AI-powered Development: How to Accelerate Software Project Implementation With AI
There is no doubt that artificial intelligence (AI) performs certain tasks faster, and sometimes better, than humans. In the modern world, the refusal to integrate AI into work processes leads to a decrease in productivity and a loss of competitiveness. Of course, it is still not possible to fully rely on smart algorithms (and do we really need to?). However, the effective implementation of AI in various operational areas should be a priority for companies aiming for long-term success.
Today, almost every business, to some extent, faces the need for software development. Whether you are a software vendor, an e-commerce store, or a real estate agency, you want the tasks assigned to your IT partner to be completed within the set deadlines. AI software development is one of the most effective ways to accelerate code production and testing.
In this post, we'll cover the basics of AI-powered software development and demonstrate how your project can benefit from it.
So, how exactly is AI used in software projects, and what AI tools are utilized in the programming process? Here are a few prominent examples of applications of AI:
AI code generation
Not only can AI generate code, but it does so reasonably well. A few years ago, tales of robots creating software programs would be regarded as science fiction, however, it is now a common reality. If you google "AI for code generation," you will get tons of results for various AI-based tools that can write code. The most popular ones are OpenAI Codex, GitHub Copilot, ChatGPT, Tabnine, and Amazon CodeWhisperer. Moreover, these AI coding tools, like human programmers, often specialize in specific programming languages, although they have some "expertise" in other languages as well.
According to GitHub, 92% of programmers are already using AI assistance in their work. Does this mean that human coders will soon become obsolete? Well, this depends on how fast AI evolves and how human-like it will be in terms of flexibility and decision-making.
At the moment, AI code generators are still in their infancy. They, indeed, speed up the work of programmers, but they still require human supervision and fine-tuning to ensure the accuracy and quality of the generated code.
Developers looking to stay competitive in the ever-changing market should consider AI as one of their go-to instruments. However, to develop high-quality software in a short time, they must possess strong expertise to critically evaluate the code generated by artificial intelligence.
Two ways AI code generation can be used
Developers can use AI for code completion or actual code generation, using text-to-code or speech-to-code techniques.
Code completion means suggesting or automatically filling in code snippets, functions, or variables as developers type. Code completion reduces the need to manually type repetitive or common code segments, saving the overall time for project implementation.
Text-to-code and speech-to-code convert human language into executable code. They leverage natural language processing (NLP) and machine learning algorithms to bridge the gap between human language and programming language.
Using text-to-code, developers can convert written instructions into executable code. Text-to-code techniques can range from simple command-based systems that convert specific keywords or phrases into code snippets to more advanced models that employ machine learning to generate code based on contextual understanding and learned patterns.
Using speech-to-code, developers can verbally communicate their code instructions instead of manually typing them. For example, they can dictate their code (declare variables, specify functions) or explain program logic, and have it automatically transcribed and converted into executable code.
AI code improvement
AI is not only great for writing code but also for checking existing code. It rapidly scans human-written code and makes adjustments to optimize the codebase while eliminating bugs. Let's explore the key areas where AI plays a crucial role in code optimization within software projects.
Code review. AI conducts code reviews by utilizing machine learning algorithms and natural language processing techniques. It analyzes the codebase, looking for patterns, code smells, and potential issues. Coding AI tools detect syntax errors, check adherence to coding standards, and identify performance bottlenecks and security vulnerabilities. Through continuous learning, AI algorithms improve their ability to identify and flag code issues, providing developers with valuable insights and recommendations for improving the quality of their code
Code refactoring. AI can assist in various ways to refactor code.For example, it can:
recognize common patterns and identify areas that can be refactored
detect complex logic, duplication, and performance bottlenecks
automate certain refactoring operations, such as renaming variables, extracting methods, or reorganizing code structure
learn from open-source projects and suggest refactoring improvements
Bug fixing. AI assists greatly in detecting and eliminating bugs in code bases. It:
automates bug detection by recognizing code patterns that are often associated with bugs
identifies the root cause of bugs by highlighting the specific lines or sections of code that are likely causing the issue
prevents bugs by analyzing code patterns and suggesting code improvements
automatically repairs code by proposing patches and code modifications
recommends how to deal with critical bugs by learning from historical data
AI software testing
Testing and quality assurance are integral parts of the software development process. Testing allows for evaluating the quality of the product being developed and eliminating bugs and issues before the product reaches end users.
QA engineers spend a lot of time developing test cases and scenarios, writing test scripts, and actually conducting testing. AI accelerates the work of the QA team by providing efficient assistance in various aspects of software testing.
Here are the main uses for AI-driven testing:
Test case generation. By analyzing project specifications, AI algorithms can quickly generate test cases, surpassing the speed of human testers. While the generated test cases may not always perfectly align with the project's requirements, QA engineers can easily modify and adjust them as needed. This process is still much quicker compared to creating test cases entirely from scratch.
