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Last Updated: November, 15 2024
Since the advent of ChatGPT, the AI space has been touted as the next frontier to boost human productivity. But most consumer-grade generative AI solutions only address basic requirements such as text and image generation. This raises the question about the viability of using AI development tools during the software development lifecycle (SDLC).
Thankfully, there have been multiple studies that have explored the interactions of AI solutions within software development processes. One such study from SSRN states that 80% of tech respondents use generative AI for daily tasks already. The rapid adoption of AI by tech professionals highlights a growing trend of using AI to generate code and documentation.
This trend is catered to by a whole bunch of Independent Software Vendors (ISVs) and startups that have built artificial intelligence and machine learning solutions to revolutionize the development atmosphere. But are these dedicated AI solutions any good? The answer to this question is a resounding yes, and to showcase their prowess, we have compiled this list of the top 10 best AI development tools.
The common use case for AI tools across all industries is primarily the automation of manual processes. This holds true even in the context of software development, where the core focus is to automate stages such as testing and deployment. Since software vendors have already developed so many AI solutions for each problem, we can narrow down the key use cases where AI is best suited in software development.
The major AI model types such as differentiation, generative and predictive models can all be used within the programming and deployment stages. Now let us explore how these model types serve different use cases as AI software development tools.
Just when the GPT-3 API was launched, several gimmicky AI solutions were introduced that served little to no purpose. This is why it is important to identify high-quality AI solutions to trust your project’s needs with. Below is our list of what we believe are the top 10 best AI tools for developers to use based on their functionality and reliability.
Tabnine is an AI code completion assistant that ramps up the speed of programming for most developers by automatically completing any code that it detects being written. It does this by analyzing the past lines of code and deciphering the possible functions that are involved. Based on this predictive analysis, it can present entire lines of code that may fit well.
One feature that makes Tabnine unique in the space of AI coding assistants, is the fact that it supports a wide variety of programming languages and frameworks, rather than being limited to popular languages such as C++ or Swift. The support for various programming languages is seen as an excellent advantage of using Tabnine.
Tabnine runs on proprietary AI models but also provides users the option of using public AI models such as GPT-4o or Claude 3.5. They advise users to only use these other AI models if they are comfortable sharing their code data with third parties such as OpenAI or Anthropic as their AI models are closed and thus rely on the active transfer of data to regional servers.
Snyk is an amazing AI-powered tool for ensuring your application’s resiliency. Its primary purpose is to analyze your entire application’s architecture to detect any dependencies between components or code blocks. This allows you to evaluate if these dependencies might result in catastrophic failure if even a single component breaks down.
Since Snyk operates all the time and keeps an eye on your application, you can ensure graceful degradation at all times, even when you are in the middle of writing code. Once Snyk detects any dependencies, it will automatically suggest code fixes to remedy the issue. This constant vigilance during the development process gives teams immense confidence when deploying the application as most of its vulnerabilities have already been resolved.
Additionally, Snyk is powered by a hybrid AI model where both symbolic and generative AI models are used in tandem with other ML technologies. What this means is that the symbolic AI algorithms can create strict boundaries for code dependency tolerances, and make the generative AI output adhere to the same standards to prevent any chances of AI hallucinations.
Pro Tip
Snyk has tremendous integration capabilities especially for DevOps cycles due to its seamless integration into popular CI/CD pipelines such as the AWS CodePipeline and GitHub Actions. Just check Snyk’s CI/CD integrations list to check if your pipeline of choice is compatible for instant integration.
Applitools solves one of the trickiest challenges within application testing, which is detecting issues with the user interface on the visual level. Most testing-related AI tools are usually oriented to detect discrepancies within code. However, Applitools works in a totally different manner where it uses computer vision AI models instead.
Using its image classification technology, Applitools is quickly able to spot any missing or repetitive elements within your web page or application. Such machine learning-powered recognition features are especially important if you are building applications for multiple devices and browsers. As users shift between different devices, your application’s design needs to remain consistent.
Applitools allows you to test configurations for multiple screen resolutions for this very reason. You can test your application’s presentation across multiple devices such as smartphones and desktops, and also different browsers. Based on its findings of visual discrepancies, it can suggest various changes to the alignment of elements and overall design to be cohesive with other layouts.
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Harness is an automatic software delivery solution which takes a lot of stress away from the deployment process for any software development operation, especially for CI/CD pipeline-based cycles. Much like other popular software development tools for deployment, Harness allows users to schedule deployments across all the three stages which are production, staging and development.
But the special twist here is AI-driven validation where Harness uses machine learning algorithms to verify the success of each deployment with new testing parameters. It integrates with popular monitoring tools such as Prometheus to allow developers access to key performance metrics at whim. But users can also enable role-based access control systems to limit who has access to application metrics.
Harness offers smart triggers where deployments can be automatically initiated based on specific actions such as code commits, scheduled timings or testing clearance. However, users need not worry about faulty deployments as Harness also supports automatic rollback and rollforward capabilities when it detects a malfunctioning update. To track which actions caused the faulty update, managers can access audit trails where they can view the interaction history.
Keeping track of deliverables during meetings and staying on top of details is a complex task that requires tremendous multitasking abilities. Project managers often have the trouble of missing out on contextual details and information getting lost in the noise. Otter.ai is your solution to fix these troubles by keeping track of all the details during a client call or work conference.
While seemingly simple, Otter.ai is a powerful AI-powered transcription tool that transcribes all dialogue into text. The AI model that is used to power Otter.ai can fetch highly accurate transcriptions with its natural language processing algorithms along with speaker identification. Since it can effortlessly decipher natural language, it can also automatically highlight any keywords that may have been used by particular speakers.
