Code refactoring is the process of restructuring existing computer code without changing its external behavior. The goal is to improve the nonfunctional attributes of the software. Think of it as cleaning up your workspace: while it doesn’t directly contribute to the completion of your tasks, it makes the process smoother, more efficient, and more enjoyable.
Code refactoring can be as simple as renaming a variable to make its purpose clearer or as complex as breaking down a monolithic system into a series of microservices. The goal is to improve the readability, efficiency, and maintainability of the code without interfering with its primary functions.
We’ll discuss the main principles of refactoring, show techniques and examples, and show how generative AI can be a game changer for teams faced with challenging refactoring projects.
Refactoring is not just about making changes to the code; it’s about making the right changes. There are several principles that guide this process, ensuring that our refactoring efforts are effective and beneficial.
1. Do no harm
The first and foremost principle of code refactoring is to “do no harm.” That means whatever changes we make to the code, the software’s functionality should remain consistent. We’re not adding new features or fixing bugs; we’re improving the code’s structure and design.
A developer working on refactoring should always ensure that their modifications don’t break the software or introduce new bugs. The software should behave the same way before and after the refactoring process.
2. Refactor in small steps
Another key principle of refactoring is to make small, incremental changes. It’s tempting to overhaul the entire codebase in one fell swoop, but that’s often a recipe for disaster. Instead, we should make one small change at a time, test it, and then move on to the next.
This approach reduces the risk of introducing new bugs and makes it easier to identify the cause if something goes wrong.
3. Maintain a robust suite of tests
A robust suite of tests is a critical tool for refactoring. Automated tests give us the confidence to make changes to the code, knowing that if we inadvertently break something, the tests will catch it. These tests serve as a safety net — they provide instant feedback, alerting us if our modifications have inadvertently changed the software’s behavior.
4. Refactor code on an ongoing basis
The final principle is to integrate refactoring into the regular development lifecycle. Refactoring should not be a one-off task, done only when the code becomes unmanageable. Instead, it should be a regular part of the development workflow, akin to tidying up our workspace at the end of each day.
This continuous refactoring approach helps prevent technical debt from accumulating and keeps the codebase clean and manageable.
While refactoring involves effort, time, and sometimes, a fair bit of courage, the benefits it brings are substantial.
One of the significant benefits of refactoring is improved code readability. A clean, well-structured codebase is easier to read, understand, and maintain. It’s like a well-organized library: finding the book (or code snippet) you need is quick and straightforward.
This improved readability benefits not just the original developer but everyone who works on the project. It makes onboarding new team members easier, facilitates code reviews, and makes the code more self-explanatory, reducing the need for external documentation.
Refactoring can also enhance software performance. By optimizing the code, we can make the software run faster, consume less memory, and respond more quickly to user inputs. This performance boost can lead to a better user experience, making the software more pleasant and satisfying to use.
Technical debt is the cost of postponing good development practices. Just like financial debt, it accumulates interest: the longer we leave it, the harder it becomes to pay off. Refactoring helps us reduce this technical debt, keeping our codebase clean and manageable.
A well-refactored codebase makes bug identification and resolution easier. When the code is clean and well-structured, it’s easier to spot anomalies and inconsistencies. Moreover, when each function or module is small and focused, it’s easier to isolate the problem and fix it.
Here are some of the common techniques used by developers to refactor code:
The extract method is a refactoring technique that involves breaking down a complex method into smaller, more manageable pieces. This technique is particularly useful when a method is performing multiple tasks, making it hard to understand. The extract method can improve the readability and maintainability of the code, making the refactoring process more straightforward and efficient.
The red/green/refactor technique is a fundamental part of test-driven development (TDD). It involves writing a failing test (red), making it pass by writing code (green), and then refactoring the code to improve its design (refactor). This technique can help ensure that your refactoring efforts do not negatively impact the functionality of the code.
Abstraction is a powerful technique that can greatly simplify the refactoring process. It involves hiding the details of complex code and providing a simplified interface. Abstraction can make the code easier to understand and maintain, reducing the complexity and making the refactoring process less daunting.
“Composing methods” refers to the practice of breaking down a method or function into a series of smaller methods, each of which performs a single task. This technique can help improve the readability and maintainability of the code, making it easier to refactor. Composing methods can also help identify and remove duplicate code, which can further streamline the refactoring process.
“Simplifying methods” refers to the practice of simplifying complex methods or functions to make them easier to understand and maintain. This can involve removing unnecessary code, simplifying complicated expressions, and replacing complex conditional logic with simpler constructs.
Preparatory refactoring is a technique that involves making small, incremental changes to the code to prepare it for larger changes. This can involve simplifying complex methods, removing duplicate code, or improving the design of the code. Preparatory refactoring can help make the larger refactoring task less daunting and more manageable.
