve Count Tested Devices After Test Operations?

This problem uses Array, Simulation, Counting. Understanding these concepts is essential for solving this Easy problem efficiently.

What are the key insights for solving Count Tested Devices After Test Operations?

Insight 1: The order of testing matters. Decrementing battery levels after testing a device impacts the testability of later devices. Insight 2: We don't need to explicitly decrement battery percentages. The number of tested devices is solely determined by comparing the current device's battery percentage to the already tested devices count.

Which companies ask LeetCode 2960. Count Tested Devices After Test Operations in interviews?

This problem is frequently asked at Accenture.

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Count Tested Devices After Test Operations - LeetCode 2960 Solution

Count Tested Devices After Test Operations - Complete Solution Guide

Count Tested Devices After Test Operations is LeetCode problem 2960, a Easy level challenge. This complete guide provides step-by-step explanations, multiple solution approaches, and optimized code in python3, java, cpp, c.

Problem Statement

You are given a 0-indexed integer array batteryPercentages having length n , denoting the battery percentages of n 0-indexed devices. Your task is to test each device i in order from 0 to n - 1 , by performing the following test operations: If batteryPercentages[i] is greater than 0 : Increment the count of tested devices. Decrease the battery percentage of all devices with indices j in the range [i + 1, n - 1] by 1 , ensuring their battery percentage never goes below 0 , i.e, batteryPercentages

Detailed Explanation

The problem asks you to simulate a device testing process. You have an array `batteryPercentages` representing the initial battery levels of several devices. You test the devices one by one. If a device has a battery percentage greater than 0, you increment a counter of tested devices and decrement the battery percentage of all subsequent devices by 1 (ensuring percentages never drop below 0). The goal is to find the total number of devices that are successfully tested.

Solution Approach

The efficient solutions (Python3, Java, C++) utilize a single loop to iterate through the `batteryPercentages` array. They maintain a `testedDevicesCount` variable. For each device, it checks if its battery percentage is greater than `testedDevicesCount`. If true, it means the device can be tested, so `testedDevicesCount` is incremented. This cleverly avoids the need for the inner loop to decrement subsequent battery percentages, as that step is implicitly handled by the comparison.

Step-by-Step Algorithm

Step 1: Initialize `testedDevicesCount` to 0.
Step 2: Iterate through the `batteryPercentages` array.
Step 3: For each device's battery percentage, check if it's greater than `testedDevicesCount`.
Step 4: If it is, increment `testedDevicesCount` (this device is tested).
Step 5: Continue to the next device.
Step 6: After iterating through all devices, return `testedDevicesCount`.

Key Insights

Insight 1: The order of testing matters. Decrementing battery levels after testing a device impacts the testability of later devices.
Insight 2: We don't need to explicitly decrement battery percentages. The number of tested devices is solely determined by comparing the current device's battery percentage to the already tested devices count.
Insight 3: The provided Python3, Java, and C++ solutions are optimized. They avoid unnecessary computations by directly comparing the battery level with the already tested count.

Complexity Analysis

Time Complexity: O(n)

Space Complexity: O(1)

Topics

This problem involves: Array, Simulation, Counting.

Companies

Asked at: Accenture.

Count Tested Devices After Test Operations | LeetCode 2960

Easy

Array Simulation Counting

Time: O(n)Space: O(1)

Solution Code

Available in:

class Solution {
    public int countTestedDevices(int[] batteryPercentages) {
        int testedDevicesCount = 0;
        for (int battery : batteryPercentages) {
            if (battery > testedDevicesCount) {
                testedDevicesCount++;
            }
        }
        return testedDevicesCount;
    }
}

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Problem Info

Description

Problem 2960: Count Tested Devices After Test Operations - Detailed explanation and solution approaches.

Topics

Array Simulation Counting

Companies

Accenture

Stats

Difficulty:Easy

Languages:4

Companies:1

Problem #:2960

Problem Explanation

💡

Think Before You Code

🤔 Ask Yourself

→What is the input? What output do I need?
→Can I solve a smaller version of this problem first?
→What patterns have I seen in similar problems?

✏️ Try This First

Before looking at the solution, try to solve it on paper with a simple example!

🚀 Try on LeetCode

Solution Approach

📋

Step-by-Step Solution

6 steps

1
Step 1: Initialize `testedDevicesCount` to 0.
2
Step 2: Iterate through the `batteryPercentages` array.
3
Step 3: For each device's battery percentage, check if it's greater than `testedDevicesCount`.
4
Step 4: If it is, increment `testedDevicesCount` (this device is tested).
5
Step 5: Continue to the next device.
6
Step 6: After iterating through all devices, return `testedDevicesCount`.

💡

Pro Tip for Students

Try tracing through each step with a small example on paper before coding. Understanding "why" each step happens is more important than memorizing the code!

⚡

Complexity Analysis

⏱️

Time Complexity

O(n)

👍 Linear - Good! Visits each element once.

