Discover the potential of generative AI in software development: New opportunities for developers

Generative Artificial Intelligence (AI) Generative AI (GenAI) is a technology that enables the creation of new content through the use of advanced neural network models and deep Machine Learning strategies. Unlike classic Artificial Intelligence (AI),  focused mainly on reproducing information or trends detected in the analyzed data, generative Artificial Intelligence allows the creation of new original structures: from literacy and musical works, through graphics, to the source code of computer programs. 

As a result, generative Artificial Intelligence has opened new perspectives for the IT industry, going far beyond the previously known research applications, while providing new solutions both technical and business. Tools based on generative Artificial Intelligence, such as ChatGPT or GitHub Copilot, are quickly gaining popularity, and their use in development teams is becoming not only an  interesting fact, but also a real strategic advantage. Observing how quickly these tools have adapted to the software development process, we can say that they have become so important that many programmers today consider them an indispensable part of their daily work. It is worth noting that the increased interest in generative Artificial Intelligence matched the growing need to speed up and improve the application development process. As a result, we stop speaking only about new, “trendy” technology, and treat it more and more often as a real driving force, allowing us to improve the software development process. 

How does Artificial Intelligence affect software development?

Traditional methods of software development require precise, often time-consuming work of analysts, architects, programmers and testers who iteratively design, produce, improve and verify code. Today, thanks to the use of AI, some of these activities can be partially or even fully automated. Multilayer neural networks, including popular transformers, enable efficient processing of the sequence of data provided. Then, on their basis, they can detect existing patterns and propose new original solutions, which are then reflected in the architecture, code, tests, and other automations of the software development process. 

In practice, this means that the programmer, instead of analyzing extensive documentation, can enter several phrases describing the desired function, and the AI tool will prepare a series of hints. Thanks to this, the path from a business need to a ready-made solution in code can significantly shorten. Importantly, the scalability of these tools has stopped being solely the domain of large companies with extensive R&D departments. Thanks to cloud services and open-source platforms, medium and smaller organizations can also implement solutions based on generative language models. 

The use of AI also affects the working culture of development teams. More and more often we hear about the so-called “AI pair programming”, where the tool serves as a virtual collaborator for discussing potential technical solutions. This style of work changes the perspective of software engineering, in which instead of manually developing details, programmers can outsource monotonous or repetitive Artificial Intelligence tasks, and focus on challenges that require creativity and soft skills. This trend, supported by the growing number of AI tools available on the market, is increasingly redefining what we mean by “creative work” related to software development. 

What are the advantages of using generative AI in software development?

Generative Artificial Intelligence completely changes the market and creates many new opportunities to use technology in the next stages of the software life cycle. Below I discuss only selected and most commonly identified in development projects. 

1. Speeding up prototyping 

Accelerate development. Generative language models can automatically create backbone application code, unit tests or even sample test data, significantly reducing the time needed to develop the first versions of the project. Thanks to this, the team can move faster from the proof-of-concept phase to the verification of the solution in the context of the target use case. 

2. Reducing repetitive tasks and improving quality 

Many developers (including myself) admit that a lot of time in the project is dedicated to creating standard application modules, e.g. for integration layers, data access, or authorization security configurations. Generative Artificial Intelligence, with the support of tools such as GitHub Copilot, can automate most of the above-mentioned work, thus reducing the number of errors resulting from the so-called human factor. All this improves the quality of the delivered software. 

3. Increasing software developers’ productivity

Automating repetitive and structured tasks removes a significant part of the responsibilities of programming teams. According to an article published by Harvard Business School, the productivity of the team implementing AI tools can increase up to 40% depending on the area of application. This includes writing new code, refactoring, and testing or documentation. Improved productivity is also reflected in lower costs (since money can be used in other, more strategic areas).   

 4. Helping developers with solving technical problems

Generative Artificial Intelligence tools not only can create original content, but also understand its context. Thanks to this, they can act as a virtual mentor and give us a hand when we encounter complicated issues in programming. Often it is enough to paste a snippet of code or ask a technical question (Fig. 1), and ChatGPT tools will suggest a potential path to solve the problem and clarify any doubts in detail. 

5. Facilitating documenting and commenting on the code

IT projects, especially those with a long history, are often associated with challenges in the area of poor quality and even lack of documentation. Generative Artificial Intelligence can quickly prepare a description of the operation of a system, function, class, or module, based on the project code and its structure, as well as the naming conventions used. This allows developers to save time and make everyday work easier. In addition, generative Artificial Intelligence tools monitor the created code on an ongoing basis and are able to create suggestions and comment on the solutions used. 

What are the risks of generative AI in software development? 

Although Artificial Intelligence offers great opportunities, I must also mention that its implementation is associated with a number of significant challenges. Neglecting these issues can result in unexpected consequences.  

1. Over-reliance on automation 

There is a concern that developers will rely too much on automatic suggestions, which may lead to a decline in a deeper understanding of the code and architectural patterns used in the project. As a result, this may adversely affect the quality of long-term projects, because the team may lose the ability to solve problems with full understanding. 

