AI-augmented development and what are its uses?

Software development is beginning to benefit from using artificial intelligence to enhance development and the developer experience.

There have been tools for AI-augmented development, like Microsoft's IntelliSense, for some time. These AI automation tools have suggested possible function names and offered guidance for AI-augmented software development regarding parameters and data types based on the developer's typing of code in the immediate context. Despite their limited functionality, auto-suggest is a valuable feature that is a major reason why some developers prefer to use IDEs.

Currently, AI algorithms are going even further. Copilot, an AI-based development tool based on OpenAI code, can create blocks of code based on context or directives, taking coding assistance to a new level. The advanced capability of AI-assisted tools, on the other hand, has received mixed code reviews. Some say it's an excellent way to deliver code faster and cheaper; some say it delivers a code of doubtful quality. Let's dive deep into the topic and find out the real pros and cons of AI-assisted development so you can decide for sure if you'd like to employ AI-augmented software engineers.

In this article:

What is AI-augmented software development?

From applications' functionality to their evolution, development, and sale, AI has altered nearly every aspect of the software engineering industry. However, AI-augmented development is this field's most cutting-edge AI application. AI-augmented software development, defined as a system of AI-enabled development tools and platforms, enables developers to create apps more effectively, quickly, and reliably than they could with traditional "hand" coding.

AI-augmented software development systems offer a number of benefits, including the ability to perform manual code writing, accurately predict code generation, and even suggest code frameworks. AI-driven tools are the future of software development in many ways.

AI-augmented development vs. traditional software development

Application development "on steroids" is what AI-augmented software development is like. It makes the software team's experience better for everyone; whether you're a developer, an IT manager, a security expert, or a quality confirmation (QA) engineer, you'll find that simulated intelligence expanded tooling and systems assist you with working quicker and with improved results.
The primary distinction between AI-augmented and traditional software development is the amount of time saved by sticking to tried-and-true methods instead of using sophisticated ML or AI models to address issues previously thought to be only human-only. Programming improvement with the assistance of man-made brainpower of any kind (AI technology, machine learning, predictive analytics, etc.) can be an extraordinary resource since it can offer experiences that people could need.

How can AI help?

ChatGPT is currently being utilized by software professionals all over the world to gain firsthand experience with the ways in which advanced AI-augmented platforms can enhance the capabilities of application developers, software engineering managers, and business stakeholders. In their reports, they discuss the advantages that current AI-augmented software engineering can offer:

Creating the code

One report claims that ChatGPT's ability to write code has "shocked" software developers. According to this user, if you instruct ChatGPT to do so, "it will happily create web pages, applications, and even basic games" in all programming languages frequently used today. However, as will be shown in the following section, today's AI still possesses a number of significant limitations while writing software.

Documenting and explaining the existing code

One of the best features of ChatGPT is that you can give it some existing code, ask it what it does, and get a clear, accurate, and easy-to-understand explanation. That is a significant benefit that an advanced AI platform can only provide for developers working with legacy code, which is frequently highly opaque due to inadequate documentation. In point of fact, the AI engine's explanations are so well-written and clear that they can also be a great learning tool for developers with less experience.

Enhancing QA and defect remediation

ChatGPT can look at a piece of code to find and explain bugs that human programmers might miss. It is also able to suggest solutions to the errors it finds. Software testing can be significantly automated by advanced AI platforms, significantly shortening the development cycle.

Moving from one environment or language to another

It is possible for programmers to present ChatGPT with code written in one language and have it precisely translated into the syntax of a language with which the programmer may not be as familiar.

Development of low-code or no code

Low-Code/No-Code (LCNC) already significantly impacts many businesses' operations. It makes it possible for business users with limited technical coding skills to automate workflow processes without the need for IT professionals. The democratization of the software development process is further enhanced by the capabilities of ChatGPT to generate code from natural language inputs.

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Geniusee's experience with AI-augmented software development

Geniusee stays on the cutting edge of software development trends, so our developers actively explore opportunities for AI-augmented development. We provide an AI-augmented development team for our clients to boost their businesses and provide multiple benefits, such as faster movement on SDLC (software development lifecycle) and cost reduction.
Among AI staff augmentation, we provide other services, such as AI-assisted coding and integrations of our products and services with chat GPT or other AI tools using API connectors. 
In our work, we are focused on three main tools for AI-augmented software development:

• Chat GPT 

OpenAI ChatGPT is an extensive language model intended to comprehend natural language inputs and generate appropriate responses. Even though ChatGPT is not made for creating code, it can still be used for this in some situations.

