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:

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.

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:

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:

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:

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.

FAQs

Introduction

One of the common challenges that companies face when moving legacy applications to the cloud is that a simple “lift-and-shift” frequently isn’t an option. The reason why some applications can’t be migrated without modification could be that a workload assumes a specific physical infrastructure that isn’t replicable in a cloud environment.

What is refactoring in cloud migration? To refactor cloud migration is to restructure existing applications and infrastructure for optimal performance, scale, and reliability in a cloud ecosystem. It’s a must to extract the best benefits of cloud computing.

More often, a lift-and-shift doesn’t make sense because the application can’t take advantage of elastic compute or cloud-native offerings. As a result, institutions frequently face the daunting task of a full refactorization of their critical business applications into microservices. While doing so may be beneficial once realized, it can be risky and is almost always expensive.

At Ness, we’re proud of our perfect track record of rebuilding, re-platforming and refactoring applications for the cloud. There’s no secret sauce to our success in cloud refactoring initiatives, refactoring cloud migration or application refactoring for cloud. Instead, we base our practice on the following pillars:

Focused Approach

When moving legacy applications, a common practice is to replicate the existing system’s functionality exactly. Doing so may be unnecessary—applications that are 10+ years old aren’t built frequently to serve the current needs of a business. Instead of forcing a square peg in a round hole, we might be better off focusing on what the application should be doing rather than on what it’s currently doing.

Domain Knowledge

Technology excellence is required but not enough in cloud refactoring, app refactoring or in the refactorization of functionally rich applications. Our senior staff has decades of financial services domain expertise, and we lean heavily on our business analysts throughout the process, beginning with planning and design.

Refactoring applications into microservices or app refactoring requires that the boundaries of services are appropriately defined. These definitions aren’t engineering constructs but are determined at an organizational and business-function level. Domain-Driven Design (DDD) is an approach that can help define these boundaries, but the domain must be well understood to be effective.

Technical Expertise

Over the years, we’ve built critical trading, risk management, and reporting systems for many of the largest banks, hedge funds, and industry utilities. We’re also a handful of AWS Consulting Partners with the AWS Financial Services Competency. We have more AWS Certifications than we have staff in the company (and we are counting the staff inclusive of Operations and Sales). This level of certification guarantees that everyone on a project, even our business analysts, has rigorous technical training.

Traditionally, legacy systems rely on a “shared state” (e.g., using the same database) as an integration point. One of the patterns facilitating an application’s breakup into microservices is a move toward “shared flow” (e.g., using a redo log like Kafka). This approach allows us to develop components in parallel more easily, scale them, and move some into the cloud while leaving others in a data center as needed.

Iterative Process

Whenever we start on a big re-architecture or a refactorization project, our first milestone is to provide a quick working Proof of Value—will the suggested technology and architecture meet the business and technical requirements? The only way to be sure is to get real data.

We follow a disciplined, iterative process with regular progress demos at the end of each sprint. Even if an application can’t go into production at the end of each sprint, the regular demos of functionality and integration points allow for incremental testing. It removes nasty surprises around go-live, provides early feedback, and enables robust test automation.

Cost Efficiency

We work very closely with AWS and can leverage several AWS funding programs. Our AWS partnership has allowed us to cut our clients’ migration costs by up to half.

Author

Vassil Avramov, Chief Technology Officer, Ness Digital Engineering

FAQs

What is Industry 4.0

The fourth industrial revolution, or Industry 4.0, as it is popularly known, was coined in 2011 to signify the future of manufacturing. It is meant to evolve the industry based on the advances bought about by the previous three industrial revolutions. While computers and automation spearheaded the third industrial revolution, Industry 4.0 is driven by the Industrial Internet of Things (IIoT). IIoT can be visualized as a huge network of physical assets fixed with sensors that can connect and exchange data with devices and systems through the internet. Through data exchange, these sensors can detect and respond to changes in the industrial asset, be it light, temperature, motion, pressure, etc. They can warn of potential issues before they become bigger challenges.

What is digital industrial transformation

Digital industrial transformation effectively uses digital technologies to transform industrial processes and move towards Industry 4.0. It is about enhancing manufacturing efficiency and strengthening the business’s growth curve. With the effective fusion of technologies such as cloud computing, IoT, AI-ML, edge computing, and digital twins, it is also cost-efficient, saves time, and reduces errors due to manual intervention. Here are a few characteristics which are indispensable and promising.

• Effective data leverage: It’s more machine-dependent and less human. With physical systems connected with sensors that are integrated into a network, most decisions are automated based on performance data. The data is collected in a data lake & AI-ML models are used to derive insights from this data to enable autonomous decisions, reducing the need for centralized controls and operator interventions.
• Improves operations: Using data, factory staff can make faster decisions and understand equipment availability, performance outcomes, quality, and productivity. They will be able to proactively understand the cause of faults and maintenance requirements and take suitable corrective measures. Through data interoperability, enabled by IoT, companies will be able to share information with their suppliers, vendors, and partners more seamlessly.
• Virtual Instances: Virtual instances of physical assets, systems, and processes can be created. These virtual instances help in monitoring & optimizing production process. It’s a secure virtual replica of the factory. This has many uses such as simulations, running software tests, and testing configurations. It is an ideal way to understand the outcomes without impacting the production on the factory floor. The most common kind of virtual instances are Digital Twins.
• Adaptive production: Enables flexible production systems that adapt to changing market demands, product customization needs, and efficiency requirements. By dividing production lines into smaller modules, that can be added, removed, or reconfigured based on production needs without impacting the assembly line.

