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April 12, 2024 - 6 minutes

How AI is Transforming the Software Development Lifecycle

Discover how AI is reshaping the software development industry, making it more agile and efficient.

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Artificial Intelligence

AI (artificial intelligence) is permeating every industry, from finance and healthcare to eCommerce and manufacturing. In fact, 42% of enterprise-level companies have been actively incorporating AI models into their workflows. AI-driven tools simplify numerous complex tasks, from content creation (ChatGPT is a good example) to business forecasting (take Einstein Prediction Builder by Salesforce as an example). The capacity of AI to process vast amounts of data, learn from experience, and automate processes makes it a go-to solution to optimize the software development lifecycle (SDLC) as well. 

The SDLC is a well-structured process consisting of several phases: requirements analysis, planning, creating, testing, deploying, and maintaining software applications. It's essentially a roadmap guiding developers from the initial idea all the way to producing a high-quality and functional product. The role of AI technologies in this journey is to streamline processes and boost developer productivity.

In this article, we'll cover the pros and cons of AI-assisted software development and analyze how AI models revamp the role of software developers.

Key Benefits of Using AI in Software Development

AI tools unlock wide opportunities for programmers. Applying machine learning algorithms and natural language processing (NLP) technologies, these smart models enable software developers to boost productivity, improve code quality, and expedite product launches. Let's analyze in detail how AI impacts software engineering.

Enhances efficiency

Writing and reviewing code, detecting bugs, and testing comprise a large portion of the development process. Manually performing this work is time-consuming. AI can automate routine tasks, allowing developers to focus their efforts on complex and creative aspects of a project.

For example, tools like GitHub Copilot streamline the workflows by providing real-time code suggestions. When developers type, Copilot instantly shows appropriate code snippets, function calls, and variable names. It can also understand natural language comments, analyze the existing codebase, and learn a developer's coding style over time to tailor suggestions to specific contexts.

Meanwhile, Amazon CodeGuru and DeepCode assist software developers in scanning code, identifying errors, and finding possible fixes and improvements in case of vulnerabilities. By detecting flaws and security issues before deployment, dev teams can create apps and platforms that are future-proof and more secure.

When it comes to the testing process, AI-powered tools turn out to be a valuable solution as well. Through the use of cutting-edge technologies, such as machine learning (ML), deep learning (DL), NLP, and computer vision, they assist developers in multiple aspects of software testing:

  • Generate test cases automatically by analyzing code, user stories, and historical data

  • Identify patterns and pinpoint potential flaws

  • Automate visual testing by comparing the user interface (UI) across various platforms

  • Recover and adapt automatically despite minor UI modifications and code refactoring

Besides speeding up the processes and increasing efficiency, AI-augmented testing solutions enable developers to improve software quality. Some popular examples of AI testing tools include Applitools, Testim, and Functionize.

For the record, integrating AI with IT asset management software is an excellent way for tech teams to boost productivity. The dynamic duo becomes a valuable tool for optimizing resources, managing risks proactively, and meeting deadlines. This ultimately leads to streamlined workflows, expedites the SDLC, and paves the way for breakthrough software solutions.

Facilitates decision-making

AI in software development goes beyond automating repetitive chunks of work in code generation, testing, and bug detection. Powered by machine learning, deep learning algorithms, and neural networks, these smart models excel at:

  • Analyzing large amounts of data related to software projects

  • Recognizing patterns and trends

  • Spotting potential bottlenecks

Based on the insights extracted by AI, developers can make data-informed decisions about optimizing software features, allocating resources, managing risks, and refining overall product strategy.

Trained on historical data like system logs, previous performance metrics, and user feedback, AI models can project future outcomes of a new product release. Using this information, software engineers can prevent possible issues before deployment, ensure a smooth product launch for end-users and eliminate the risk of post-release errors and bugs.

Promotes collaboration

Good teamwork is integral for a winning software development project. Artificial intelligence is especially beneficial for improving collaboration among members from different departments. Here's how:

  • AI automates repetitive, tedious tasks; thus, developers can focus on strategic work, project architecture improvements, and cooperation with other team members.

  • AI-powered chatbots bridge language barriers and facilitate smooth communication and knowledge exchange among team members regardless of their location.

  • AI models enhance pair programming by offering code snippets, highlighting potential issues, and suggesting improvements based on the established coding standards. This creates a more collaborative workspace where developers can share their knowledge, generate ideas, evaluate alternative approaches, and find the best solutions to developing code.

  • AI project management tools build a centralized platform for all dev teams, including frontend, backend developers, and DevOps engineers, so they can cooperate efficiently by timely spotting any roadblocks, monitoring the current state of a project, and understanding dependencies.

AI Challenges in the Software Development Industry

Don’t get too excited: there's always the flip side of the coin. Despite the advantages AI software development confers, it also comes with certain challenges. Let’s discuss. 

Steep learning curve

AI systems incorporate a range of cutting-edge technologies, such as machine learning, deep learning, and natural language processing. Not all developers are familiar with these complex algorithms and data science principles and companies should consider training software engineers to fill the skill gap.

The learning process can be time—and resource-intensive. Quality training programs generally cost a decent amount of money and significant upfront expenses may put an extra burden on the budget that not all companies are able to withstand. 

Moreover, AI is an ever-evolving field. Software developers should get used to the idea of continuous learning and upskilling. This implies keeping track of the latest developments, best practices, and trending AI tools.

