AI-assisted Software Development: A Star in the Making

In the early third millennium, humans aren’t the only owners of sophisticated intelligence on the planet. Machines are quickly becoming ever smarter thanks to artificial intelligence that powers them. As a disruptive innovation, AI is revolutionizing multiple spheres of our lives, including education, healthcare, banking, logistics, e-commerce, construction, and more. Naturally, the high-tech industry follows in the wake of this across-the-board drive, switching over to AI-assisted software development. 

This article explores the reasons for this technology's popularity among IT experts, highlights AI use cases in software development, exposes the challenges professionals can face during the process, and offers a roster of the best-in-class tools in the niche.

AI in Software Creation: The Compelling Statistics

In our IT-fueled world, the usage of machine learning, neural networks, natural language processing, computer vision, robotics, and other artificial intelligence branches is on a steep spike, with the global AI market predicted to exceed the staggering $1.75 trillion by 2030.

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The trend is also symptomatic of the current IT sector, which is embracing AI in its pipeline activities on a growing scale. According to the 2024 Worldmetrics Report, 80% of companies invest heavily in AI that is utilized for various purposes, including software development, and 50% are planning to raise such expenditures in the future. 

Today, 21% of tech executives admit enjoying the benefits of using AI for development of high-tech products, and this number is only likely to increase. Over 35% of UX tools will be AI-based within three years – a seven-fold surge against 2023! Moreover, in 2026, almost a third of new apps will have AI-created user interfaces with unique adaptation and personalization capabilities, and a year later, 15% of all apps will be generated by artificial intelligence without human intervention or assistance.

What are the advantages generative AI offers when applied in the field of software development?

Assets of AI for Software Development Showcased

As a company specializing in offering AI-powered development tools, we at Metatable.ai know the perks the participation of artificial intelligence in SDLC ushers in.

• Augmented productivity. Since AI enables to automate the lion’s share of development workflows (coding, testing, bug detection, QA, integration, document generation, and the like), project teams can proceed faster and accomplish more within a certain period, which reduces a solution’s time-to-market.

• Cost efficiency. Automation spells less need for manual labor and consequently fewer human personnel involved in software creation, thus cutting down on OPEX immensely.

• Scalability and continuous improvements. The ML algorithms AI leverages allow tools to learn during the process of software building. As a result, they can use the knowledge they have obtained for more complex projects and become more sophisticated as they mature. Moreover, AI systems can adapt to the individual coding styles of developers they assist, which leads to the creation of a harmonized human-machine workforce. 

• Accessibility. Employing AI for software development allows for a massive involvement of non-tech experts in software building, thus paving the way for a wider harnessing of low-code/now-code development practices.

• Improved code quality. Less participation of human employees spells fewer errors and bugs, which leads to higher coding standards. Besides, AI can automate code reviews and offer real-time suggestions that can be implemented on the hoof without the need to wait for the testing phase.

• Accelerated bug detection and resolution. Another perk of AI-driven tools is their huge bug-detecting power, allowing QA engineers to streamline and speed up the debugging procedure dramatically.

• Enhanced project timeline assessment. Predictive analytics leveraged by AI for web development provide accurate forecasts regarding the time needed to complete the project. As a result, the development team can boost risk management, resource allocation, and project planning, ensuring its delivery within time and budget.

• Foolproof integration. Most AI coding tools play well with popular IDEs, which tells positively on the efficiency of the SDLC.

• Code refactoring. AI-driven tools can improve code structure and readability without affecting the codebase’s external behavior.

• Boosted collaboration. Consistent coding standards, automated code merging, and seamless integration support foster a collaborative environment with all stakeholders involved in software development and knowledge sharing.

Despite the evident advantages of generative AI in software development, IT experts should watch out for the pitfalls existing in this field.

Zooming in on the Challenges of AI-assisted Software Development

As with any budding technology, using AI-driven software development tools is fraught with certain risks and bottlenecks.

• Ethical considerations. The developer community voices concerns related to possible biases in algorithms and exacerbating existing disparities caused by the immaturity of such tools.

• Complacency. The excessive reliance on AI tools may diminish the role of human creativity and intuition, which form a healthy foundation for the coding process. 

• Subpar code quality. If the AI model you employ lacks validation and real-time testing, the code it generates may abound in errors, bugs, and security gaps.

• Code interpretability. Quite often, developers have problems understanding the code and explaining the rationale behind steps taken by AI mechanisms, which endangers the accountability and transparency of SDLC.

• Security issues. Tech-savvy cybercriminals or unscrupulous developers can manipulate AI models, which yield unintended and unexpected outcomes, posing a threat to the solution’s integrity. 

If you manage to avoid these pitfalls, you can leverage AI-assisted tools at various stages of the software development process.

The Use Cases of AI-assisted Tools in SDLC

You can maximize the benefits of AI in software development if you know where to apply the technology.

• Code generation. Here, AI leverages machine learning algorithms and models to generate software code in accordance with high-level requirements and parameters. Moreover, AI mechanisms can understand context and syntax and utilize predictive mechanisms to anticipate code lines, which rules out the necessity for developers to type complete lines of code manually. 

