Productivity and performance are critical to building a successful product and achieving business goals in the software development industry. That’s why software developers and companies constantly seek more efficient methods to improve their development processes and create better products. With the rapidly pacing AI dominance in our age, it’s vital to leverage artificial intelligence to drive high-quality software solutions and shorten the time-to-market for helpful products. That said, you must learn how to increase developer productivity by using AI in software development.
Artificial Intelligence (AI) has permeated many sectors of our world and emerged as a transformative force in the software development industry. Because AI can replicate human intelligence and upend some human limitations, it has become helpful for streamlining intricate processes, automating mundane tasks, and improving developer productivity.
This article shows how you can leverage AI with your human software development team to optimise operational efficiency, remain competitive in your industry, and furnish superb software solutions of exceptional quality.
First, let’s explore the software that most AI software engineers use.
What are the 6 best AI development software platforms?
The foremost AI tools that many software developers and companies use have built-in machine learning and AI capabilities. Developers use these products to create high-quality applications and software products quicker.
Google Cloud AI platform
Google Cloud AI Platform is a suite of cloud-based tools and services provided by Google Cloud for developing, training, and deploying machine learning models. The service offers a scalable and flexible infrastructure to support various stages of the machine learning workflow, including data preparation, model training, hyperparameter tuning, and model deployment.
Microsoft Azure AI
Microsoft Azure AI is an enterprise-ready cloud computing platform with integrated AI features, such as machine learning, deep learning, cognitive search, speech services, databricks, custom vision, bots services, and many other tools for designing, implementing, and managing AI-driven solutions at scale.
IBM Watson
IBM Watson is a powerful artificial intelligence (AI) platform developed by IBM. It combines various technologies and capabilities, including natural language processing, machine learning, data analytics, and cognitive computing, to provide advanced data analysis and cognitive computing services.
Developers use IBM Watson to integrate advanced artificial intelligence capabilities into their applications, allowing them to understand, analyse, and derive insights from unstructured data like text, images, and audio.
Infosys XtractEdge
XtractEdge is a powerful Document AI tool helpful for gaining insight from legal contracts, commercial insurance papers, SOPs, images, handwritten notes, PDFs, or emails. Enterprises can use this tool to deliver measurable value by gleaning insights from unstructured data.
Additionally, XtractEdge helps clients leverage the inherent power of a connected enterprise by amplifying human potential, crafting connected customer journeys, and exploiting the power of value networks.
H2O.AI
H2O.ai is a software company for developing open-source machine learning and artificial intelligence platforms. The company’s flagship product is called H2O, which is an open-source, distributed, and scalable machine learning platform. H2O provides a user-friendly interface and a suite of machine learning algorithms that software developers can use for data analysis, modelling, and predictive analytics.
AWS AI/ML services
AWS AI/ML services refer to a collection of artificial intelligence and machine learning services provided by Amazon Web Services (AWS). These services enable businesses to leverage the power of AI and ML to analyse data, extract valuable insights, and build intelligent applications.
With AWS, you can pay for AI/ML services on a pay-as-you-go basis, allowing you to avoid upfront investments in expensive hardware and software. AWS provides user-friendly interfaces and pre-built models, making it accessible to users with varying levels of expertise in AI/ML. Not to mention the ease of scalability…
Top 8 ways to use AI to improve your software development team’s efficiency
The best AI tools help improve developer productivity. Discover our top selection for software developer productivity tools and start to leverage AI in software development projects.
1. Prototyping
Efficient time management is paramount during the prototyping stage of any software development project. This crucial phase involves the creation of preliminary versions of the software to test and validate its functionalities.
By embracing AI-driven automated code generation, developers can expedite the prototyping process, enabling rapid iterations and refinements. With this possibility, developers can use AI to swiftly explore different design possibilities, identify potential issues early on, and enhance collaboration between teams.
Consequently, leveraging AI in prototyping facilitates accelerated development cycles, promotes innovation, and ultimately leads to the delivery of robust and user-centric software solutions.
2. Debugging
AI-assisted bug detection uses advanced algorithms to automatically identify software issues by analysing code patterns, data flow, and error logs. Developed using sophisticated algorithms, it can also meticulously analyse code patterns, data flow, and error logs to swiftly and accurately identify anomalies and deviations that may indicate the presence of bugs.
This proactive approach allows developers to detect and address bugs early, reducing their impact on the development timeline. By swiftly scanning large codebases and learning from historical bug data, AI algorithms streamline bug identification and improve overall software quality, preventing issues from reaching end users.
3. Code explanation
AI-powered code explanation systems use natural language processing (NLP) and machine learning techniques to analyse code and generate human-readable explanations. These explanations provide insights into code snippets’ purpose, functionality, and logic, making it easier for developers to comprehend and work with unfamiliar or intricate code.
The benefits of AI-driven code explanation include:
- Simplifying complex codebases to speed up new developer onboarding.
- Improving software development team collaboration and knowledge sharing by standardising code discussions.
- Tailoring code explanation because of AI’s adaptive learning capacity and ability to process developer comments to understand the development team’s specific needs and tastes.
4. Refactoring code
Code refactoring helps in analysing code and identifying areas of improvement in a software development project. With AI-powered code refactoring systems utilising machine learning algorithms and pattern recognition techniques, you can access more direct suggestions about refactoring strategies, such as code restructuring, renaming variables, removing duplicate code, or applying design patterns, to enhance code quality and maintainability.
