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“Cloud vs. Colocation Data Centers” — Which Solution Fits Your Business Needs? - Related to which, super.super.method(), colocation, your, calling

“Cloud vs. Colocation Data Centers” — Which Solution Fits Your Business Needs?

“Cloud vs. Colocation Data Centers” — Which Solution Fits Your Business Needs?

Choosing between Cloud and Colocation depends on your business needs, infrastructure preferences, and performance expectations. While cloud services offer scalability and flexibility through virtualised environments, colocation provides greater control over hardware with dedicated, low-latency connectivity. Understanding these differences is crucial for making an informed IT strategy.

Read the latest Blog by Sify Technologies’ to explore the benefits of each approach and determine the best fit for your business: Cloud vs. Colocation: Key Differences.

CloudComputing #Colocation #DataCenters #SifyTechnologies #ITInfrastructure #BusinessGrowth.

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Selenium Datepicker Example

Selenium Datepicker Example

Interacting with date pickers in web applications can be challenging due to their different implementations. Some date pickers allow direct text input, while others require selecting a date from a dropdown calendar. In this article, we will use Selenium WebDriver and JUnit to automate the selection of a date from the “Date of Birth” field on DemoQA’s Automation Practice Form.

Add the following dependencies to your [website] file:

org.seleniumhq.selenium selenium-java [website] [website] junit-jupiter-api [website] test [website] junit-jupiter-engine [website] [website] webdrivermanager [website].

These dependencies set up Selenium WebDriver for browser automation, JUnit 5 for testing, and WebDriverManager for automatic driver management. selenium-java provides the core API, while junit-jupiter-api and junit-jupiter-engine enable writing and running tests. WebDriverManager eliminates manual driver downloads, ensuring compatibility with the latest browser versions.

Now, let’s write the JUnit test to open the DemoQA page and select a date from the date picker.

To select the year (1995), the script locates the year dropdown using the XPath:

WebElement yearDropdown = driver.findElement([website]"//select[@class='react-datepicker__year-select']"));

Similarly, the month dropdown is located using XPath and the required month (December) is selected.

Finally, the script selects the 10th day of the month using XPath with contains() to make it more dynamic and resistant to changes:

WebElement day = driver.findElement([website]"//div[contains(@class,'react-datepicker__day') and text()='10']")); [website];

After selecting the date, the test retrieves the value of the date input field using getAttribute("value") and verifies that it matches “10 Dec 1995”. If the selected date does not match, the test will fail.

Once the test completes, the tearDown() method ensures that the browser closes, preventing unnecessary resource usage.

In this article, we explored how to automate date selection from a Selenium datepicker using XPath in a JUnit test. We interacted with the datepicker UI by selecting the year, month, and day dynamically, ensuring accurate date selection. The test verified the chosen date by retrieving the input field’s value. By leveraging XPath locators, we created a flexible test that mimics real user interaction. This approach is particularly useful for handling complex web elements and improving test reliability.

This was a guide on automating Selenium datepicker selection for form testing using JUnit and WebDriver.

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Why Calling super.super.method() is Not Allowed in Java

Why Calling super.super.method() is Not Allowed in Java

Why Calling [website] is Not Allowed in Java.

Java follows a strict object-oriented approach with a well-defined inheritance model. Unlike some languages that allow direct access to grandparent methods, Java restricts access to only the immediate superclass using super . Let us delve into understanding why Java super super not allowed is a restriction in the language.

In Java, the super keyword allows a subclass to access methods of its direct superclass. It allows access to:

Superclass methods that are overridden in a subclass.

However, developers sometimes expect to call methods of the grandparent class (two levels up) directly using [website] , but Java does not allow this.

Java enforces encapsulation and method resolution through a single-level inheritance mechanism. Directly calling a grandparent’s method would break this encapsulation and can lead to ambiguity in method resolution.

class Grandparent { void show() { [website]"Grandparent method"); } } class Parent extends Grandparent { void show() { [website]"Parent method"); } } class Child extends Parent { void display() { // Attempting [website] is NOT ALLOWED // [website]; // This will cause a compilation error } }.

The above example attempts to call [website] but fails to compile because Java does not allow skipping levels in inheritance.