2. Risk of inaccurate suggestions 

Generative AI models, despite impressive results, can still generate errors or propose suboptimal solutions. As the results of the research presented in the article “Evaluating Large Language Models Trained on Code” show, these tools sometimes produce seemingly correct but, in fact, bad code fragments, which requires mindfulness and an in-depth understanding of the proposed solution on the part of the developer.  

3. Privacy and security issues

The process of training generative Artificial Intelligence requires a huge amount of data. The sources of this data are mainly public clouds, Internet resources, but also data entered by customers into AI tools. Companies providing solutions based on this technology often collect their customers’ data in order to use it in development processes. However, this data may contain sensitive or copyrighted information. Therefore, it is important to use the right configurations and always check what data is entered into the AI tool so that the entire process is controlled and safe. 

 4. Potential copyrighting infringement

The above aspect implicates huge challenges in terms of potential copywriting infringement.  

Training Artificial Intelligence using the code may cause some doubts as to the originality of the solutions provided and compliance with copyright law. Developers using AI tools must therefore be cautious, especially when taking advantage of tools that generate code snippets from licensed sources. For large corporate projects, the company’s legal team should verify the license under which the code was generated and whether it can be safely incorporated into the solution created by the organization. 

5. Difficulty in auditing decisions made by the AI model

Most modern AI models, including generative ones, operate on the principle of the so-called “black box”. This means that understanding the exact course of model reasoning in the decision-making process is limited. This may complicate security audits, especially in the context of regulatory compliance (e.g. GDPR), as well as in some cases make it difficult to verify the quality of the provided code 

How does generative AI fit into the software development lifecycle?

Software development cycle Software Development Lifecycle (SDLC) consists of the following steps:  

• planning;  
• requirements analysis  
• design,  
• implementation  
• testing,  
• implementation,  
• maintenance 

Planning and analysis of requirements 

Generative AI can be used at almost any of these stages, but its impact is most visible in the design and implementation phase. In the planning and requirements analysis phase, AI tools can help to automatically collect information from various sources (documentation, conversations, emails) and process it into a coherent list of requirements or user stories. Thanks to this, the development team can quickly understand the essence of the tasks to be carried out.  

Design and implementation 

At the design and implementation stage, generative models serve as intelligent programming assistants. Once the developer enters the context (e.g. the target library or framework), the AI will suggest the module structure, design patterns and generate sample code. Tools such as GitHub Copilot can additionally suggest which syntax, unit tests and optimization corrections are best used in the project.  

During testing, generative models can support the creation of test cases and scenarios for automated tests. Most unit tests in development projects are strongly standardized, so AI – based on the test coverage testing process – can significantly improve this process.  

When it comes to implementation and maintenance of the application, Artificial Intelligence can help in monitoring and analyzing logs in search of unusual behaviors or system failures. As a result, AI becomes an integral part of the entire software life cycle, bringing the individual stages together in a more automated and coordinated process. 

What are the use cases for generative Artificial Intelligence in the software development process?

The number of such use cases is currently virtually unlimited. However, in order to best illustrate how huge opportunities are created by Artificial Intelligence models, I will list the most common issues. I will also indicate the main AI tools supporting tasks in this area. 

• support in the analysis of requirements – AI tools can automate the processing of large amounts of data and formulate final design requirements; e.g. IBM Watson Discovery / OpenAI ChatGPT 
• architecture design – generative neural networks can propose potential architectural patterns and entire modules, taking into account the structure of requirements and technological limitations; e.g. GitHub Copilot / Diagramming AI 
• prototyping and mockuping – GenAI can create initial versions of interfaces (UI) or model basic functionalities, speeding up the verification of ideas with stakeholders before developers even start writing code in HTML/CSS/JavaScript; e.g. Midjourney / DALL·E 
• recommendations of security patterns – generative models identify gaps in the code and suggest safer approaches to data storage, encryption or authorization; e.g. Snyk / Checkmarx 
• automatic code generation and completion – GenAI suggests fragments or even whole blocks of code, which speeds up implementation and reduces the number of a syntax errors; e.g. GitHub Copilot / Microsoft IntelliCode 
• code refactoring and optimization – AI tools indicate places where code can be simplified, execution accelerated, or secured, minimizing the risk of new defects; e.g.  DeepCode / SonarLint 
• generating automated tests – AI models analyze code and existing tests to generate new test scenarios, providing higher coverage and faster error detection; e.g. Testim / Mabl 
• generating test data examples – GenAI is able to generate diverse and realistic input data that facilitate verification of the correct operation of the application in various conditions; e.g. Gretel.ai / Mostly AI 
• error detection and debugging – analysis of patterns occurring in logs or stack traces allows AI to suggest the cause of the error and indicate corrective steps; e.g. Logz.io / Datadog APM 
• code translation and conversion between languages – AI facilitates technological migrations by translating code fragments into a selected language or framework; e.g. TransCoder (Facebook AI) / CodeConversion.ai 
• log analysis and monitoring – Artificial Intelligence detects anomalies in data coming from operating systems and applications, signaling potential failures and performance problems; e.g. Elastic APM + Machine Learning / Dynatrace with Davis AI 
• performance optimization – AI indicates snippets of code or configurations that need improvement, e.g. in the context of resource consumption, response time or scalability; e.g. AppDynamics / Amazon CodeGuru Profiler 
• creating documentation and comments – NLP models generate descriptions of functions and modules based on the code structure; e.g. CodeT5 / AI Docstring 
• automatic generation of API documentation – AI, based on code and comments, creates definitions of endpoints and their parameters; e.g. Postman AI Assistant 
• support for DevOps and CI/CD processes – AI models automate the creation of implementation scripts and configuration of pipelines, improving the process of building, testing, and implementing software for various environments; e.g. Jenkins with AI / Harness Plugins 