Learn more in our detailed guide to code refactoring techniques.
Now let’s look at some practical examples of refactoring in commonly used programming languages:
Example 1: Renaming variables
The code below is functional, but it’s not user-friendly for other developers who may need to maintain or enhance the code later:
int a = 5; int b = 10; int c = a + b;
This code can be refactored by renaming the variables to make the code more readable and understandable:
int numberOne = 5; int numberTwo = 10; int sumOfNumbers = numberOne + numberTwo;
Learn more in our detailed guide to code refactoring in Java.
Example 2: Extracting methods
Another common refactoring technique in Java is method extraction. This is particularly useful when you have a long method that’s doing too many things:
public void printDetails() {
System.out.println("Name: " + name);
System.out.println("Age: " + age);
System.out.println("Address: " + address);
}
We can refactor the above code by extracting methods:
public void printDetails() {
printName();
printAge();
printAddress();
}
private void printName() {
System.out.println("Name: " + name);
}
private void printAge() {
System.out.println("Age: " + age);
}
private void printAddress() {
System.out.println("Address: " + address);
}
Example 1: Simplifying nested if statements
Python’s readability makes it easy to spot nested if statements that can be simplified:
def check_marks(marks):
if marks >= 60:
if marks >= 75:
return "Distinction"
else:
return "First Class"
else:
return "Fail"
This code can be refactored to reduce complexity and increase readability:
def check_marks(marks):
if marks >= 75:
return "Distinction"
elif marks >= 60:
return "First Class"
else:
return "Fail"
Example 2: Replacing temp with query
In Python, you can refactor your code by replacing temporary variables with a query:
def calculate_total(price, quantity):
total = price * quantity
return total
The refactored code might look like this:
def calculate_total(price, quantity):
return price * quantity
Example 1: Replacing magic numbers with named constants
In C, you can improve code readability by replacing “magic numbers” (which might be unclear to other developers) with named constants.
float calculate_area(float radius) {
return 3.14159 * radius * radius;
}
The refactored code might look like this:
#define PI 3.14159
float calculate_area(float radius) {
return PI * radius * radius;
}
Example 2: Extracting code into functions
Extracting code into functions is one of the most common refactoring techniques in C. For example, consider this code:
void print_details() {
printf("Name: %s\n", name);
printf("Age: %d\n", age);
printf("Address: %s\n", address);
}
It can be refactored as follows:
void print_name() {
printf("Name: %s\n", name);
}
void print_age() {
printf("Age: %d\n", age);
}
void print_address() {
printf("Address: %s\n", address);
}
void print_details() {
print_name();
print_age();
print_address();
}
Example 1: Removing dead code
Dead code is code that’s never executed. Here’s an example of dead code in C#:
public int CalculateTotal(int price, int quantity) {
int total = price * quantity;
int discount = 0; // This is dead code
return total;
}
Here’s the refactored code:
public int CalculateTotal(int price, int quantity) {
return price * quantity;
}
Example 2: Inlining temporary variables
In C#, you can refactor your code by inlining temporary variables:
public int CalculateTotal(int price, int quantity) {
int total = price * quantity;
return total;
}
Refactored code:
public int CalculateTotal(int price, int quantity) {
return price * quantity;
}
Here are some of the key challenges you might face when refactoring code:
Inadequate test coverage
Inadequate test coverage leaves your code vulnerable to bugs and other issues. Without comprehensive tests, there’s no safety net to catch potential errors that may arise during the refactoring process. The absence of robust tests can make refactoring a precarious endeavor.
Large codebase or legacy system
Huge codebases or legacy systems often contain spaghetti code, making them difficult to understand and even harder to refactor. If the codebase is large and complex, the risk of introducing bugs or breaking existing functionality is high, and the task can quickly become overwhelming.
Lack of documentation
A common challenge in code refactoring is the lack of adequate documentation. When the original intent, design decisions, and functionality of the code are not well-documented, understanding and modifying the code becomes significantly harder. This lack of documentation can lead to misinterpretation of the code’s purpose, increasing the risk of introducing errors during refactoring.
Difficulty in estimating time
Finally, estimating the time required for refactoring can be a significant challenge. The complexity of the codebase, the scope of the changes, and the level of test coverage are just some of the factors that can affect the time needed for refactoring.
Despite the challenges, these best practices can help you succeed in your code refactoring efforts:
Plan your refactoring project and timeline carefully
Refactoring requires a well-considered plan and timeline. You need to understand the scope of your refactoring project, set realistic goals, and allocate sufficient time and resources to achieve those goals.
The planning phase involves identifying parts of your codebase that need refactoring, establishing the order in which you’ll tackle them, and estimating the time each task will take. You also need to factor in potential setbacks and build some flexibility into your timeline.
