Disruption of Traditional Software Engineering: The Dawn of Intelligent Engineering
To maintain competitiveness, every company needs to behave like a modern software engineering company and deliver intelligent software with high velocity, quality, and reliability.
New features need to be introduced rapidly to meet these challenges using modern software engineering and software engineering platforms to meet higher quality expectations, faster deployment releases, simplifying operations, reducing debugging and traceability issues, challenges in production, mitigating security risks and scaling uptime and reliability needs.
In short, traditional software engineering methods are changing radically. Some trends and innovations that are causing this disruption are:
Cloud is eating software: This has changed not only the infrastructure where software is hosted but how applications are architected, developed, packaged, and deployed. Cloud is driving the adoption of cloud-native technologies. Serverless components and containerization have changed how we build applications. As a result, the line between development and infrastructure teams is getting fuzzy.
Cybersecurity is now a boardroom concern: Rising cyber and malware attacks have left everyone vulnerable. Traditionally, software was released every few months, giving enough time for security testing by specialists. However, with very rapid release cycles today, security checks must start early in the development process.
AI/ML is the new electricity, but it is causing shocks: AI/ML-powered use cases are finding their way across platforms across industries. Traditional software engineers are new to AI/ML, and ML developers are not skilled with engineering disciplines and best practices, leaving a big divide in engineering maturity.
When one company raises the bar, others are expected to follow: Consumer expectations are constantly changing, and they expect from every application the same experience they get from Amazon, Uber, and Google. You are expected to provide a seamless, frictionless experience—your platform is available anytime, on any device, and is fast and secure.
API fueling innovation: Innovation at speed and scale is not easy, but APIs (such as Azure DevOps api) can enable that by tapping into the collective power of the crowd.
Today, any developer can use platform-provided APIs (such as Azure DevOps api) to build a new innovative solution. API-first mindset is emerging. The App Store model (e.g., Apple App Store, Salesforce AppExchange, AWS Marketplace) has changed our view of innovation and new platform extensions.
Low code, if not no code: Such platforms quickly evolve, making it easy to develop small, situational applications without writing code. Citizen developers are emerging.
Modern architecture paradigms drive new working methods: Modern software engineering software platforms and architecture has evolved significantly in recent years from a monolithic application to SOA to microservices API-based distributed platforms. Event-driven architectures have gained prominence, and streaming architecture has become increasingly popular for supporting massive scalability.
It significantly impacts how engineering teams are organized and how they build, test, deploy and manage new platforms.
Legacy software engineering platforms are slowing down growth: Legacy applications, built 20+ years ago, need to be modernized as per today’s context.
Modernization or automating software development is a massive exercise depending on age, technology, and application size. But modernization is easier said than done. The legacy and new worlds need to co-exist for a while and need a bridge strategy from a technology and culture perspective. Software engineering methods needs to become more “intelligent” to meet these changing demands.
The Advent of Intelligent Engineering
Ness can bring intelligence to engineering by using more data to manage projects, leveraging AI/ML analytics to improve engineering productivity and predictability, enable software engineering automation, and apply the right process to the right problem.
Data-driven decision-making in engineering
Modern DevOps software engineering teams use numerous tools in the development environment and software engineering automation for code repository, bug tracking, code scanning, build, etc.
Popular tools include Azure DevOps, GitHub, Atlassian tools, and Jenkins, among others. These tools generate a lot of granular data, and all these data silos can be aggregated and used to track interesting metrics around:
Developer productivity: How much time is spent on writing how much code vs. other activity and story points delivered vs. committed
Software quality: Number of bugs generated, the rate at which bugs are fixed, and bugs that slip into production
Additionally, on top of this data, one can build an intelligence layer using ML to draw hidden insights, highlight co-relations, and conduct better root cause analysis. These insights can bring greater predictability to engineering outcomes. E.g., if you will meet your release schedule, will you meet your release schedule, level of quality, etc.
Data provide evidence of how your teams are performing. It helps in removing potential roadblocks and better communication with all stakeholders. It can be used to manage project performance better and leveraged for objective decision making.