Data dependency

AI models depend heavily on the data they're trained on. To harness the full potential of artificial intelligence and mitigate risks of bias and inaccurate predictions, software developers should expose advanced algorithms to large, diverse, and high-quality data sets.

High-quality or clean data has no errors, outliers, missing values, or inconsistencies. Software engineers can apply different methods to ensure data quality. The most common strategies include cleaning, labeling, and validating.

Extensive and diverse data sets are essential for AI tools to function effectively. In some cases, collecting enough data can be difficult for several reasons:

  • Data privacy laws like GDPR (General Data Protection Regulation) may hinder gathering user information for training AI models, particularly in domains where user privacy is a key focus.

  • Certain data types may be inherently hard or pretty expensive to get. For example, obtaining sensitive patient data to train AI tools for medical diagnosis is no easy task due to privacy restrictions or ethical concerns.

  • Specific data types may be missing. This is especially true for niche-focused industries where extensive data sets are not available.

Software developers need to come up with creative ideas to address data scarcity. Synthetic data generation is one reliable technique, which involves creating artificial data that simulates the characteristics of real data. This method is particularly valuable for software testing when production data isn’t accessible.

Privacy concerns

Privacy is one of the major concerns that can hinder the adoption of AI in software development. Since AI models are trained on large volumes of data, there's a risk you might share some sensitive information. 

Suppose you want to improve the existing code for an app your company is developing. You feed the AI parts of the code that might contain business-specific details. If the AI model operates on an external server, your application code can be easily compromised.

Therefore, it's essential to strike a balance between data accessibility and maintaining privacy. To achieve this, software developers should adopt strong encryption methods, anonymization techniques, and transparent data governance frameworks.

Integration with legacy systems

Incorporating AI solutions into existing software systems can be a real challenge. The biggest stumbling block is that legacy software solutions were not designed with AI compatibility in mind. To leverage artificial intelligence successfully, dev teams might need to:

  • Update certain components to support the computational power of AI algorithms

  • Develop custom adapters, including data format and API (application programming interface) adapters, to translate data between the legacy system and AI components and ensure their smooth communication

  • Utilize middleware solutions to connect two disparate systems without changing the core of the existing software environment

Cloud services are a good option to consider. These platforms provide AI as a Service (AIaaS) and enable companies to tap into powerful AI capabilities without extensive on-premise upgrades. Google Cloud AI, IBM Watson, Amazon Web Services (AWS), and Microsoft Azure AI are some popular AIaaS providers.

Limited creativity

Even though AI models can automate some coding tasks, analyze large data sets, identify patterns, and forecast outcomes based on this data, they fail at handling complex and uncommon issues. Compared to human developers who can think outside the box and use creative problem-solving techniques, machine learning and deep learning models can’t really boast such capabilities, at least yet.

AI software solutions are limited by the data they're trained on and may struggle to fully grasp the nuanced context and human aspects of the software development processes. Therefore, programmers shouldn't rely too heavily on AI algorithms to guide their work. Complete dependence on AI may decrease the overall software quality and potentially hamper the growth of the software development industry.

Future of Software Developers with AI

Will AI replace software engineers? This question hovers around as artificial intelligence keeps transforming the software development process. AI is a valuable tool for automating repetitive tasks; however, as mentioned in the previous section, it falls short in creativity, problem-solving skills, and keen insight into user demands when compared to human developers.

A software engineer possesses qualities that AI models have yet to replicate, in particular, critical thinking and the ability to collaborate and adapt to project requirements and unique challenges.

AI systems are about to revamp the role of software engineers rather than substitute them. To succeed in an AI-augmented development environment, tech experts should prioritize continuous learning and enhance their skill set in:

  • Machine learning, neural networks, deep learning, and natural language processing

  • Data science, including data analysis, statistics, and data visualization

  • Programming languages used to develop AI, such as Python, R, C++, Java, Julia, etc.

  • Version control systems (for example, Git)

  • Collaborative development tools

  • Software testing methodologies

  • Ethical considerations, such as transparency, privacy, and bias mitigation, for designing responsible AI solutions

As AI keeps evolving, the demand for software engineers who are competent at deploying machine learning models will increase accordingly. Therefore, it would be more correct to think of AI not as a substitute but rather as a tool for amplifying human capabilities and opening unique opportunities for innovation in software development.

Embrace AI-Assisted Software Development for Business Growth

AI and software engineering go hand in hand. This partnership enables companies to level up their development workflows and make them more agile and efficient. By injecting AI models into the SDLC, dev teams can focus on complex and strategic work instead of being bogged down by monotonous tasks.

With AI's ability to automate code generation, code review, and bug detection as well as analyze vast amounts of data and make predictions, software engineers can speed up the development process, decrease the number of errors, and ensure high-quality software solutions.

Before integrating AI technologies into their systems, companies should weigh up all the pros and cons and make rational decisions based on their needs and objectives. AI in software development is a valuable tool that aims to support human software engineers but not replace them. The combination of machine learning techniques and human creativity extends the boundaries of what's possible in the software development industry and enables companies to lead the innovation pack.

Are you keen on expanding your knowledge about AI? Check out Ironhack’s intensive bootcamps where you’ll discover more about data science, cybersecurity and machine learning. Enroll today and take your tech dreams to the next level!    


About the Author

Alex Husar is a chief technology officer at Onilab. He’s been with the company for almost a decade and has honed his skills in web development, creating progressive web apps (PWAs), and team management. Alex is always eager to deepen his knowledge in diverse technological areas and shares insights through his articles. He also helps programmers address common challenges and keep up with the latest web development trends.

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