• Codex

OpenAI Codex is a natural language processing (NLP) model that uses natural language input to generate code. It can process a wide range of text inputs and respond with high-quality code because it is based on the ChatGPT architecture. It can comprehend the underlying structure and logic of programming languages because it is trained on a large dataset of code examples.

• Copilot

GitHub Copilot is an artificial intelligence-fueled code colleague intended to assist software engineers with composing code all the more productively. It uses machine learning algorithms to suggest code snippets and functions based on the project's context and is built on top of OpenAI Codex.

Yet, we do not hesitate to use other AI-powered coding tools, such as 

• Tabnine
• Captain Stack
• GPT-CC (GPT-Code-Clippy)
• Second Mate
• IntelliCode
• CodeWhisperer
• YouCompleteMe
• Asm-Dude
• Atom
• Clara 
• and many others.

Discover our track records in multiple industries in our portfolio, or book a call for a free consultation with experts.  

AI-augmented software development concerns

It's important to remember that AI engines don't actually think. Instead, they use patterns in their training data to predict the proper response based on the parameters given to them. As a result, the real-world context of the issues they discuss is lost on them. As a consequence of this, they may make glaring mistakes that would be obvious to a human.

For instance, despite ChatGPT's extensive and impressive ability to convert natural language descriptions into code, it has significant limitations when it comes to producing usable code on its own, particularly for non-trivial coding issues.

ChatGPT occasionally produces code that may or may not work when given complex software development tasks. That is, the code may appear to carry out the developer's instructions, but it contains non-obvious flaws that render it unreliable. Naturally, such code is the stuff of nightmares for developers.

Thus, we're not even close to where programming can basically be gone over to a simulated intelligence coding motor. However, machine learning and AI-enabled platforms and computer systems may be writing software that human developers can use as a starting point, saving them time and avoiding many bugs that humans always introduce into their code when starting from scratch.

1. Lack of creativity 

The developers of web and mobile applications have a lot of vision, creativity, imagination, and way of thinking. This is because humans can only create a design. Despite its intelligence, AI has the limited processing power and is restricted to programmable algorithms. While using AI can assist development teams with specific aspects of blueprint design, it cannot be creative like the human mind.

2. Loss of human employees

This is an immediate risk of computer-based intelligence machines since experts with a normal range of abilities or those in the learning stage can be supplanted on the grounds that man-made intelligence can complete routine first-level undertakings. For instance, AI can automate the supervision system of mobile app development companies, reducing the amount of labor required for an assembly line position.

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3. Price

Cost is another crucial aspect in determining the viability of AI, following the lack of creativity and job loss. The cost may be a case-by-case issue for mobile app developers. The majority of AI-based mobile development solutions have a significant development cost. AI has limited vision and must be updated frequently to match expected outcomes. Costs for management and maintenance go up as a result. As a result, big businesses with big projects and budgets are more likely than small businesses to make the most of their AI investments.

4. Lack of experienced adjustment 

Even the most intelligent AIs lack human-like judgment. Instead of using algorithms and calculations, business owners frequently need to make decisions based on their judgment. For instance, to flee the shooting in Sydney, Australia, in 2014, individuals called Uber. Instead of deciding to assist those in need, the app increased the price in accordance with the economics of supply and demand.

5. Naive way of thinking

Human insight is based on a cognitive knowledge process that necessitates real-time observation and experience, which is less spontaneous. However, the AI machines that are utilized in the creation of mobile apps are distinct and follow robotically programmed wisdom. In contrast to humans, AI cannot access and analyze the vast amounts of information that they store. AI machines cannot adapt their actions and responses to changing requirements. It lacks the human touch needed to make sound decisions, like determining how well two people work together. When making firm decisions based on instinct and common sense, human judgment is superior to AI.