Review dependencies
Before you begin refactoring your code, you need to understand the dependencies within your codebase. Dependencies can be a major source of complexity and can make your refactoring process more challenging. By mapping out your dependencies, you can discover opportunities to simplify your code by eliminating unnecessary dependencies or consolidating related ones.
It’s even more important to update dependencies to the latest versions, apply security patches, and remove dependencies that are no longer maintained by their creators. This is critical for improving the security of legacy software but should be done with care to address breaking changes in new versions of the dependencies.
Reuse the existing tech stack
To ensure efficient and effective refactoring, it’s crucial to reuse the existing tech stack as much as possible. This approach minimizes the learning curve for the development team and leverages existing knowledge and resources.
By focusing on the existing technologies, teams can identify underutilized features or functions that could be better exploited, leading to a more streamlined and coherent codebase. This practice also helps maintain consistency across the project and reduces the risk of introducing new bugs or compatibility issues associated with new technologies.
Focus on progress, not perfection
It’s easy to get caught up in trying to make the code perfect, but this can lead to endless refactoring that doesn’t add value to your software.
Instead, aim for incremental improvements. Each small change you make contributes to the overall quality of your code, making it more readable, maintainable, and flexible. Remember: refactoring is a continuous process, and you can always make further improvements in the future.
Use refactoring tools
Refactoring can be a tedious and time-consuming process, but fortunately, there are tools available that can make your job easier. Refactoring tools automate some of the repetitive tasks involved in refactoring, reducing human error and speeding up the process.
These tools can help you identify parts of your code that need refactoring, suggest improvements, and even perform some refactoring tasks automatically. They can also provide insights into your code’s structure and dependencies, assisting you in making informed refactoring decisions.
Given the challenges of code refactoring, recent advances in generative AI can be a big help to development teams. Tabnine is an AI coding assistant that can predict and generate code completions in real time and provide automated code refactoring suggestions, which are sensitive to the context of your software project.
Tabnine integrates with your integrated development environment (IDE). As you type in your IDE, Tabnine analyzes the code and comments, predicting the most likely next steps and offering them as suggestions for you to accept or reject.
Tabnine utilizes a large language model (LLM) trained on reputable open source code with permissive licenses, StackOverflow Q&A, and even your entire codebase (Enterprise feature). This means it generates more relevant, higher quality, and more secure code than other tools on the market.
Tabnine is the AI coding assistant that helps development teams of every size use AI to accelerate and simplify the software development process without sacrificing privacy, security, or compliance. Tabnine boosts engineering velocity, code quality, and developer happiness by automating the coding workflow through AI tools customized to your team. Tabnine supports more than one million developers across companies in every industry. With more than one million monthly users, Tabnine typically automates 30-50% of code creation for each developer and has generated more than 1% of the world’s code.
Unlike generic coding assistants, Tabnine is the AI that you control:
It’s private. You choose where and how to deploy Tabnine (SaaS, VPC, or on-premises) to maximize control over your intellectual property. Rest easy knowing that Tabnine never stores or shares your company’s code.
It’s personalized. Tabnine delivers an optimized experience for each development team. It’s context-aware and delivers precise and personalized recommendations for code generation, code explanations, and guidance, and for test and documentation generation.
It’s protected. Tabnine is built with enterprise-grade security and compliance at its core. It’s trained exclusively on open source code with permissive licenses, ensuring that our customers are never exposed to legal liability.
The Tabnine in IDE Chat allows developers to communicate with a chat agent in natural language and get assistance with various coding tasks, such as:
Onboarding Agent for Tabnine Chat
Tabnine Onboarding Agent helps developers onramp to a new project faster. For developers who are new to an organization or existing developers who are new to a project, the Onboarding Agent provides a comprehensive overview of key project elements, including runnable scripts, dependencies, and overall structure to help them get up to speed effortlessly.
Learn more how to use Tabnine AI to analyze, create, and improve your code across every stage of development:
Try Tabnine for free today or contact us to learn how we can help accelerate your software development.
We’re excited to announce another leap forward in the performance and capability of the Tabnine AI coding assistant: highly personalized recommendations for every developer through local code awareness, and recommendations tailored to engineering teams through integrations with their global codebase.
Tabnine can now use the context of our users and enterprise customers to deliver more precise and more personalized recommendations for code generation, code explanations, and guidance, and for test and documentation generation. Starting today, you can increase Tabnine’s contextual awareness by enabling it to become aware of your environment — from a developer’s local IDE to the entire code base — and receive code completions, explanations and documentation that are specifically tailored to you and your engineering team.