In the above example, Java programming language prevents [website] because:

Violation of Encapsulation: Java enforces encapsulation by ensuring that a subclass interacts with its immediate superclass without direct access to deeper levels of the inheritance chain. Allowing [website] would expose internal implementation details of the class hierarchy, making it harder to refactor or modify base classes without affecting subclasses. In the following code, class C should only interact with B , but if [website] were allowed, it would break encapsulation and tightly couple C to A , violating abstraction principles. class A { void show() { [website]"Class A method"); } } class B extends A { @Override void show() { [website]"Class B method"); } } class C extends B { @Override void show() { [website]"Class C method"); [website]; // Calls B's method // [website]; // Invalid in Java - would break encapsulation } } public class Main { public static void main(String[] args) { C obj = new C(); [website]; } }.

would expose internal implementation details of the class hierarchy, making it harder to refactor or modify base classes without affecting subclasses. Ambiguity with Multiple Inheritance via Interfaces: While Java does not support multiple inheritance with classes, it allows a class to implement multiple interfaces. If Java permitted [website] , resolving method calls from multiple interfaces could lead to conflicts, especially when different interfaces provide default methods with the same name. In the example below, interfaces X and Y both define a show() method. If [website] were allowed, it would be unclear which implementation should be invoked. interface X { default void show() { [website]"Interface X"); } } interface Y { default void show() { [website]"Interface Y"); } } class Z implements X, Y { @Override public void show() { // Conflict! Java does not allow automatic resolution of super calls from multiple interfaces. // [website]; // Invalid in Java [website]; // Must explicitly specify which interface to call } } public class Main { public static void main(String[] args) { Z obj = new Z(); [website]; // Calls X's implementation explicitly } }.

, resolving method calls from multiple interfaces could lead to conflicts, especially when different interfaces provide default methods with the same name. Dynamic Method Resolution (Polymorphism) via Virtual Method Table (VMT): Java uses a mechanism called the Virtual Method Table (VMT) to resolve method calls dynamically at runtime. This ensures that the most specific overridden method in the hierarchy is executed. If [website] were allowed, it would bypass this mechanism, leading to unexpected behavior and breaking polymorphism. In the example below, Java ensures that method resolution happens dynamically. If [website] were allowed, it would force execution of a grandparent’s method, breaking the expected behavior. class Parent { void display() { [website]"Parent class"); } } class Child extends Parent { @Override void display() { [website]"Child class"); } } class GrandChild extends Child { @Override void display() { [website]"GrandChild class"); super.display(); // Calls Child's method // [website]; // Invalid in Java - would break VMT resolution } } public class Main { public static void main(String[] args) { Parent obj = new GrandChild(); obj.display(); // Calls the most overridden method in GrandChild due to VMT } }.

Though [website] is not allowed, we can explicitly call the grandparent’s method by defining a public method in the parent class that exposes it.

// Base class (Grandparent) with a method 'show' class Grandparent { void show() { [website]"Grandparent method"); } } // Intermediate class (Parent) that overrides 'show' from Grandparent class Parent extends Grandparent { void show() { [website]"Parent method"); } // Method to explicitly call Grandparent's 'show' using 'super' void callGrandparentShow() { [website]; // Calls Grandparent's show() method } } // Child class that extends Parent class Child extends Parent { void display() { // Indirectly calls Grandparent's show() through Parent's callGrandparentShow() callGrandparentShow(); } } // Test class with main method public class Test { public static void main(String[] args) { Child c = new Child(); c.display(); } }.

In the given Java code, we have a class hierarchy demonstrating method overriding and the use of super to access a grandparent class method.

The Grandparent class has a method show() that prints “Grandparent method”. The Parent class extends Grandparent and overrides the show() method to print “Parent method”. Additionally, it has a method callGrandparentShow() , which explicitly calls the show() method from Grandparent using [website] .

The Child class extends Parent and defines a method display() , which calls callGrandparentShow() . This ensures that the show() method from Grandparent is invoked, bypassing the overridden show() method in Parent .

In the main method, an instance of Child is created, and the display() method is called. This triggers a chain of method calls: display() → callGrandparentShow() → [website] , resulting in “Grandparent method” being printed.