Test data generation. AI can generate diverse and realistic test data to simulate different scenarios and edge cases. By using such techniques as generative models and data synthesis, AI can create test data that covers a wide range of inputs, enabling more thorough testing and better identification of potential issues.
Test prioritization. AI algorithms can analyze the risk factors associated with different components and features of the software and prioritize the execution of test cases accordingly. By considering factors such as code complexity, change impact, and historical bug data, AI can optimize the testing process by focusing on areas that are more likely to contain critical defects.
Automated test execution. AI can automate the execution of test cases, saving time and effort for repetitive and time-consuming tasks. Test automation frameworks powered by AI can execute tests across different platforms, devices, and configurations, ensuring broad test coverage and accelerating the testing cycle.
AI project management
Project managers make significant efforts to set each project on track and establish a productive workflow that yields good results. Acting as intermediaries between clients and developers, project managers organize teamwork in a way that tasks are completed on time while ensuring the team operates at a comfortable pace without rushing.
AI assistants play a significant role in project management if used correctly. Listed below are some key areas where AI for project management can help:
Resource allocation. AI algorithms can analyze project requirements, team capabilities, and available resources to optimize the allocation of tasks and assignments. They can help project managers make informed decisions on resource allocation, ensuring that the right people are assigned to the right tasks at the right time.
Risk assessment. AI can analyze historical project data and patterns to identify potential risks and predict their likelihood of occurrence. By providing early warnings and insights into possible risks, AI can help project managers proactively mitigate risks and make informed decisions to ensure project success.
Progress tracking. AI-powered tools can automatically track the progress of tasks and milestones, providing real-time updates and insights on project status. This enables project managers to monitor project timelines, identify bottlenecks, and take necessary actions to keep projects on track.
Decision support. AI can analyze large amounts of project data, including past performance, resource utilization, and market trends, to provide project managers with valuable insights for decision-making. It can assist in project planning, budgeting, and resource management, enabling project managers to make data-driven decisions.
Communication and collaboration. AI-powered chatbots and virtual assistants can facilitate communication and collaboration among team members. They can provide instant responses to queries, schedule meetings, and assist in coordinating tasks and activities, improving overall team efficiency and productivity.
AI documentation creation
Writing documentation is an integral part of the software development process. Software project documentation includes:
Software Requirements Specification (SRS) - which describes the functional and non-functional requirements of the software and specifies what it should do and how it should behave.
Design documentation - which includes architectural diagrams, system design, database schemas, and other technical specifications that outline how the software will be implemented.
User documentation - that describes how to use the software from the users' perspective and includes manuals, tutorials, and FAQs.
Test plans and reports - which include testing strategy, test cases, and testing results.
Change management documents - that record changes made to the software and include change requests, bug reports, and their resolutions.
Project timeline - which specifies the project schedule, key deliverables, and milestones.
Deployment and operations documentation - that specifies how to configure and maintain the software in production and includes system requirements, installation guides, and troubleshooting information.
AI in software development documentation saves time and reduces manual effort from the technical writers’ side. Below we provide the main use cases of applying AI for creating project documents:
Automated document generation. AI-powered tools can analyze project artifacts, such as requirements, designs, and code, to automatically generate portions of the documentation. Developers and technical writers only need to review and add missing information to what has been written by AI. This is much faster than writing documentation from scratch.
Language translation. AI is really good at translating documentation into different languages. AI-generated translation requires minimum corrections from the human side.
Content summarization. AI algorithms can summarize lengthy documents or technical specifications, condensing them into concise and digestible formats. This improves project understanding and promotes more efficient collaboration among team members.
Knowledge extraction. AI algorithms can analyze unstructured data, such as user feedback, customer support tickets, or online forums, to extract valuable insights and incorporate them into the documentation for improved user understanding.
Version control. AI can track and document changes made to the codebase. For example, it can analyze code repositories, identify differences between versions, and generate detailed reports highlighting the modifications.
Empower your project with AI: partner with Erbis
Modern life is heavily reliant on software technologies. We interact with computer programs and IoT devices daily, and it seems that we are constantly witnessing the emergence of smarter and more advanced technologies. Technological progress is rapidly advancing, and companies cannot afford lengthy development processes that rely solely on manual code writing. To optimize their efficiency, save time, and achieve excellent results, companies should embrace the competent use of AI tools for coding and other aspects of project implementation
At Erbis, we excel in combining human expertise with cutting-edge technologies. With over 11 years of experience in the market, even before AI-powered development became a common term, we have gained the trust of more than 30 clients worldwide and successfully completed over 50 projects. As AI assumes a significant role in project implementation, our developers deliver results faster without compromising on quality.
If you are seeking an experienced technology partner well-versed in modern technologies, we invite you to contact us. By utilizing AI and human resources to the full extent, we can create tech magic together.