Notably, Tabnine continues to stay true to our values and delivers these highly personalized recommendations without compromising our customers’ privacy. We continue to commit to advanced encryption and zero data retention for our SaaS users and offer company codebase awareness within our private, customer-deployed product. By significantly improving the quality of the output, these updates will help engineering teams further accelerate and simplify software development without sacrificing the security of legal compliance.
With code awareness, Tabnine takes into account the relevant parts of your project (e.g., existing APIs, frameworks, and patterns) and provides results that are more accurate and more specific to you.
For example, Tabnine will tell you how to connect to a database in a way that’s used for your project instead of connecting to any available database or connecting to a database in a different or generalized way.
For example, if you have an app to book flights, you can ask Tabnine to generate code that validates the flight data based on the logic within that specific app.
Generate code that aligns with the syntax and semantics of elements in your project, thus reducing potential hallucinations. Check code relative to project-specific logic. Be consistent with style and coding patterns when generating or completing code.
Generate a test for function X using the structure, style, and syntax used for the test of function Y.
In the past few years, AI coding assistants have gone from a “nice to have”’ for savvy developers to a “must have,” being implemented by engineering managers across their teams as they’ve proven to increase developers’ productivity, efficiency, and satisfaction. Early results from adopting AI software development tools have been promising, but there’s still significant room to improve, especially when it comes to continuing to improve the quality of the responses in the context of what the user is asking.
The LLMs that each of the AI coding assistants uses have some inherent limitations (even an engineered-for-purpose coding model like Tabnine’s). By design, these LLMs are universal; although they’ve been trained on vast amounts of data and contain billions of parameters, they’re not generally aware of the specific code and distinctive patterns of an individual organization. This lack of context and domain-specific knowledge likens their recommendations more to a skilled software engineer off the street rather than a deeply experienced engineer who is familiar with how an organization works. The result is that the recommendations from AI coding assistants, while accurate, often aren’t specifically tailored to an individual developer’s needs.
Just like you need context to intelligently answer questions in real life, coding assistants also need context to intelligently answer questions. Contextual awareness augments the LLMs by providing the subtle nuances that make a developer and organization unique. Tabnine achieves this context awareness in two ways:
In addition to the personalization of the AI assistant through the new capabilities, Tabnine continues to offer model customization to further enrich the capability and quality of the output. Enterprise engineering teams can benefit from a custom “Tabnine + You” model that fine-tunes our universal model with a customer’s own code.
The personalized recommendations from Tabnine don’t come at the expense of giving away access or control over your proprietary code and user data.
To achieve personalization, Tabnine uses retrieval-augmented generation (RAG) to provide the Tabnine AI coding assistant with knowledge of your organization’s specific characteristics and code. RAG is widely used to improve AI performance and not only reduces LLM hallucinations but also helps to overcome the inherent limitations of training data. Continuing to honor our commitment to your privacy, any information Tabnine uses to support RAG remains exclusively yours. Tabnine commits to zero retention or sharing of any customer data, ensuring your privacy at all times.
Here’s a brief description of how we handle data and maintain privacy.
Check out the Docs for more details on our data privacy standards.
There’s no additional cost to get these new personalized results from Tabnine. Every Tabnine Pro user is already benefiting from local code awareness. Tabnine Enterprise users can follow these instructions to activate local code awareness for their teams and can request access to the private preview of global code awareness by contacting their sales representative or our support team.
Sign up for our webinar to see this new level of personalization in action or check out the Docs to learn more. If you’re not yet a customer, you can sign up for Tabnine Pro today – it’s free for 90 days.
The hand wringing around artificial intelligence killing off jobs is likely overhyped.
But before we debate the impacts of AI, we have to first agree on what AI even is.
The term “AI,” as it is most often used today, is really just a software machine that analyzes data and predicts what’s likely based on that data. Generative AI, popularized by ChatGPT, identifies patterns in data to create new content, such as software code, essays, images, and art.
While it is remarkable to see a text prompt generate content seemingly out of nowhere, it’s just an acceleration of the software automation that’s been underway for 30+ years. The machine is not creating something wholly new. It responds to your request by interpolating content it has seen in training data and delivering it in a new form.
This might feel like an act of creation, but only in the basest sense. Like an apprentice repeating the efforts of a craftsman, the machine offers back a product that reflects only what we have told it and in a way that we directed it to deliver — potentially hallucinating to fill in gaps for which it does not have sufficient information. Automation has moved well beyond a machine stamping out duplicates, but this is still more diode than Da Vinci.
Why does this nuance matter? Because it gets to the heart of the future of AI and work.
The large language models that power tools like ChatGPT have historically relied on an understanding of the structure and rules of the languages they attempt to emulate. And the languages with the clearest structure and rules? Programming languages.