This code illustrates the concept of method overriding and how super can be used to access methods from grandparent classes in a multilevel inheritance structure.

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Market Impact Analysis

Market Growth Trend

2018201920202021202220232024
7.5%9.0%9.4%10.5%11.0%11.4%11.5%
7.5%9.0%9.4%10.5%11.0%11.4%11.5% 2018201920202021202220232024

Quarterly Growth Rate

Q1 2024 Q2 2024 Q3 2024 Q4 2024
10.8% 11.1% 11.3% 11.5%
10.8% Q1 11.1% Q2 11.3% Q3 11.5% Q4

Market Segments and Growth Drivers

Segment Market Share Growth Rate
Enterprise Software38%10.8%
Cloud Services31%17.5%
Developer Tools14%9.3%
Security Software12%13.2%
Other Software5%7.5%
Enterprise Software38.0%Cloud Services31.0%Developer Tools14.0%Security Software12.0%Other Software5.0%

Technology Maturity Curve

Different technologies within the ecosystem are at varying stages of maturity:

Innovation Trigger Peak of Inflated Expectations Trough of Disillusionment Slope of Enlightenment Plateau of Productivity AI/ML Blockchain VR/AR Cloud Mobile

Competitive Landscape Analysis

Company Market Share
Microsoft22.6%
Oracle14.8%
SAP12.5%
Salesforce9.7%
Adobe8.3%

Future Outlook and Predictions

The Super Cloud Colocation landscape is evolving rapidly, driven by technological advancements, changing threat vectors, and shifting business requirements. Based on current trends and expert analyses, we can anticipate several significant developments across different time horizons:

Year-by-Year Technology Evolution

Based on current trajectory and expert analyses, we can project the following development timeline:

2024Early adopters begin implementing specialized solutions with measurable results
2025Industry standards emerging to facilitate broader adoption and integration
2026Mainstream adoption begins as technical barriers are addressed
2027Integration with adjacent technologies creates new capabilities
2028Business models transform as capabilities mature
2029Technology becomes embedded in core infrastructure and processes
2030New paradigms emerge as the technology reaches full maturity

Technology Maturity Curve

Different technologies within the ecosystem are at varying stages of maturity, influencing adoption timelines and investment priorities:

Time / Development Stage Adoption / Maturity Innovation Early Adoption Growth Maturity Decline/Legacy Emerging Tech Current Focus Established Tech Mature Solutions (Interactive diagram available in full report)

Innovation Trigger

  • Generative AI for specialized domains
  • Blockchain for supply chain verification

Peak of Inflated Expectations

  • Digital twins for business processes
  • Quantum-resistant cryptography

Trough of Disillusionment

  • Consumer AR/VR applications
  • General-purpose blockchain

Slope of Enlightenment

  • AI-driven analytics
  • Edge computing

Plateau of Productivity

  • Cloud infrastructure
  • Mobile applications

Technology Evolution Timeline

1-2 Years
  • Technology adoption accelerating across industries
  • digital transformation initiatives becoming mainstream
3-5 Years
  • Significant transformation of business processes through advanced technologies
  • new digital business models emerging
5+ Years
  • Fundamental shifts in how technology integrates with business and society
  • emergence of new technology paradigms

Expert Perspectives

Leading experts in the software dev sector provide diverse perspectives on how the landscape will evolve over the coming years:

"Technology transformation will continue to accelerate, creating both challenges and opportunities."

— Industry Expert

"Organizations must balance innovation with practical implementation to achieve meaningful results."

— Technology Analyst

"The most successful adopters will focus on business outcomes rather than technology for its own sake."

— Research Director

Areas of Expert Consensus

  • Acceleration of Innovation: The pace of technological evolution will continue to increase
  • Practical Integration: Focus will shift from proof-of-concept to operational deployment
  • Human-Technology Partnership: Most effective implementations will optimize human-machine collaboration
  • Regulatory Influence: Regulatory frameworks will increasingly shape technology development

Short-Term Outlook (1-2 Years)

In the immediate future, organizations will focus on implementing and optimizing currently available technologies to address pressing software dev challenges:

  • Technology adoption accelerating across industries
  • digital transformation initiatives becoming mainstream

These developments will be characterized by incremental improvements to existing frameworks rather than revolutionary changes, with emphasis on practical deployment and measurable outcomes.