Despite this, engineering leaders are operating in the dark. Decisions are made based on intuition and gutfeel rather than data. Hence, it is time to become more intelligent by using more data in project performance tracking and decision making.
Elevate productivity and predictability of engineering services with AI
The question, what is automation in software development? is well answered by Artificial Intelligence and Machine Learning. AI/ML is disrupting every industry and is now finding its way into modern software engineering. AI/ML-enabled developer tools are emerging and can be leveraged across the modern software engineering lifecycle.
These tools can save time for repetitive activities and improve quality in large and complex tasks. AI/ML-enabled tools can enhance developer’s daily life, from coding to testing. AI-powered tools can scan and analyze code to provide intelligent application and code completion suggestions, flag deviation from coding best practices (naming convention compliance, variable misuse, etc.), perform peer review, convert code from one language to another, find security vulnerabilities, etc.
Dynamic Application Security Testing (DAST) solutions use AI to discover potential attack vectors in milliseconds, which would otherwise take a few days. AI/ML can help auto-generate HTML code from hand-drawn UI sketches. AI/ML can help developers automatically generate unit tests from existing code and provide suggestions about improving tests.
Testing is the biggest category that can benefit from AI/ML-based intelligence, specifically in large complex applications. QA teams who rely on manual testing cannot keep pace with the rate at which code changes and releases are done today.
AI/ML-enabled tools can help testing teams across various dimensions:
Change impact analysis: AI/ML can help with coverage analysis and identify which tests need to run based on what has changed in the application.
Test creation: AI/ML can help test teams create test cases from plain English descriptions and learn how to improve tests and automatically heal broken tests over time. AI-powered tools can automatically convert manual UI tests to API tests.
Visual testing: AI-powered image comparison technology can enable visual testing to analyze the UI screen differences detected across tests.
Test analysis: AI can analyze test cases and defect metrics to increase test coverage while reducing the number of tests.
It is time to become more intelligent. Use AI-powered tools to augment your engineering teams, improve their productivity, and bring predictability.
Expanding software engineering automation beyond development processes
Today, there are many options for automating software development. However, there is always this lingering question, is devops software engineering? Here is the answer.
DevOps software development methods enable automation of the build, test, and deployment of new versions of your software. You can also automate other activities, such as test automation, code scanning, performance test, etc. DevSecOps automates security processes (e.g., code scanning for vulnerabilities) in the engineering lifecycle.
AI/ML-powered tools can further drive automation improvements—e.g., improving flow in your CI/CD pipelines. Overall, automation enables higher team productivity and results in rapid or on-demand release cycles required for business agility.
Devops software development automation also applies to other parts of the engineering lifecycle. For example:
Infrastructure-as-Code tools can automatically provision and configure cloud infrastructure environments (storage, network, etc.).
Monitoring tools can automate monitoring for availability, production load metrics, and security problems and generate alerts.
Log management—the number of logs generated in today’s ecosystem is vast, and it is impractical to collect them manually. Log management tools can automatically aggregate and analyze.
Let’s get more intelligent by aggressively leveraging automation throughout the engineering lifecycle to automate repetitive tasks and low-value processes and utilizing the saved time for higher-value activities.
Agile for ideation
Several popular software engineering processes are available, such as Agile, Scaled Agile, Spotify model, etc. Regardless of your process, you can improve them with the intelligent application of the right methods and tools to the right problem. For example, traditional agile processes were designed to develop mature software. But in today’s fast-changing market, development teams need to rapidly and continuously try new ideas to demonstrate new business features or perform technical validation of emerging technology. It is true not only in the early stages of new product development but also on existing platforms, where you need to keep pace with changing market dynamics.
If the software is eating the world, complexity is eating software!
While product engineering has been around for a long time, “modern” software engineering is much more complex than just writing code and following agile processes.
Follow Intelligent Engineering practices, so you can get solutions to market faster, with better quality and reliability, and gain more time for innovation to keep up with today’s speed of business.