In addition, it is much harder to review the correctness and accuracy of natural language, whereas code is more easily validated; you can confirm it does what you want as an executable and you can validate it through syntax checks. Programming languages also don’t come with the risks of bias, toxicity, and the subtlety of meaning.
Given all of this, it is no surprise that the first knowledge worker role to see generative AI-powered assistants emerge was software development.
My company, Tabnine, created its first AI-based software coding assistant in 2018. It started with the simple ability to predict the code a developer was typing and offer suggestions, but evolved to generate whole functions, highlight bugs and defects, explain someone else’s code, and even generate documentation and tests. AI coding assistants are estimated to be in use by over 2.5 million developers. They typically automate 30%-50% of code generation and cut task completion time for developers in half, as noted by a McKinsey study. With more than five years of history and 5%-10% of l developers using this class of tools, we are starting to see how generative AI can transform a role that was previously perceived as immune to automation.
So is AI killing software development jobs? Not even close.
Automation from AI-enabled software development tools have thus far reduced low-level tasks and automated mundane and repetitive tasks, enabling developers to do more. There is usually initial doubt amongst developers in adopting AI coding assistance, but that doubt typically transforms into an appreciation of the acceleration that AI brings to the role. Studies have shown that developers who use AI coding assistants report significantly higher job satisfaction, an ability to stay “in flow” longer, and have greater mental energy to apply toward more strenuous tasks. They also report being able to complete tasks that they would have failed at before – as much as 10% more.
This research potentially points to a pattern that looks to be emerging around AI tools augmenting knowledge workers in all kinds of jobs and industries. AI automation’s ability to help workers shift time from low-level grunt work to higher order thinking and creativity can create greater value for employers and more meaningful work for workers. As Turing Award-winning computer scientist Donald Knuth stated, “Some tasks are best done by machine, while others are best done by human insight; and a properly designed system will find the right balance.”
This new level of automation could not come at a better time. Job growth for software developers, quality assurance analysts, and testers is projected to grow 25% from 2022 to 2032, the U.S. government says. No way we will produce 25% more skilled workers to fill those jobs in the same timeframe—or even get them from other countries.
A survey by GBK Collective found that 78% of companies expect to use AI for software development within three to five years. Freshworks estimates that U.S. companies could save over $15,000 per IT employee each year with AI automating repetitive tasks. An IDC study found that nearly 40% of IT executives said generative AI “will allow us to create much more innovative software.” Contrary to replacing jobs, large enterprises reported to IDC that they expect AI to help them overcome the shortage of skilled developers and operators.
Bottomline: AI as machine-driven automation increases productivity, which drives more innovation and, ideally, stronger economies. The kid who worked the neighborhood with a push lawnmower can now work two neighborhoods with an electric one. More earnings. More spending.
This productivity boost is desperately needed, as the shortfall of workers in tech has been mirrored in countless other roles. We need automation to continue enjoying the comforts of modern society and to grow GDP — it’s the only way to meet the labor force needs as boomers retire and birth rates decline.
The current advancements in AI differ from automation in the past, in which machines replaced human work that was often physical. This time, AI (particularly Generative AI) will impact more white collar work. Some jobs will go but others will be created in their place.
As the McKinsey Global Institute states, “we see generative AI enhancing the way STEM, creative, and business and legal professionals work rather than eliminating a significant number of jobs outright. Automation’s biggest effects are likely to hit other job categories. Office support, customer service, and food service employment could continue to decline.”
Of course, it hurts individuals when jobs go away. Certain types of workers will suffer more. Then again, surprises occur when technology changes the world. When ATMs arrived, everyone predicted the death of the bank teller. Not so, As Reddit explains, the rise of ATMs meant fewer tellers per branch. But because it became cheaper to open branches, more branches arose and so did teller jobs.
The roles that will continue to be in high demand are those rooted in the act of creation. And not just artists and writers, but anyone who knows how to turn an idea into something new. As we learned in software development, the machine can show you options, fill in the gaps, and point you towards the right answers, but it can not independently create a new application or business. AI lacks both ideas and innovation. Use generative AI well and it’s like putting on a suit that amplifies your speed and agility. Use AI to create something you do not have a vision for, nor truly understand, and it will create gibberish.
Humans and machines differ, and they always will. “The question of whether AI will replace human workers assumes that AI and humans have the same qualities and abilities — but, in reality, they don’t. AI-based machines are fast, more accurate, and consistently rational, but they aren’t intuitive, emotional, or culturally sensitive. And, it’s exactly these abilities that humans possess and which make us effective,” writes AI expert, author and professor David De Cremer and Garry Kasparov, in the Harvard Business Review.
The authors conclude that AI will augment human intelligence, not replace it. I couldn’t agree more, and that’s why I think hand wringing should really be replaced by high fives.