6. Challenges in implementation 

Despite the numerous advantages of AI, its implementation is challenging. Adding and utilizing AI capabilities in multiple dynamic aspects can be tiresome for web and mobile app developers. This is because the app's moving parts need to work perfectly together to ensure a smooth and engaging user experience. The AI algorithm must be taught to process while evaluating and comprehending inputs during the implementation process to ensure that the final output meets the user's expectations. A developer must also incorporate the most recent trends and alter their behavior because AI apps are open to regular learning. Implementing AI  augmentation is difficult due to these factors, but the results will benefit your company.

What are the benefits of augmented intelligence applications?

Expanded knowledge further develops human decision-making, both by overwhelming information that would overpower a human leader and by eliminating factors that can vary or misjudge information, including inclination, weakness, and interruption. (It is essential to recollect, nonetheless, that people can infuse inclination into expanded knowledge calculations during framework creation.) When used correctly, human intelligence and augmented intelligence are greater than the sum of their parts.

In science, business, and everyday life, augmented intelligence allows people to make decisions based on more accurate data. It addresses a harmonious connection between man and machine. Augmented intelligence enhances our ability to make decisions and, by extension, our intelligence rather than replacing us.

AI-augmented software development use cases

Augmented intelligence and deep learning delivered applications in any industry that mines big data for patterns and predictive indicators. Examples include the following:

1. AI-enabled citizen developers

One of the essential aspects of AI is that it enables less technically savvy individuals to develop and update applications independently. The general public also benefits from AI, just like skilled developers do. Software productivity has increased to previously unimaginable levels due to making software development accessible to everyone.

Composing programming code is a phenomenally nuanced and fastidious undertaking, requiring an informed proficiency with sharpened innovativeness and scrupulousness to make any new, complex piece of programming. AI is currently unable to generate code on its own. On the other hand, AI systems can "learn" a mountain of data patterns, use them to generate code suggestions, layout patterns, and decision trees, and forecast future trends. In this way, even novices with no formal education can collaborate with AI-augmented software to produce helpful software code.

2. The AI data explosion 

Now that it is easier than ever to write software thanks to AI augmentation, big business, and data are colliding. Most AI-assisted application designs aim to mine, manipulate, and utilize data for competitive advantage. This makes it easier for people to access all that data, which is inevitable. Data mining is anticipated to expand at a parabolic rate as a negative side effect of such a data explosion.

3. Concerns about AI-augmented software's security 

At this point, the security concerns about AI-augmented software are pretty obvious. Additionally, there are numerous security flaws as our software infrastructure becomes increasingly complex. It goes without saying that AI isn't nearly sophisticated enough to give a layperson the ability to launch a significant cyberattack, but security must remain vigilant. It's vital to guarantee all artificial intelligence expanded stages that you carry out come furnished with worked-in guardrails to impede cyberattacks from outside designers.

4. AI Building AI

Before reading the following section, you might want to sit down if you are still struggling with the idea of a "meta-verse." Since pretty soon, computer-based intelligence expanded stages will foster simulated intelligence of their own.

Yes, that is accurate: AI will eventually create more AI, not just ''simple'' software solutions.

This idea might appear dystopic. But don't worry—this kind of self-referential development will undoubtedly fuel exponential tech growth, enabling faster and more advanced software development, which will be mirrored by sophisticated security measures. It's hard to imagine how this stage of AI enhancement could direct humanity's future. On the other hand, future AI should be to everyone's advantage as long as human safety remains the top priority.

5. Last but not least, AI and the Democratization of Technology 

One of the most remarkable effects that AI-augmented software will have on the world is the democratization of technology as a whole. Thanks to cloud computing, even the smallest new businesses can rent data centers and compete in large markets. Proportionately, taking on man-made intelligence expanded programming stages let private ventures work out an enormous, cutthroat foundation.

How AI can help develop better software faster

Developers use AI to enhance each stage, from gathering requirements to deploying software. Take a look at the following instances.

• Requirements management

When done poorly, requirements management—the process of gathering, validating, and tracking the requirements that end users have for a piece of software—is a significant contributor to project delays, costs, and failure. Digital assistants that can look over requirements documents, point out ambiguities and inconsistencies, and offer suggestions for improvement have been introduced by several vendors. These instruments are fueled by normal language handling and prepared on broadly referred to rules for composing excellent prerequisites. These tools can find inaccuracies or other weaknesses like incomplete requirements, immeasurable quantification (missing units or tolerances), compound requirements, and escape clauses to speed up requirements review.

