Last Updated on June 14, 2024

Traditional software development, with its rigid phases and waterfall-like processes, borrows heavily from the world of physical construction and caused what is known as software crisis. But unlike bricks and mortar , software is intangible and constantly evolving, making rigid planning and sequential phases less effective.

In addition to this, growth in software complexity, inadequate testing and quality control, lack of communication and collaboration,  led to what is known as a “software crisis” – a challenge to produce a quality software system in a defined timeline.

Before the rise of agile methodologies in the 1990s, software development primarily relied on the waterfall approach, also known as structured development. While waterfall contributed significantly to software development practices, it faced limitations that fueled the emergence of agile methods.

Frustrations with waterfall’s rigidness and inflexibility led to the development of more iterative and collaborative approaches.

Agile Manifesto

This approach was summarized in The Agile Manifesto that emerged from a collaboration of 17 software developers who gathered at the Snowbird Ski and Summer Resort in Utah in February 2001. These individuals represented various software development methodologies and practices, coming together to find common ground and define a new direction for software development.

Several agile methodologies, such as Scrum, Kanban, and Extreme Programming, emerged, emphasizing:

  • Iterative development: Delivering working software in short cycles with continuous feedback loops.
  • Focus on user needs: Involving users throughout the process to ensure alignment with their needs.
  • Collaboration and adaptation: Fostering team communication and adapting to changing requirements.
  • Continuous improvement: Reflecting and refining the process through regular retrospectives.

Scrum is a popular framework within the agile methodology that emphasizes short iterative development cycles, collaboration, and continuous improvement. It provides a structured approach to managing teams and tasks, offering a clear roadmap for delivering working software in small increments.

Kanban is another popular framework within the agile methodology, offering a flexible and visual approach to managing work. Unlike Scrum’s structured sprints, Kanban focuses on continuous flow and prioritized tasks, making it particularly suitable for projects with evolving requirements or unpredictable workloads.

While Lean Startup shares some principles with Agile methodology, it’s not considered a framework within Agile itself. Rather, it’s a distinct methodology focused on building successful businesses through validated learning and rapid iteration. This often overlaps with and complements Agile practices, creating a powerful combination for innovative ventures.

While not directly part of Agile, Lean Startup offers valuable tools and principles that can complement and enhance Agile practices, especially for startups and ventures with high uncertainty and dynamic markets.

Agile Engineering Techniques

The rise of the internet in the 1990s demanded faster product life cycles that traditional software development methods were not able to deliver.

Developed by Kent Beck, in his book Extreme Programming Explained describes extreme programming as a software-development discipline that organizes people to produce higher-quality software more productively.

Extreme Programming(XP) introduces a number of basic values, principles and practices on top of the agile methodology.

For example, XP emphasizes four basic activities that are performed within the software development process: coding, testing, listening, and designing. 

Perhaps the most important principles of XP are feedback – seen as most useful when it is frequent and prompt, change – embrace the change and do not work against it.

XP, or Extreme Programming, is a specific agile software development framework known for its prescriptive practices and emphasis on technical excellence. While technically considered part of the agile umbrella, it stands out for its more demanding and rigorous approach compared to other frameworks like Scrum or Kanban.

Pair programming is a core practice in many agile methodologies, particularly Extreme Programming (XP). It involves two developers working together at the same computer station, taking turns at the keyboard and collaborating to write code, review it, and solve problems.

Test-Driven Development (TDD) is a software development practice with strong ties to agile methodologies, particularly Extreme Programming (XP). It emphasizes writing tests before writing the actual code, ensuring thorough functionality and high software quality.

It focuses on writing unit tests before writing the actual code. 

This means defining functionality of a small unit of code by writing a test that fails(red). Then write the minimum amount of code for the test to pass(green). Refactor the code to improve the quality without breaking the existing test.

This process is known as Red-green-Refactor.

DevOps Role

DevOps in Agile bridges the gap between the traditionally separate worlds of software development and operations. While Agile focuses on iterative development, frequent releases, and user feedback, DevOps emphasizes automation, collaboration, and continuous delivery.

Shared Values – Both Agile and DevOps emphasize collaboration, communication, and continuous improvement, creating a unified culture for delivering high-quality software.

Automation in DevOps is a core practice that involves using tools and technologies to eliminate manual tasks and streamline processes throughout the software development and delivery lifecycle. It’s about making things happen automatically, without human intervention, to achieve greater efficiency, speed, reliability, and consistency.

Continuous Integration(CI)

is a crucial practice within the DevOps methodology. It focuses on automating the integration of code changes back into a shared repository as frequently as possible, followed by automated building and testing of those changes. 

CD stands for Continuous Delivery, which is the next step after Continuous Integration (CI). While CI focuses on automating the integration and testing of code changes, CD takes it a step further by automating the delivery of those changes to production environments. By automation the delivery CD increases the frequency and quality of deployment. There are several types of deployment depending on the needs of different platforms:

Blue-Green deployment – beside the production(blue) environment, a new parallel(green) environment is created and traffic is switched to this one. This way downside and risk of failure is minimal, rollback is simple.

Canary deployment – Deploy the new code to a small subset of users (the “canary” group) and other feedback and monitor performance before rolling out to the entire user base. 

Dark launches – Deploy new code to production but keep it hidden from users and other real-world data and performance metrics before full release.

Rolling Deployments – Gradually deploy code changes to subsets of servers or users in a rolling fashion and minimize impact on overall system availability and performance.

A/B Testing – Release two different versions of a feature to distinct user groups and compare performance and user behavior to determine the better version.

Choosing the right release type depends on the organization’s goals, culture, risk tolerance, user base and expectations. 

Infrastructure as Code (IaC)

is a DevOps practice that involves managing and provisioning infrastructure (servers, networks, storage, etc.) using code rather than manual processes. This means defining the desired infrastructure configuration in a text-based file, which can then be used to automatically create, deploy, and manage the infrastructure using software tools.

Monitoring in DevOps plays a crucial role in keeping your software systems healthy and performing optimally. It involves continuously collecting and analyzing data about various aspects of your infrastructure, applications, and services.

In the world of DevOps, observability takes monitoring to the next level. It’s like shifting from watching a single gauge on your car’s dashboard to understanding the entire engine under the hood. While monitoring focuses on predefined metrics and thresholds, observability takes a more holistic approach to understanding how your system behaves.

Role of  Generative AI 

Various engineering techniques and CI/CD within agile approach greatly increased productivity and quality in the light of increased software complexity and faster product life cycles.

Every day demands for automation and productivity are greater and greater.

Given that we are limited in terms of time and people, can the software development methodology productivity be pushed even further?

Generative AI is making significant waves in software development, holding tremendous potential to revolutionize the field. It’s not just about code generation (although that’s a big part of it), but about augmenting and empowering developers in various ways, across the entire software lifecycle. 

AI is impacting software development:

  • Code generation – Translation natural language code translation, assist with complex code challenges and boilerplate generation
  • Assist in quality improvement process – testing, debugging and help in code review
  • Security and optimization – Performance optimization, vulnerability detection and prevention
  • Data driven decision – Use AI to analyze large datasets and derive insights for optimizing various aspects of the software development process. 

While concerns about AI replacing developers are often exaggerated, the reality is far more exciting. AI is actively reshaping the landscape of software development, not to replace human talent, but to empower developers to focus on their unique strengths: creativity, innovation, and tackling the truly challenging aspects like security, performance optimization, and complex integrations. By partnering with AI, developers can break through limitations, unlock new possibilities, and usher in a new era of software development characterized by increased efficiency, agility, and innovation.

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