💾

Space Complexity

O(1)

💪 Constant space - Uses same memory regardless of input!

📝

Why?

The solutions iterate through the array once, performing a constant-time comparison in each iteration. No extra space proportional to the input size is used; only a few integer variables are needed.

📖New to Big O? Click to learn more

Big O Notation - Quick Guide

O(1)Best - Instant

O(log n)Excellent - Binary Search

O(n)Good - One loop

O(n log n)OK - Sorting

O(n²)Slow - Nested loops

O(2ⁿ)Very Slow - Recursion

Alternative Approaches

Naive Approach

This approach explicitly simulates the decrementing of battery percentages for all subsequent devices after a successful test. This involves a nested loop.

Time: O(n^2)Space: O(1)

Trade-offs: Less efficient than the optimized approach. The nested loop makes it significantly slower for larger input arrays. The optimized approach is preferred for its superior time complexity.

✨

Key Insights

1Insight 1: The order of testing matters. Decrementing battery levels after testing a device impacts the testability of later devices.
2Insight 2: We don't need to explicitly decrement battery percentages. The number of tested devices is solely determined by comparing the current device's battery percentage to the already tested devices count.
3Insight 3: The provided Python3, Java, and C++ solutions are optimized. They avoid unnecessary computations by directly comparing the battery level with the already tested count.

📚

Key Terms Explained

For Students

Array

A collection of items stored at contiguous memory locations. Like a row of boxes.

Edge Cases

Edge case 1: All battery percentages are 0. The solution correctly returns 0 tested devices.
Edge case 2: All battery percentages are greater than 0. The solution correctly returns the total number of devices.

⚠️

Common Mistakes to Avoid

✗Common mistake 1: Incorrectly handling the decrementing of battery percentages. Failing to ensure percentages don't go below 0 can lead to incorrect results.

✗Common mistake 2: Misunderstanding the order of operations. The testing process must strictly follow the order given in the problem description.

✅

Learning Tip

Every mistake is a learning opportunity! When you make an error, understand why it happened before moving on.

🌍

Where is This Used in Real Life?

💼Real-world scenario 1: Simulating resource consumption in a system where resources are shared and depleted over time (e.g., network bandwidth allocation, power distribution).

🏦Real-world scenario 2: Modeling a sequential process with dependencies where the availability of a resource or component affects subsequent steps (e.g., manufacturing, software testing).

💡Understanding real-world applications helps you see the "why" behind algorithms. It's not just about passing interviews—these concepts power the apps and websites you use every day!

Author's Notes

💡 Key Insight

When dealing with arrays, always consider if sorting would simplify the problem. Many array problems have O(n) solutions using hash maps or two-pointer technique.

🎯 Interview Tip

Don't rush. Explain your thought process clearly. Easy problems are often about communication.

⚠️ Watch Out

Off-by-one errors are the most common mistake. Double-check your loop bounds and array indices.

👨‍💻 From Experience

This is one of the first problems I solved when learning Array. It's a great foundation for more complex problems.

Written by Saswata Mondal

Software Engineer • Algorithms Enthusiast • Interview Coach

👨‍💻

About the Author

👨‍💻

Saswata MondalAuthor400+ LC Problems

B.Tech CSE, IEM Kolkata | LeetCode Rating: 1672 (Top 15%)

Certified: IBM Java Developer, NPTEL Programming in Java

Last updated: August 20, 2024LeetCode Profile GitHub

This solution has been crafted based on my experience solving 400+ LeetCode problems. Each explanation is designed to help you understand the underlying concepts, not just memorize the code.

Community Tips & Insights

Don't forget to handle the edge case when the input is empty. Many candidates miss this in interviews.

@DevExpert92Jul 14

The key insight here is recognizing the pattern. Once you see it, the solution becomes straightforward.

@AlgoMasterJul 7

❓

Frequently Asked Questions

What is the most efficient way to solve array problems?

Most array problems can be solved efficiently using techniques like two-pointer approach, sliding window, or hash maps. The key is identifying whether the array is sorted (enabling binary search) or if tracking seen elements (using hash maps) can help achieve O(n) time complexity.

How should I approach easy-level algorithm problems?

Easy problems build foundational skills. Focus on: 1) Understanding the problem completely before coding, 2) Writing clean, readable code, 3) Handling all edge cases (empty input, single element, etc.), 4) Analyzing time and space complexity. Don't skip complexity analysis even for simple solutions.

How should I practice algorithm problems effectively?

Focus on understanding patterns rather than memorizing solutions. After solving a problem, review optimal solutions and understand the intuition. Group similar problems to recognize patterns. Practice explaining your approach out loud. Review problems after a few days to test retention.

What is the optimal approach for "Count Tested Devices After Test Operations"?

For this easy-level Array problem, the key is to identify the core pattern. Analyze the input constraints to determine acceptable time complexity. Consider whether simulation techniques can optimize the solution. Always handle edge cases and validate your approach with examples before coding.

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