We’re excited to announce a strategic partnership with DigitalOcean to deliver Tabnine’s AI coding assistant to startups and small and medium-sized businesses (SMBs) across the world. Starting today, all DigitalOcean users can purchase Tabnine’s Pro plans for themselves and their engineering teams directly from their DigitalOcean account for consolidated billing and account management. Additionally, we are providing an introductory offer that gives 25% discount on Tabnine Pro monthly pricing to all DigitalOcean users. This makes it easier for startups and SMBs to leverage AI code completion and AI chat agents in their development workflows.
As the originator of the AI coding assistant category, Tabnine’s mission has always been to accelerate software development through AI for engineering teams of every size. DigitalOcean is the leader in cloud computing for startups and SMBs, and this partnership makes it easier for these smaller teams to benefit from generative AI. Every DigitalOcean customer can now accelerate and simplify the entire software development life cycle without sacrificing privacy, security, and compliance.
With this partnership, DigitalOcean customers get access to Tabnine Pro plans for one user to up to 10 users. To get started, simply choose the Tabnine plan that works best for you and click the ‘Add Tabnine’ button in your control panel.
Tabnine serves more than one million developers, generating more than 1% of the world’s code, and typically automates 30–50% of code generation. We support all the major IDEs and languages. Thus you can expedite the building of your app using Tabnine and deploy it easily on DigitalOcean’s platform via Droplets (cloud VMs), DigitalOcean Kubernetes, or App Platform (fully managed platform-as-a-service). Tabnine’s efficiency coupled with the simplicity of DigitalOcean is bound to unleash developer productivity among startups and SMBs.
We can’t wait to see what you’ll create with DigitalOcean and Tabnine. You can sign up for the Tabnine plans here and check out the Docs to learn more. If you’d like to chat about using Tabnine in your business or want to deploy Tabnine in your secure environment (on-premises or on VPC), please contact our Sales team.
We’re pleased to announce that Tabnine has successfully achieved compliance with the General Data Protection Regulation (GDPR), marking a significant milestone in our commitment to data protection and privacy. This accomplishment underscores our dedication to transparency, security, and, most importantly, our customers’ trust in our services.
At Tabnine, we recognize that privacy and security are inseparable. We’re proud to have implemented robust measures to ensure the highest standards of data protection and privacy for our users. As we embark on this GDPR-compliant journey, we’re reaffirming our commitment to prioritizing the confidentiality and security of your information.
GDPR is a comprehensive set of data protection and privacy laws established by the European Union (EU). Enforced since May 25, 2018, GDPR is designed to empower individuals with greater control over their personal data. It applies not only to EU-based organizations but also to those outside the EU that process the personal data of EU residents.
Over the past few months, we’ve diligently worked towards official compliance with the GDPR. This extensive process involved a thorough review and update of Tabnine’s Terms of Use and Privacy Policy. Additionally, we have formalized internal policies, introduced new processes, and implemented various changes throughout our services, such as the introduction of a new cookies policy. Look at Tabnine’s Trust Center for full details on our GDPR compliance.
By achieving GDPR compliance, Tabnine users and customers can be confident that their data is being handled responsibly. This compliance not only reinforces the trust you place in us but also positions Tabnine as a reliable partner for European companies and global organizations with employees in the EU.
Thank you for entrusting us with your information. Tabnine remains dedicated to delivering excellence in service and ensuring the highest standards of data protection. We look forward to continuing to serve you with the transparency, security, and privacy that you deserve.
AI has made significant strides in recent years, automating numerous aspects of our lives, from self-driving cars to voice assistants. Among these advancements, OpenAI’s ChatGPT has astounded the global community with its uncanny ability to generate human-like text and code based on natural language prompts. This leads many to wonder if ChatGPT or similar AI tools could soon replace human programmers.
While the prowess of AI like ChatGPT is undeniable, understanding the nuances of its functioning, its potential, and its limitations will give us a clearer perspective on its role in programming. This article explores its capabilities and limitations as a programming assistant, following its evolution from its initial release to the more sophisticated version powered by GPT-4. We’ll compare traditional programming practices with coding through ChatGPT, and address the critical question: Is it a useful tool to enhance productivity, a potential tutor for beginners, or could it render human programmers obsolete?
This is part of a series of articles about ChatGPT alternatives.
ChatGPT is an online AI-based chatbot. It was initially based on generative pretrained transformer 3.5 (GPT-3.5), an AI language model developed by OpenAI. It’s trained on a wide variety of text from the internet. But unlike most AI models, which are trained to perform a specific task, GPT-3.5 is a generalist. It has the power to answer questions, write essays, summarize texts, translate languages, and even generate code in any programming language.
