Generative AI for Coding

In the ever-evolving landscape of software development, developers are constantly seeking ways to increase efficiency and productivity. Generative artificial intelligence has the potential to revolutionize the way they write code, debug, and solve coding challenges. Large Language Models (LLMs) are prime examples of generative AI models, as they can generate coherent and contextually relevant text including code, based on the natural language prompts they receive.

Toloka's support to BigCode project showcases the tremendous potential of LLMs and crowd collaboration in the sphere of coding. We will explore how LLMs are transforming coding and highlight some other noteworthy applications.

What are code-generating LLMs

A large language model is a deep-learning model that knows how to generate relevant textual information and thus represents a vivid example of generative AI. Deep means that the neural network that is the base of LLM has a lot of layers, which gives such a system an extensive array of capabilities. For example, it can memorize a huge amount of facts and insights about the world, predict a large number of words following each other in a text, and so forth.

Code-generating LLMs are specialized AI models designed for the task of generating computer code. Such models undergo training with datasets that encompass repositories, specialized documentation, programming code, relevant web content, and other related data. They possess a deep understanding of various programming languages, coding patterns, and software development concepts. Code LLMs are engineered to assist developers and programmers.

When these LLMs are seamlessly integrated into the developer's Integrated Development Environment (IDE), they possess a comprehensive understanding of the coding context, which includes elements such as comments, function names, and variable names. This contextual awareness empowers them to provide more refined and contextually relevant coding suggestions.

Capabilities of LLMs in Coding

Initially, LLMs were intended to generate various textual data. They accomplish the tasks of composing, translating texts, and can respond to queries posed by a human and others. LLMs have demonstrated remarkable capabilities in understanding and generating human-like text. And now they can also make code suggestions, as it is also a language, but designed specifically for programming.

These models are fine-tuned for specific tasks, and when it comes to coding, their capabilities are staggering. LLMs have the potential to significantly boost developer’s productivity, reduce coding errors, and enhance the overall coding experience. Generative AI, LLMs in particular, offer numerous ways to assist developers across various aspects of their work:

Code Generation

LLMs excel at generating code snippets or entire functions based on natural language descriptions. Developers can simply describe what they want to achieve in plain language, and the LLM can generate code that corresponds to their needs. This functionality significantly reduces the entry barrier for coding, making it more accessible to non-programmers.

Code Completion

Code completion, often referred to as autocompletion, is one of the AI coding tools that automatically suggest code snippets and other elements as developers type. Autocompletion reduces the need to write lengthy code constructs manually. By offering contextually relevant suggestions, it speeds up the coding process. Developers can write code quickly and more efficiently. This not only saves time but also minimizes the risk of typos and syntax errors.

Code Translation and Modernization

LLMs are trained on extensive multilingual text data, making them proficient in multiple programming languages. They can easily understand and translate existing code from one language to another, aiding in interoperability between different systems and languages, because different programming languages may not seamlessly integrate with one another. Code translation is necessary to ensure that components written in different languages can work together effectively. Also, older projects may be written in outdated or less commonly used languages. Code translation can modernize such code.

Code Summarization

Software projects involve extensive codebases, libraries, and frameworks. LLMs are good at understanding natural language, including code comments and documentation. They can analyze and explain code, providing concise summaries. Code summarization makes codebases more readable by extracting key insights, function descriptions, and usage patterns, reducing the load on developers. Maintaining and updating code is more efficient when developers can quickly comprehend its purpose and functionality. Broadly speaking, сode summarization can accelerate developer productivity

Code Refactoring

Refactoring code makes it easier to read. Maintaining clean and efficient code is crucial for software projects. LLMs can offer suggestions for code refactoring, helping developers improve code performance. Through refactoring, developers can identify and eliminate inefficiencies.. This optimization can result in faster execution and reduced resources consumption.

AI-Assisted Testing

Quality assurance and testing are integral parts of the software development lifecycle. Generative AI can assist in creating test cases, ensuring code correctness and robustness. LLMs can automatically generate such tests based on code descriptions or specifications, saving time and effort in test case creation. By automating test case generation, enhancing test data creation, and proactively identifying issues and vulnerabilities, AI-driven testing not only ensures software quality but also accelerates development cycles

Error Detection and Debugging

LLMs can analyze existing code and recognize errors, including errors in syntax or variables and logical inconsistencies, saving developers valuable time in the error identification phase. Generative AI can provide detailed descriptions of errors, explaining their causes and suggesting potential fixes, making it easier for software developers to understand and address issues.

With LLM-powered code suggestions, developers can code faster, more accurately, and with a deeper understanding of their projects, ultimately accelerating software development. While LLMs offer powerful assistance, developers should also consider the quality of the generated code, potential ethical concerns, and data privacy and security when using generative AI.