Users can add custom terminology and slang to improve the accuracy of this tool in nuanced situations. Otter.ai is also ready for integration into any of the popular communication platforms such as Slack, Microsoft Teams, or Skype, so that it can autonomously join in scheduled calls and start transcribing the conversation. Even if you cannot transcribe in real time, you can upload audio files to gain the same results.
Perhaps one of the best AI-powered tools for application monitoring, New Relic AI combines the powers of generative artificial intelligence with their proprietary telemetry analysis systems. The AI tool does this by integrating with your application and studying all telemetry and interactions. Once this is done, project managers or any other user can instantly gain access to all the insights on application performance.
The user simply has to input their desired data or metrics into the tool’s chat window and it will scout all application logs to find the data you need. For instance, project managers can input the history of deployment changes to figure out which instance caused a bug within a testing environment.
New Relic AI also sets dynamic baselines for your application autonomously to observe any drastic changes in performance that deviate from the usual benchmarks. Once any such deviations are detected, it immediately notifies the concerned users to help curb the performance issues. All of these AI features can prove to be vital in helping your application stay resilient against any malfunctions.
Figstack is an incredibly helpful tool for novice developers as it can accelerate your learning journey greatly while helping you write entire code snippets. Whenever you are working on any project, it can easily help you explain code and translate the function of any highlighted line of code in simple language. This helps you learn everything there is to know about each and every line of code without being proficient in the language.
Figstack also fosters cross-department collaboration by allowing you to share these code explanations with other users. This can help developers who are unfamiliar with any technology understand the logic and engineering behind multiple code snippets instantly.
Additionally, Figstack is extremely useful for projects where two or more programming languages are involved, such as in the case of multi-platform applications. This is because Figstack also has a code translation feature that can translate code from one programming language into another. For instance, code written in Python can easily be translated into Ruby without any manual effort.
AI tools have always been in production to specifically address the issue of crafting accurate documentation for custom software solutions. Mintlify takes this a step ahead with their AI tool where they can not only generate documentation but also provide users with additional context for the same. This tool can be used for the entire architectural documentation of the app or even just simple API documentation.
Mintlify provides on-the-go editing powers when you’re composing the document to make it more readable for the end user. This AI tool can also be readily integrated into GitHub for instant deployment of documentation without any major hassle.
As mentioned before, Mintlify also provides dedicated chat windows for readers within the platform to give them additional context and information about each code block. This is all done through the GPT-4 AI model from OpenAI which powers the tool. Developers can also access analytics for each and every document which shows how users have interacted with the page.
Cursor is a top-of-the-line editing tool with code completion capabilities that actively give you autocomplete suggestions as you type code. Simply pressing the Tab key will execute this function and complete the code snippet for you. This makes it one of the best AI tools for developers as it helps accelerate the development process while reducing the risk of faulty code lines.
Once you have written down your lines of code, Cursor can perform code reviews to suggest any code fixes or additional code snippet optimizations that may be required. Whether you are working on Python code or CSS does not matter as these AI coding tools support various programming languages to suit all of your needs. If developers find that a programming language of choice is missing, they can add additional plugins to Cursor to enable support.
Within their platform, they also support collaboration with windows where multiple users can work on the same codebase without needing any separate tools. It can also maintain a consistent version history by integrating with Git. This way, users can also access commit history, and resolve any conflicts that are caused due to changes in the codebase.
Grit.io is an excellent addition in terms of AI tools for developers looking to migrate applications. It solves almost all of the issues with software migration for software developers starting right from dependency identification to continuous delivery. It uses its machine learning algorithms to first identify the structure of the source application to detect interactions and any dependencies. Based on this, it will assess all of the complexities and complications that may arise during the migration process.
Once all of these factors have been documented, Grit.io creates an entire migration strategy which outlines data formatting and component configurations to ensure successful migration. It creates a risk profile as well, which dictates all of the potential risks of the migration process. This same AI tool can be used for any migration, be it application modernization or migrations from on-premises to cloud infrastructure from AWS or Azure.
Now the real magic begins! After all the strategizing and configurations, Grit.io performs code refactoring where using artificial intelligence, it is able to transform legacy code into modern frameworks and programming languages. It also actively transforms data into the compatible schemas and structures to further improve compatibility. Just this couple of features might reduce weeks of manual validation and coding time.
Judging by the autonomy granted by current-generation AI tools, it is likely that we will see a greater emergence of no-code platforms. These platforms allow users to engineer custom software solutions without having any prior coding knowledge, just using natural language prompts. This will open a lot of doors for creative ideas and solutions that were previously inaccessible to regular individuals.
Much like using any other AI tool, the reliability of the final results produced depends on the quality of training data and the contextual understanding limits of the core model. Almost all AI tools for developers on the market specifically address this risk and state that all results produced should be checked for their accuracy by an expert. This is because if left unchecked, there is a possibility that AI hallucinations might cause an entire project to crash in the production environment due to faulty code.
Of course. The rise of open-source AI models specifically for code completion, such as CodeLlama 2, shows the growing shift towards adopting custom AI models that are purpose-built. These open-source AI models can serve as a great starting point for building your own custom AI solution that addresses your specific needs. Alternatively, you could contact a custom AI development company to build such a specialized solution for you without any of the hassles involved.
With our list of the best AI software development tools, you have gained a bird’s eye view of all the current AI tools that are best for aiding you in your development journey. Everything from machine learning algorithms to correct code snippets to natural language processing engines to transcribe meetings has been covered. This goes to show how far the AI industry has come along already and what we can expect from it in the future.
But alas, no solution is perfect and this calls for custom AI development solutions to address challenges that are unique to your operations. If this describes your situation, then you can always contact Space-O to learn how we can help you leverage the power of artificial intelligence to build custom solutions that allow you to automate development processes.
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