• Code review and optimization. Developers must make sure the codebase adheres to all quality standards. AI tools scan it and assess for deviations, pinpointing problem areas. Besides, they analyze detected bottlenecks, resource inefficiencies, and other issues causing underperformance and suggest measures to mitigate deficiencies and improve the product’s UX.

• Bug identification and resolution. Used for static and dynamic code testing, AI not only streamlines and facilitates bug hunting in software products, prioritizing issues according to their severity and impact. Its machine learning techniques can improve their capabilities after analyzing vast datasets code repositories contain and even behave proactively, preventing the most common programming mistakes.

• Quality assurance. Alongside routine automation of the testing pipeline, AI-assisted tools can be highly instrumental in creating offbeat testing scenarios. Thanks to them, QA engineers can assess the product’s performance in different conditions and predict software flaws before they actually occur.

When you get to grips with software development areas AI-fueled tools excel at, you should choose those tools that will bring maximum value to the SDLC. 

AI-assisted Development Tool Roster: The Magnificent Seven

What tools would we recommend as a vetted IT vendor specializing in this niche?

1. Metatable.ai

This is not a standalone tool but a full-fledged AI-first development platform, enabling the end-to-end creation of top-notch web applications. Using Generative AI, you begin with building a cloud-based SQL database to serve as a data pool for the machine learning mechanisms to draw upon. 

During the SDLC, you can keep in constant touch with the configurable AI assistant that will accompany you throughout all project phases. Each software creation aspect (front-end development, back-end development, API building, infrastructure setup, etc.) becomes a breeze with dedicated AI developers that assist the tech team in transforming their ideas into secure and fully functional software products. 

By using this platform, you can set up infrastructure promptly, get rid of tedious writing of repetitive code (which can amount to 90%!), and let your development team focus on the project, not on the tool they struggle to handle.

2. GitHub Copilot

Its primary task is code completion. The tool is trained on a huge code database from open-source projects, thanks to which it can understand the context of its writing. At the same time, it will block code suggestions that coincide with public code snippets to forestall open-source license violations. Copilot is compatible with major IDEs (such as Neovim, Visual Studio Code, JetBrains, and more) and supports most current programming languages. However, the support of some rare languages is subpar due to the limited data available in them. 

The vendor offers three pricing plans for its product (Individual, Business, and Enterprise), which also can be free for verified teachers and students. Yet, the owner retains the decision to judge whether your project qualifies for the free plan.

3. Tabnine

This tool emphasizes privacy and security in its operation being guided by SOC-2 compliance. Such capabilities are achieved by the tool running in a totally isolated mode within the chosen environment. The upside of this approach is the unique customization of the assistance to the specific codebase. On the flip side, AI fueling the tool can learn only from your code, which limits its power.  

Tabnine’s Basic version is free of charge, while more advanced Pro and Enterprise pricing plans enable full-function code completion, premium support, and private deployment.

4. Amazon CodeWhisperer

It is geared for code and code document generation as well as for offering real-time recommendations tailored to suit your current project and working style. Also, it can predict and complete code snippets or entire functions during their creation. Although compatible with popular IDEs, CodeWhisperer operates as a part of Amazon’s ecosystem and leverages its capabilities for different purposes (for instance, CodeGuru for performing security scans).

The tool’s individual level is free, and the professional tier is quite affordable, too.

5. Sourcegraph Cody

The tool excels at autocomplete capability, working across multiple configuration files, documents, and programming languages. Its peculiar feature is a conversational chatbot powered by NLP technology. You can consult it on tackling various code challenges (like legacy code deciphering) and obtain advice on a particular project. Cody is also good at creating unit tests and integrates with the three IDE behemoths (Visual Studio Code, Neovim, and JetBrains). If you employ it for under 500 code completions per month, you can enjoy Cody free of charge. For projects of larger scopes, you will have to choose between its Pro and Enterprise versions.

6. Gemini

It is not a separate tool but an AI-powered assistant in Android Studio. This free product is honed to answer simple questions related to resource location and code generation. It can predict code by suggesting its possible completion, but its advice may be inaccurate, so you should double-check what you are writing. Among other limitations are Gemini's narrow scope (honed for building native Android and Flutter apps) and the necessity to log into your Google account.

7. SQLAI

This is one more narrow-purpose tool meant to generate SQL and NoSQL databases. It provides a personal library to store your SQL and NoSQL snippets and can be integrated with mainstream databases (Postgres, Oracle, MySQL, MongoDB, and more) in one click. To visualize real-time data insights, the tool displays the results in tables or charts. The major limitation of SQLAI is obvious: it is called to generate databases but not languages. 

The vendor offers three pricing plans differing in usage limits and availability of access to real-time data analytics. 

Summing It up

Artificial intelligence is all the rage in the contemporary IT landscape, pushing the technological envelope across multiple industries. The software development domain is no exception, with an increasing number of professionals using AI-powered tools to create software products. If they manage to address challenges related to employing such instruments, they can drastically improve code generation and completion, its analysis and optimization, quality assurance, debugging, and testing of solutions they build. 
To maximize the value artificial intelligence brings to the software development pipeline, you should select a proper tool. Metatable.ai is a perfect choice for a comprehensive AI-assisted platform that can cover all the needs of a development team during the SDLC. Leveraging it, developers boost their productivity, enhance code quality, accelerate infrastructure setting and facilitate bug detection.