The secret sauce of ChatGPT lies in its architecture. The core architecture of ChatGPT is a deep learning model known as a transformer. This model leverages the power of attention mechanisms, which allows it to focus on different parts of the input when generating each word in its output. This architecture provides the model with a robust ability to understand the context and relevance of words in a sentence.
GPT-3.5 has an astounding 175 billion parameters, which are a bit like synthetic brain cells, each making a tiny contribution to the output. With this massive number of parameters, ChatGPT can generate impressively coherent and contextually rich responses, navigating nuanced conversations and delivering detailed information on a myriad of topics.
OpenAI released GPT-4 in April 2023, and ChatGPT can now be used both with the older GPT-3.5 and the newer GPT-4 model. While there are not many details available about the architecture and number of parameters in GPT-4, it’s a far more capable model. According to OpenAI, it was designed to be “more reliable, creative, and able to handle much more nuanced instructions.” A primary innovation of GPT-4 is that it enables larger prompts, between 8,192 and 32,768 tokens in length. (A token is roughly equivalent to four English language characters.)
Since its launch, a primary use case of ChatGPT has been to generate code. Let’s explore the main steps in the evolution of the service as it pertains to code generation.
ChatGPT showcased its initial capabilities in code generation in version 3.5. Using its underlying language understanding and text generation abilities, it could generate simple code snippets based on textual prompts. For example, if you asked it to write a Python function to calculate the factorial of a number, it could do that.
However, these capabilities were somewhat limited. The generated code was often straightforward and simplistic, and while it could handle basic coding tasks, it struggled with more complex problems. It also often required exact, clear, and explicit instructions to generate the correct code.
ChatGPT’s potential for code generation took a leap forward with the release of version 4. This update introduced more advanced code generation capabilities, allowing the model to handle more complex coding tasks with better precision. It could generate longer and more complex code snippets, and its comprehension of programming concepts improved.
Despite these improvements, ChatGPT-4 is still not perfect. It sometimes generates incorrect or inefficient code and can struggle with ambiguous or poorly defined prompts.
To further enhance the code generation capabilities of ChatGPT, OpenAI introduced a code interpreter plugin. This plugin allows ChatGPT to interpret and analyze existing code, which users can upload as files of any format. This can help it generate more accurate and efficient code and perform other advanced tasks.
The code interpreter plugin works by analyzing the input code and providing context to ChatGPT, allowing it to better understand the task at hand. This can result in more accurate and efficient code generation, but it’s still not perfect. The plugin might struggle with more complex tasks or situations where the context is ambiguous or unclear.
One of the initial drawbacks of using ChatGPT for coding is that it’s not natively integrated with most integrated development environments (IDEs). While it can generate code, programmers will have to copy the code and paste it into their IDEs manually. This lack of direct integration can lead to inefficiencies in the coding process, particularly in complex projects.
Another significant limitation is the issue of code privacy. Given that ChatGPT generates its outputs based on the inputs it receives, there’s a risk that sensitive code could potentially be exposed to third parties. To illustrate the risk, OpenAI has already experienced a data breach where more than 100,000 ChatGPT accounts and their data were stolen by attackers.
While ChatGPT can generate code, the quality and security of that code are not guaranteed. The AI bot is not currently equipped with the ability to test the code it generates for potential bugs, vulnerabilities, or inefficiencies. This limitation puts an additional burden on programmers to thoroughly check and verify the code generated by the AI, negating some of the time-saving benefits of the tool and introducing risks in case programmers do not diligently check output.
In addition, ChatGPT cannot operate in an isolated environment. For example, there’s no way to run it in your on-premises data center or inside a virtual private cloud (VPC). This significantly increases the security risk while working on sensitive, proprietary codebases with ChatGPT.
Finally, while ChatGPT might seem like a great tool for junior programmers, it might not be the best tutor. Given its limitations and inability to provide context, using ChatGPT could potentially lead to adopting bad coding practices. Junior programmers must learn the fundamentals of coding from experienced human mentors who can provide context and guidance before relying on code from tools like ChatGPT.
In traditional programming, the responsibility of checking for errors falls squarely on the programmer. We have to meticulously go through our code, line by line, to ensure there are no syntax errors, logical errors, or runtime errors.
In ChatGPT, error checking is still necessary because the tool is not able to test its code, nor does it understand the full context in which the code will be used. However, it can be more challenging to test the code, because the code was generated by AI and might use logic or structure that is unclear to the human tester. Programmers could also fail to check the output, treating ChatGPT output as reliable, tested code, which can lead to serious consequences.
When it comes to interactivity, ChatGPT has a clear edge. Traditional coding is a solitary process, often involving long hours spent alone in front of a computer. ChatGPT, on the other hand, allows for a more interactive experience. You can communicate with it, ask it questions, and get instant responses. This can make the coding process more engaging and less isolating.