Some advantages of AI-driven code refactoring include the following:
- AI algorithms may detect code smells and anti-patterns, which can cause bugs, performance issues, and codebase maintenance issues. Automatic detection algorithms allow developers to proactively fix these flaws, resulting in cleaner, easier-to-maintain code.
- AI-powered code refactoring solutions improve code readability, modularity, and efficiency. Using industry best practices and coding norms, the systems can suggest refactoring actions, helping developers follow coding standards and improve code quality.
- AI algorithms can learn from code repositories and software development practices to observe trends and suggest refactoring options based on that information.
5. Predictive analysis
AI-based predictive analytics tools use historical project data, machine learning algorithms, and statistical models to forecast future project outcomes. These algorithms can accurately estimate project timelines, resource requirements, and potential risks by analysing patterns, trends, and dependencies.
The benefits of employing AI for predictive analysis in project management include the following:
- AI algorithms can help define realistic goals and project estimates, helping to guarantee project completion, resource allocation, and reduction in bottlenecks.
- AI-powered predictive analytics can analyse barriers and detect dangers and bottlenecks early by analysing past project data, team performance, external dependencies, and market conditions. This helps project managers reduce risks, optimise resource allocation, and meet deadlines.
- AI systems can adapt to project development and make real-time predictions and recommendations.
This agile approach to project management empowers teams to respond promptly to changing requirements and evolving circumstances, fostering adaptability and flexibility.
6. Automated testing
Using AI automated testing methods helps you quickly analyse code, identify potential vulnerabilities, and simulate various test scenarios. By automating the testing process, AI algorithms can execute test cases more quickly and efficiently than manual testing methods, saving time and resources.
The advantages of using AI for automated testing and quality assurance include the following:
- AI algorithms can run more test scenarios to verify software functionality. This feature decreases the risk of critical bugs escaping testing and reaching consumers.
- AI systems can recognise sophisticated software behaviour patterns. Plus, they can identify faults, performance bottlenecks, and security issues that manual testing may miss. This increases software quality and reduces post-deployment difficulties.
- Developers can quickly find and fix defects using AI algorithms that analyse test results and log information. This reduces debugging time, allowing developers to meet tighter deadlines with high-quality software.
7. Documentation
Developers can work with AI-powered smart documentation generation systems to analyse code, comments, and related resources. These systems can automatically extract relevant information, generate descriptive documentation, and provide contextual explanations for code snippets, functions, and modules.
The benefits of AI-driven documentation generation include the following:
- Automates tedious technical documentation for developers.
- AI documentation systems analyse code repositories and track code revisions to update pertinent documentation pages to reflect the current codebase automatically.
- AI algorithms improve documentation readability. They can simplify technical jargon, explain complex concepts, and provide insightful explanations, making documents more accessible to technical and non-technical audiences.
8. Copilot Tools
Copilot AI tools such as GitHub Copilot, Codeium, and Whisperer are designed to assist developers in scripting by leveraging machine learning techniques.
- GitHub Copilot: GitHub Copilot is an AI-powered code completion tool developed by GitHub in collaboration with OpenAI. It uses GPT-3.5, the same underlying technology as ChatGPT, to generate code suggestions as developers write in their preferred code editor. Copilot analyses the context, including existing code, comments, and function signatures, to provide relevant code completions, snippets, and even whole functions. It aims to save time and increase productivity by automating repetitive tasks and assisting with code generation.
- Codeium: Codeium is another AI-based tool that assists in code scripting. While specific details about Codeium are not available in my training data as of September 2021, similar tools often use machine learning algorithms to analyse code patterns, learn from existing codebases, and generate code snippets or entire functions based on the developer’s context and intention.
- Whisperer: Whisperer is an AI tool developed by OpenAI that assists with natural language-to-code translation. It aims to bridge the gap between domain experts and developers by allowing users to describe their intentions in plain English or other natural languages, and then generating code based on that description. Whisperer leverages machine learning techniques to understand the user’s intent and generate code that aligns with their requirements.
These AI tools are designed to provide developers with intelligent assistance during the coding process, helping to improve productivity, reduce errors, and accelerate development workflows. However, it’s important to note that while these tools can provide valuable suggestions and code snippets, human review and validation are still necessary to ensure code quality, adherence to best practices, and to address specific project requirements.
Building an AI-powered virtual assistant to help your software teams with such tasks at a very capable level, combining the power of LLMs and RAG models is the holy grail of AI Copilot solutions. Check out how we have helped a client build such an AI Virtual Assistant to support their customer success teams:
Zartis leads the next generation of AI-assisted software development processes
Integrating Artificial Intelligence (AI) with software development teams offers immense potential for maximising efficiency. From prototyping to debugging, project management, coding explanation, and documentation generation, AI-driven solutions enhance productivity, quality, and collaboration, empowering teams to deliver exceptional software solutions.
At Zartis, we understand the significance of AI in software development processes. On that note, we extensively train our software developers on how to implement AI with their software development skills to create high-quality products faster.
If you would like to discover how to apply AI across your whole SDLC, and what are the best startegies to start your AI journeywith, we are happy to help. Feel free to reach out now!