Advantages of LLMs for Coding

• Efficiency and speed. LLMs significantly boost coding efficiency by providing coding suggestions and autocompletions. Generative AI can also greatly accelerate the coding process;

• Reduced errors. By automating routine tasks and offering real-time error prevention, LLMs help reduce human errors, leading to fewer bugs and improved software quality;

• Developers productivity. Developers can focus on higher-level problem-solving as LLMs handle repetitive tasks for coding. This results in increased overall productivity;

• Accessibility and education. LLMs with natural language interfaces make coding more accessible to non-programmers, allowing them to describe it in plain language. That way LLMs can serve as educational tools, helping beginners learn programming concepts and best practices by interacting with AI models;

• Versatility of use. LLMs are versatile and support multiple languages and programming tasks. This means developers can use the same tool for various coding needs, further speeding up the development process.

With LLM-powered code suggestions, developers can work faster, more accurately, and with a deeper understanding of their projects, ultimately accelerating software development. While generative AI offers powerful assistance, developers should also consider the quality of the generated code, potential ethical concerns, and data privacy and security when using generative AI tools.

Possible Challenges of Code Generation

Generative AI coding tools can significantly enhance productivity, code quality, and understanding, but they also come with certain challenges and considerations:

• Code Quality. The LLM code quality may vary. Therefore, developers must review and validate it to ensure that it meets their standards;

• Data Privacy and Security. Using LLMs for code creation and analysis may involve sharing sensitive data. LLMs may inadvertently introduce security vulnerabilities if not used with care. Ensuring the security of the source code is imperative, so developers should take precautions to protect data privacy and security;

• Model Biases. LLMs may exhibit biases present in their training data, potentially leading to biased code suggestions or documentation. Careful review and bias mitigation strategies are necessary;

• Over-Reliance. There's a risk of over-reliance on LLMs, which could lead to developers depending too heavily on AI code without fully understanding the logic behind it.

LLMs offer significant advantages in terms of coding efficiency, quality, and productivity. However, they come with potential drawbacks related to code quality, security, bias, and the risk of overreliance. Developers and organizations should use LLMs judiciously, exercise proper oversight, and ensure that new code aligns with project requirements and coding standards

There are occasions when generation by means of LLMs cannot be accomplished without human help. Even though generative AI generally can handle most tasks, in some specific coding cases it requires human assistance.

Tasks Requiring Developer Expertise

Surely, generative AI shows great promise in coding, yet a research held by McKinsey highlights the importance of skilled engineers in utilizing these tools effectively. While accomplishing the tasks of code development using generative AI tools for the study, the test group determined three critical areas where human oversight and expertise are indispensable:

1. Code Quality Assurance. Participants reported instances where generative AI-based tools provided coding recommendations that were incorrect or introduced errors into the code. For example, one developer had to input multiple prompts to correct an AI tool's mistaken assumptions, while another had to meticulously guide the tool through debugging processes. Thus, we can conclude that experts play a vital role in thoroughly examining code for bugs and errors;

2. Contextual Understanding. Effective work is impossible if an employee is not familiar with the company they work for. We can also say the same about generative AI coding tools. They lack awareness of a specific project's and organization's needs in their "as-is" state. Developers must provide these tools with contextual information, including how the code will be utilized, the interfaces and systems it will interact with, the data employed, and more, as emphasized by research participants. Understanding the context of a coding task is essential to ensure that the final software product aligns with other company's applications, meets performance and security requirements, and addresses end-user needs;

3. **Complex Problem Solving. Generative AI-based tools excel at responding to straightforward natural language prompts, such as code optimization tasks. However, they may struggle with intricate requirements that involve combining multiple frameworks with diverse code logic. To address multifaceted demands, developers often need to manually merge components or break the code into smaller pieces. As one participant pointed out, generative AI is less effective when tackling complex problems that require a holistic approach.

So, there's no doubt that generative AI holds tremendous potential to support developers in various coding tasks, but still, there are some issues that cannot be fully solved by coding LLMs alone. The collaboration between AI technology and human expertise here is pivotal in order to experience the full potential of generative AI tools in software development.

In addition to the issues outlined, modern LLMs for coding are affected by a lack of personal data protection. To prevent any kind of personal data leaks from occurring in the future, the BigCode project came up with the idea of creating a model for identifying and removing personal data from the training datasets for such LLMs.

To label data for such a model, tolokers have taken part in the project, since no one but humans can better label the necessary information considered personal data so efficiently and quickly. Human annotators can provide the contextual understanding, quality assurance, and domain-specific knowledge required for accurate and reliable annotation, particularly when dealing with sensitive data in code snippets.

Toloka's BigCode Project: LLM needs human assistance

The BigCode project is a collaborative effort led by HuggingFace and ServiceNow aimed at enhancing the coding capabilities of Large Language Models (LLMs) by addressing regulatory challenges and improving data privacy. The project received assistance from Toloka to address specific challenges related to the handling of sensitive information within their dataset which contains a vast amount of code written in multiple programming languages, totaling 6.4 terabytes.