How Digital Transformation Technology is changing the Industry

With its characteristics of flexibility, automation, and modularity, the digitalization of industry can widely impact the manufacturing organization. Here are a few value drivers which can help manufacturing companies identify opportunities to optimize their production process.

• Asset optimization: Maximizing machine production time is critical. Capabilities such as predictive maintenance can reduce machine downtime and increase machine life. Digital twins and predictive maintenance offer insights into the performance of physical objects and systems, enabling businesses to foresee any maintenance and repair schedules in advance. This reduces machine costs and increases machine availability.

• Labor productivity: Improves the operating speed of workers by reducing stress in executing complex tasks. Cobots (collaborative and sensitive robots) can be used to work with workers on the floor to ease their workload. They can take up hard, repetitive tasks, as the space required to operate is extremely low. They can share the same workspace without fencing as these cobots are very safe. They are precise and flexible, capable of delivering high-quality products, and versatile in handling tasks from testing to material handling.

• Inventory management: Real-time inventory tracking ensures more accurate inventory level estimates for better purchase, production, and shipping decisions. With the help of AI-ML driven predictive analytics, companies can forecast demand, reducing risks of stockouts or overstocks. Automating tasks such as order fulfillment and stock replenishment can speed up moving the products from the warehouse to the customer.

• Quality: Product quality can be controlled using statistical process controls (SPC) and advanced process controls (APC). SPC involves using statistical methods to monitor and control manufacturing processes with the help of real-time data and analytics. APC uses mathematical models and real-time data to optimize processes, identify quality issues and meet changes in quality demands.

It is a definite win for early adopters of Industry 4.0 as they are more mature to handle crises and competition. However, most firms are still reluctant to embrace modern manufacturing practices. The risk of not investing is huge. They might lose market share to more agile competitors bringing in more technologies such as IoT, analytics, and robotics. Customers get max bang for their buck as they can meet their custom needs and quality standards, all at a reduced cost. Manufacturers must sense the urgency of adopting Industry 4.0 and its technologies by realizing its impact throughout the production processes, including supply chains.

Challenges and Benefits of Industrial Digital Transformation

Let us look at challenges of implementing industrial digital transformation. One big roadblock is that to deal with legacy infrastructure, which can be a real pain. Most factories are still happy with old technology which is not interoperable with digital technologies. Employees might show resistance to adopt new technologies and the change it brings with it. Cybersecurity is another pain which must be dealt with, due to the increase in the volume of data. Cost is another factor as most digital solutions are expensive. Meeting legal and compliance standards can be a challenge as it can spike the transformation costs.

Now, here are the benefits. As we have seen, automation, optimization, and connectivity form the core of industrial digitalization. it empowers stakeholders to evaluate concepts before they are built, optimize output across production lines, and even run real-time simulations of a real factory to reduce downtime time and cost. The following technologies can significantly impact in the digitization of manufacturing operations and processes.

• Internet of things: A network of physical assets with sensors connected over the internet that collects and shares data; this data can be used to extract insights to improve operations

• Cloud computing: The key to the success of industry 4.0 as it helps to meet the scaling demands of integrating and connecting production, distribution, supply chain, and engineering departments.

• Artificial Intelligence & Machine Learning: AI & ML algorithms can be used to sift through the data generated by IoT sensors and create insights to optimize operations.

• Edge computing: By analyzing data at its source reduces the time lag for a response. This is needed as some issues need to be dealt with immediately. It isn’t practical to send the data to the cloud and get it back after analysis, as this can be time-consuming. Edge computing ensures data analysis is done at the source.

• Big Data: Big data analytics can be used to consolidate data from production plants. The data can analyze and uncover hidden logjams in production, keep production efficiencies intact, and mitigate breakdowns and machine downtime risks.

• Cyber security: Digitization can expand the attack surface. A digital smart factory combines virtual and physical systems with unique vulnerabilities, making them susceptible to cyber-attacks. A risk-based approach is a must to ensure vigilance across the manufacturing ecosystem and reduce threat risks.

The need for an industrial digital transformation service provider

Industry 4.0 answers the need of manufacturing companies facing increasing market demands and customer needs to deliver sustainable, personalized products and services. To remain relevant and competitive, they need the help of digital transformation consulting firms, or top digital transformation companies who can help them leverage the power of data, IoT, AI/ML, and next-generation digital transformation technologies to enable automated operations. Ness has proven itself a digital transformation company in USA who can help firms design the end-to-end roadmap, reinvent operating models, data governance framework, performance management, and security to accelerate the transformation. We can help with company digital transformation, improve their equipment effectiveness using digital twins and digital intelligence, lower production cost, improve safety and ensure quality outcomes. Ness also has a good ecosystem of technology providers who can help scale faster and accelerate the migration from a legacy manufacturing model to a more modern and digitized industrial framework.

FAQs