• Project management

AI is even being used by businesses to enhance project management. Tools that use advanced analytics to analyze data from a large number of previous software projects to predict the technical tasks, engineering resources, and timelines needed for new software projects have been introduced by a number of startups. Project execution and planning can be improved as a result of this.

• Coding, code-reviewing, sourcing, and debugging tools

AI-powered code completion tools offer suggestions for completing lines of code as developers type. This may cut down on the number of keystrokes required and increase code reliability by as much as half, according to various sources. Some code generation tools even produce a list of code snippets with the highest level of relevance. By comprehending the intent of the code and recognizing common errors and their variants, code review tools use AI based bug detection and suggest code modifications automatically. At Facebook, an AI tool for bug fixing predicts problems correctly and suggests solutions 80 percent of the time.

• In-depth testing

For many years, automated testing tools have been used to execute test scenarios drafted by code quality assurance analysts. Tests can now be run, and test cases are generated thanks to artificial intelligence automatically. This helps ensure that more scenarios and functionality are tested and saves analysts time.

• Deployment

Defects in software may only become apparent after it has been installed in the intended environment. However, by looking at data like application logs and statistics from previous code releases, AI-powered tools are assisting in anticipating deployment failures. In the event of a failure, this may accelerate root cause analysis and recovery.

How can AI-augmented software development enhance businesses?

By assisting in managing the problem's complexity and allowing workers to offload routine tasks to the AI, AI-augmented software development helps reduce cognitive load throughout the software development life cycle. This enables workers to concentrate on the creative and analytical tasks that humans excel at.

1. Productivity and efficiency

Gains in productivity and efficiency are two of the most frequently cited advantages of implementing AI in software development of organizations. The innovation handles errands at a speed and scale that people can't coordinate. At the same time, AI enables human workers to move on to higher-value tasks that technology cannot perform by taking care of such duties. This enables businesses to maximize the talent in their human capital while simultaneously minimizing the costs of carrying out routine, repeatable tasks that can be carried out by technology.

2. Speed acceleration

Business AI's increased speed makes it possible to reduce development cycles and the time it takes to go from design to commercialization. This shorter timeline results in a better and quicker return on investment for development funds.

3. Business model expansions and enhancement

New capabilities and the expansion of the business model As data and analytics are integrated into the enterprise, new opportunities for businesses to participate in various fields become available.

4. Raise customer service and satisfaction

AI can incorporate everything we know about the customer, their requirements, our solutions, and the competition to provide better customer service. It can then present the customer with everything they need, when they need it, and more, resulting in more customized and personalized interactions between businesses and each individual customer.

5. Monitoring boost

Organizations can now implement near-instantaneous monitoring capabilities that can alert them to issues, recommend actions, and, in some cases, even initiate a response thanks to improved monitoring AI's capacity to process massive amounts of data in real time. When large amounts of data need to be analyzed and comprehended, enterprise cybersecurity operations, for example, can benefit similarly from AI's monitoring capabilities.

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6. Better quality and less manual human error 

Organizations that incorporate AI technologies into their processes can anticipate a more vital adherence to established standards and a decrease in errors. Combining artificial intelligence (AI) and machine learning with technology like robotic process automation (RPA), which automates repetitive, rules-based tasks, not only speeds up processes and reduces errors but can also be trained to improve itself and take on broader responsibilities.

7. Better talent management

From streamlining the hiring process to eliminating bias in corporate communications, AI is being used by businesses to improve many aspects of talent management. By successfully sourcing, screening, and identifying top-tier candidates, AI-enabled processes not only have the potential to impact workforce productivity but also save businesses money on hiring costs. Companies also use chatbots to mentor employees and offer job candidates personalized experiences as natural language processing tools have improved. In addition, AI tools are being used to determine equitable pay, identify and keep high-performers, and gauge employee sentiment.

Conclusion

Companies that can change, grow, and innovate are successful. They must devise disruptive responses and effective routes to solve their problems.

Geniusee, a digitally native technology company, is passionate about driving innovation to improve the way we work by embracing transformative technologies. In this space, we are convinced that artificial intelligence can strengthen organizations, improve collaboration, and foster more profound knowledge and creativity. 

We've used augmented coding on many projects, and we want to help you use it to improve yours as well. Contact us for more information.