How generative AI tools help with coding tasks

Bearing in mind that writing code is a key element of software delivery, it is worth taking an in-depth look at the various stages of this process and tools that support the automation of both generating and optimizing the code directly at the IDE level. Often at this stage important decisions are made which determine the success of the project. Therefore, in my opinion, AI support for developers is particularly valuable here.  

1. Contextual code hints

One of the most important goals of generative tools is to eliminate repetitive and error-prone work. Thanks to deep neural networks that “understand” the context (e.g. variable names, comments and project structure), the programmer receives ready-made code fragments or entire functions for immediate use (Fig. 2.-3. The most popular tools that provide this functionality are GitHub Copilot, Microsoft IntelliCode and TabNine. 

2. Explaining the code and learning patterns

However, it is important not only to generate the code but also to thoroughly understand how it works. Especially in projects with a high turnover of employees or numerous less experienced programmers employed, GenAI can act as a first-contact mentor (Fig. 4). Explaining how a given function works or proposing the best design pattern – these are tasks in which ChatGPT, Bing Chat or Google Bard can be extremely helpful. Thanks to this, new team members can implement the project faster.  

3. Integration with IDE and CI/CD tools  

Tools such as GitHub Copilot or TabNine are also available as plugins for popular development environments (Visual Studio Code, JetBrains, etc.). Thanks to this, developers do not have to switch between applications to use AI. Some of them automatically generate pull requests in version control systems, which further simplifies the process of delivering the final solution to the repository and thus to customers. 

 4. Automation of creating and optimizing database queries

GenAI tools such as OpenAI Codex or Askdata integrate perfectly with high-level programming languages. In projects that strongly rely on databases, generative AI can help generate complex SQL queries based on a verbal query (e.g. “Show all customers who have not made a purchase in the last year and whose location is Poland”). This solution allows you to avoid typos and syntax errors, as well as to optimize indexes or query plans. 

Future of software development using Artificial Intelligence tools

Looking at the pace of AI technology development, we can say that the future of programming will be strongly linked to automation and intelligent assistants. Models similar to GPT-4 or subsequent iterations, with an even higher level of understanding of the context, will gradually take on an increasing role in software development. Many companies are working intensively on the development of dedicated domain models, trained in specific languages and specific industries, e.g. in banking, biotechnology or the automotive industry. Such specialization can make Artificial Intelligence even better understand the business requirements and the context in which the application is created. 

The role of software developer 

It is possible that in the next few years the phenomenon of the so-called “AI-driven development” will be observed. In such a situation, the main task of the programmer (human) will focus on formulating the requirements and transferring them in a form understandable for the AI model, and then supervising the generated code. The developer will then become a kind of “conductor” who will manage the orchestra of intelligent tools. Already today, we see glimpses of this vision in tools that can generate not only backend code, but also interfaces, tests and configurations for DevOps tools. 

Prompt engineering 

Of course, this vision also comes with questions and doubts. In such a world, will the role of a programmer be marginalized? History so far shows that computers take over mainly repetitive, mechanical tasks, while  human creativity and understanding of the wider context are difficult to fully replace. New specializations will probably become more and more popular, e.g. “AI Behavior Engineer” or “Prompt Engineer”, i.e. professions that allow optimal communication with AI models, as well as verify and fine-tune their results. Thus, the development of generative Artificial Intelligence does not necessarily mean the end of traditional programming – rather, taking it to a higher level of abstraction, where we will focus on the design of logic and strategy, rather than on writing hundreds of lines of code. 

AI in Software Engineering – summary 

Generative Artificial Intelligence has revolutionized the world of programming, piloting us into a new era of collaboration between humans and machines. Thanks to tools such as ChatGPT or GitHub Copilot, the software development process becomes faster and more efficient. My experience shows that GenAI significantly reduces time-consuming and repetitive tasks. 

At the same time, the use of these modern solutions requires a responsible approach. You should be aware of the risks brought by this technology. Only the conscious implementation of generative Artificial Intelligence will allow to maximize its potential, while minimizing the risks in the area of privacy and data security. In my opinion, this is the key to the future of software development.