One of the primary objectives of the BigCode project was to ensure compliance with data privacy regulations, particularly GDPR (General Data Protection Regulation). This involved the removal of sensitive personal information, such as usernames, passwords, and security tokens, from the dataset to safeguard user privacy and data security.

To achieve this goal, the project curators needed to create a personal identifiable information (PII) model training dataset that could automatically detect and mask personal data within the dataset They identified 14 distinct categories of sensitive data that needed to be accurately recognized and handled appropriately.

However, annotating and labeling such a massive dataset would be a huge challenge. Traditionally, this task would have required hiring a team of skilled programmers who could manually review and label each code snippet according to the identified categories. Given the size of the dataset, consisting of 12,000 code chunks, this would have been a time-consuming and resource-intensive process, potentially taking many months.

Toloka introduced an innovative and non-traditional approach to tackle the challenge. They used the power of crowdsourcing by engaging a diverse group of 1399 tolokers from 35 different countries. This effort just took four days and amounted to a total of 4349 person-hours. To put this into perspective, it was equivalent to the workload of a single programmer working diligently for an entire year.

In the context of the BigCode project, it becomes evident that while LLMs can be valuable generative AI tools for assisting in the creation of code, human supervision and expertise remain essential. This interesting case also illustrates that LLMs though already very powerful and versatile in terms of tasks they can accomplish, still have weaknesses. They do need human refinement and verification of the information they generate. Still, even now generative AI tools significantly speed up the work of developers if they approach the task properly.

Generative AI coding tools

Generative AI coding tools are software applications and platforms that utilize the power of generative AI, typically using Large Language Models, to assist software developers in various coding-related tasks. These tools aim to streamline the software development process, increase productivity, and enhance code quality. Here's a list of some notable generative AI tools:

GitHub Copilot

Developed by GitHub in collaboration with OpenAI, GitHub Copilot is an AI-powered coding assistant that integrates directly into popular editors like Visual Studio Code. It provides real-time autocompletions and even generates entire code examples based on natural language comments and descriptions. It can also help with boilerplate code for unit tests generation.

AlphaCode

The AlphaCode neural network created by DeepMind can solve tasks at the software engineer level with a few months of experience. AlphaCode utilizes transformer-based language models for code generation. DeepMind experts pointed out that their product turned out to have the ability to independently write program code without having structural information about algorithms or program language.

TabNine

TabNine is an AI-powered autocompletion tool that uses GPT-based models to predict and suggest code as developers write. The tool analyzes the context in which the code is used and generates the most appropriate fragments. It supports a wide range of programming languages and Integrated Development Environments (IDEs), making it a versatile choice for developers.

Amazon CodeWhisperer

The CodeWhisperer is a programming tool developed by Amazon, that can automatically write entire functions based on just a comment or a few lines of code. Developers can use it directly in their preferred IDEs, including IntelliJ IDEA, PyCharm, WebStorm, and Amazon's proprietary AWS 9 cloud. The company points out that the system constantly reviews your code and comments, and even takes into account your own programming style and variable names.

ChatGPT

While GPT (Generative Pre-trained Transformer) models are not specifically designed as coding tools, they are a class of powerful generative AI models that can be adapted for various tasks, including coding-related tasks. ChatGPT can assist in generating code snippets based on the input and context provided. The quality and accuracy of the result generated by ChatGPT depend on the clarity of the input instructions and the complexity of the coding task.

Google Bard

Google has updated its Bard AI chatbot to help developers write and debug code. Bard is capable of generating new code, debugging, helping explain lines of coding, and even writing functions for Google Sheets in more than 20 languages, including C++, Go, Java, Javascript, Python, and Typescript

Conclusion

Generative AI for coding represents a significant advancement in software development, making coding more accessible, efficient, and collaborative. The advantages are evident: increased coding speed, the capability to improve coding quality and reduce errors, as well as an opportunity to enhance overall productivity. It empowers developers to focus on higher-level problem-solving, creativity, and innovation, while routine and repetitive coding tasks are automated.

However, as with any powerful technology, there are considerations and challenges. Code quality, security, and potential biases must be carefully managed. Developers should exercise due diligence and human oversight to ensure that the generated coding suggestions align with project requirements and best practices.

Human assistance in the creation and development of code LLMs is a critical factor. These models are not standalone entities but are the result of extensive human involvement. The training process involves data supervision, labeling, and continuous fine-tuning by experts.

Prominent examples of generative AI coding tools include, for example, GitHub Copilot and TabNine, both of which are designed to assist developers in writing code more efficiently and with higher quality. These models have the potential to significantly streamline the software development process, making it faster, more accessible, and less error-prone. As AI technologies continue to evolve, we can expect even more innovative applications and tools to further enhance the developer experience.