However, there’s a flip side to this interactivity. While ChatGPT can respond to queries, it cannot truly understand or think like a human. It can’t provide the kind of creative problem-solving or unique insights that a human programmer can. So, even though it’s more interactive, it can’t replace the human element in programming.
Completeness, in the context of programming, refers to the ability to create a fully functioning piece of software from start to finish. Traditional programming requires a deep understanding of coding languages, algorithms, data structures, and more. It requires careful planning, rigorous testing, and a lot of time and effort.
ChatGPT’s ability to generate code could potentially simplify this process. It could automate certain tasks, generate boilerplate code, or even write entire parts of an application. However, it’s important to remember that ChatGPT lacks the human ability to understand the bigger picture, to plan for contingencies, or to foresee potential issues. So, while it can contribute to the completeness of a project, it cannot achieve it on its own.
Programming knowledge is a must-have in traditional coding. A programmer needs to understand the syntax and rules of the language they’re using, as well as problem-solving and logical thinking skills.
With ChatGPT, users don’t necessarily have to be skilled programmers. The AI model can generate code based on natural language input, making coding more accessible to nonprogrammers. However, this can be misleading, because without a strong grasp of the programming language, users cannot test and verify the quality and suitability of the code. So in reality, using ChatGPT without sound programming knowledge is not recommended.
AI tools like ChatGPT can certainly aid programmers in their work. They can automate repetitive tasks, generate boilerplate code, and even assist in debugging by suggesting potential solutions. This can save programmers a significant amount of time, allowing them to focus on more complex tasks that require human creativity and intuition.
Furthermore, AI tools like ChatGPT can be used as a learning aid. They can help beginners understand the syntax and structure of code, provide examples, and answer common coding questions. This can make the learning process more efficient and enjoyable, and illustrates how AI can be used to enhance human capabilities, rather than replace them.
But while AI tools like ChatGPT can assist programmers, they have their limitations. Let’s delve into why AI can’t fully replace a programmer’s role.
While AI has made significant strides in understanding and generating human-like text, it still has a long way to go before it can fully replace a human programmer. There are several reasons for this:
While ChatGPT, or any AI tool, is unlikely to replace programmers anytime soon, AI code assistants are becoming an essential part of the development process. Organizations realize this potential and are keen to provide AI coding assistants to boost productivity. However, most organizations will find consumer-oriented products like ChatGPT are not a good fit. This is where enterprise-grade AI solutions like Tabnine come into the picture.
The Tabnine in IDE Chat allows developers to communicate with a chat agent in natural language and get assistance with various coding tasks, such as:
Onboarding Agent for Tabnine Chat
Tabnine Onboarding Agent helps developers onramp to a new project faster. For developers who are new to an organization or existing developers who are new to a project, the Onboarding Agent provides a comprehensive overview of key project elements, including runnable scripts, dependencies, and overall structure to help them get up to speed effortlessly.
Learn more how to use Tabnine AI to analyze, create, and improve your code across every stage of development:
Tabnine is the AI coding assistant that helps development teams of every size use AI to accelerate and simplify the software development process without sacrificing privacy, security, or compliance. Tabnine boosts engineering velocity, code quality, and developer happiness by automating the coding workflow through AI tools customized to your team. Tabnine supports more than one million developers across companies in every industry.
Unlike generic coding assistants, Tabnine is the AI that you control:
It’s private. You choose where and how to deploy Tabnine (SaaS, VPC, or on-premises) to maximize control over your intellectual property. Rest easy knowing that Tabnine never stores or shares your company’s code.
It’s personalized. Tabnine delivers an optimized experience for each development team. It’s context-aware and delivers precise and personalized recommendations for code generation, code explanations, and guidance, and for test and documentation generation.
It’s protected. Tabnine is built with enterprise-grade security and compliance at its core. It’s trained exclusively on open source code with permissive licenses, ensuring that our customers are never exposed to legal liability.
Whether you’re choosing an AI coding assistant to make your life easier as an individual developer, or choosing a tool to deploy out to your entire engineering team to improve the effectiveness and satisfaction of your team, it’s critical to evaluate potential vendors holistically:
Only Tabnine meets all of these requirements expected by enterprise engineering teams and has the history and scale of developer adoption to prove it.
Since launching our first AI coding assistant in 2018, Tabnine has pioneered generative AI for software development. Tabnine boosts engineering velocity, code quality, and developer happiness by automating the coding workflow through AI tools customized to your team. With more than one million monthly users, Tabnine typically automates 30–50% of code creation for each developer and has generated more than 1% of the world’s code.
Try Tabnine for free today or contact us to learn how we can help accelerate your software development.