Mid-Term Outlook (3-5 Years)

As technologies mature and organizations adapt, more substantial transformations will emerge in how security is approached and implemented:

  • Significant transformation of business processes through advanced technologies
  • new digital business models emerging

This period will see significant changes in security architecture and operational models, with increasing automation and integration between previously siloed security functions. Organizations will shift from reactive to proactive security postures.

Long-Term Outlook (5+ Years)

Looking further ahead, more fundamental shifts will reshape how cybersecurity is conceptualized and implemented across digital ecosystems:

  • Fundamental shifts in how technology integrates with business and society
  • emergence of new technology paradigms

These long-term developments will likely require significant technical breakthroughs, new regulatory frameworks, and evolution in how organizations approach security as a fundamental business function rather than a technical discipline.

Key Risk Factors and Uncertainties

Several critical factors could significantly impact the trajectory of software dev evolution:

Technical debt accumulation
Security integration challenges
Maintaining code quality

Organizations should monitor these factors closely and develop contingency strategies to mitigate potential negative impacts on technology implementation timelines.

Alternative Future Scenarios

The evolution of technology can follow different paths depending on various factors including regulatory developments, investment trends, technological breakthroughs, and market adoption. We analyze three potential scenarios:

Optimistic Scenario

Rapid adoption of advanced technologies with significant business impact

Key Drivers: Supportive regulatory environment, significant research breakthroughs, strong market incentives, and rapid user adoption.

Probability: 25-30%

Base Case Scenario

Measured implementation with incremental improvements

Key Drivers: Balanced regulatory approach, steady technological progress, and selective implementation based on clear ROI.

Probability: 50-60%

Conservative Scenario

Technical and organizational barriers limiting effective adoption

Key Drivers: Restrictive regulations, technical limitations, implementation challenges, and risk-averse organizational cultures.

Probability: 15-20%

Scenario Comparison Matrix

FactorOptimisticBase CaseConservative
Implementation TimelineAcceleratedSteadyDelayed
Market AdoptionWidespreadSelectiveLimited
Technology EvolutionRapidProgressiveIncremental
Regulatory EnvironmentSupportiveBalancedRestrictive
Business ImpactTransformativeSignificantModest

Transformational Impact

Technology becoming increasingly embedded in all aspects of business operations. This evolution will necessitate significant changes in organizational structures, talent development, and strategic planning processes.

The convergence of multiple technological trends—including artificial intelligence, quantum computing, and ubiquitous connectivity—will create both unprecedented security challenges and innovative defensive capabilities.

Implementation Challenges

Technical complexity and organizational readiness remain key challenges. Organizations will need to develop comprehensive change management strategies to successfully navigate these transitions.

Regulatory uncertainty, particularly around emerging technologies like AI in security applications, will require flexible security architectures that can adapt to evolving compliance requirements.

Key Innovations to Watch

Artificial intelligence, distributed systems, and automation technologies leading innovation. Organizations should monitor these developments closely to maintain competitive advantages and effective security postures.

Strategic investments in research partnerships, technology pilots, and talent development will position forward-thinking organizations to leverage these innovations early in their development cycle.

Technical Glossary

Key technical terms and definitions to help understand the technologies discussed in this article.

Understanding the following technical concepts is essential for grasping the full implications of the security threats and defensive measures discussed in this article. These definitions provide context for both technical and non-technical readers.

Filter by difficulty:

interface intermediate

algorithm Well-designed interfaces abstract underlying complexity while providing clearly defined methods for interaction between different system components.

API beginner

interface APIs serve as the connective tissue in modern software architectures, enabling different applications and services to communicate and share data according to defined protocols and data formats.
API concept visualizationHow APIs enable communication between different software systems
Example: Cloud service providers like AWS, Google Cloud, and Azure offer extensive APIs that allow organizations to programmatically provision and manage infrastructure and services.

platform intermediate

platform Platforms provide standardized environments that reduce development complexity and enable ecosystem growth through shared functionality and integration capabilities.

scalability intermediate

encryption

framework intermediate

API

agile intermediate

cloud computing