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How Will AI Affect Software Development? 5 Impacted Sectors

Techlife   -  

May 31, 2024

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AI, short for Artificial Intelligence, has invaded and revolutionized many industries. But how will AI affect software development, the field that creates apps and software we use daily? This article is where you’ll find the detailed answer to that question.

Here, Designveloper will explore 5 areas of software development that AI is poised to transform. But first, let’s take a deep look at what AI means in this realm.

Understanding AI in Software Development

In this section, we’ll discover whether AI could replace software developers and how to survive in this AI wave.

What is AI in Software Development?

What is AI in software development?

Today, many teams integrate AI into the software development cycle. Like at Designveloper, the use of AI algorithms helps automate, accelerate, and improve repetitive tasks. 

For example, AI models like GitHub Copilot suggest code, fix a bug, interpret a developer’s code, create unit test cases for our code, and generate collections of code documentation. Other AI-powered tools can support our project management, communication, and collaboration between team members and stakeholders.

By automating several tasks traditionally done by humans, AI boosts development speed, accuracy, and efficiency. It also allows our development team more time on complex, strategic tasks. This hence improves employee productivity and efficiency to build better software solutions.

Will AI Replace Software Developers and Testers?

Will AI replace software developers and testers?

The advent of Artificial Intelligence has sparked a debate about the future of human roles in every stage of software development, from UX/UI design to testing. While AI is making significant waves in this fashion, the question remains: Will AI replace human developers and testers?

The answer is no. AI hasn’t reached the level of taking over software development entirely. Besides, AI isn’t as creative as human engineers. As such, AI can only create code based on the training data, instead of generating new algorithms, paradigms, and architectures.

At least in the short term, most developers (43%) still feel positive about using AI, according to the 2024 Codepad survey. They believed that AI-assisted tools, like ChatGPT, help them reduce workload (70%), especially in repetitive, time-consuming tasks or those demanding high accuracy. These tasks include code autocompletion, prototyping & idea generation, or documentation & API support. 

These tasks, albeit automated, still require human supervision. In particular, AI models lack the capability to interpret the nuances of human language and the complexity of user experience. So, they still need human workers to validate and modify outcomes under certain contexts and requirements.

How to Stay Standout in the AI Era

How to stay standout in the AI era

While AI can’t take over your development jobs completely, it doesn’t mean you’re not negatively influenced at all. The World Economic Forum stated that AI advancements can lose 83 million jobs, versus 69 million jobs newly created. This potentially leads to mass unemployment, income inequality, and other socioeconomic issues.

Therefore, as AI advances and becomes more popular, you need to adapt and acquire new skills. They include:

Technical Expertise

Although AI can handle some manual development tasks, not all AI tools can work without human supervision. To implement these smart tools well, you need to enhance your technical skills as follows: 

Prompt Engineering: This involves creating structured prompts or instructions that help AI models understand even highly technical queries better. This skill is necessary for you to make sure AI can craft valuable outcomes as expected. Accordingly, you need to build clear and contextually relevant prompts, experiment with prompts, and validate code outputs.

Code Reviews: AI isn’t always generating perfect code. That’s why you should develop coding knowledge and even organizational understanding to review code successfully. This ensures faulty code isn’t used in software development. Also, you need to verify that code changes are aligned with requirements or the problems it’s supposed to solve.

Testing and Security: AI can accelerate automated testing. However, to make sure AI-generated tests reach your expectations (e.g., detect potential bugs), you need a solid foundation of programming, testing principles, and security practices. 

Soft Skills

More than three-quarters of developers and recruiters believed that soft skills are as crucial as technical expertise. As AI can’t mimic human creativity and other abilities, these soft skills are becoming more important than ever. Here are some key skills as your competitive edge in the AI era:

Communication: Software development isn’t a one-player game. It requires effective collaboration between team members and stakeholders to identify project requirements, share insights about targeted end-users, and handle existing challenges.  

Critical Thinking: AI works dependently on the training data. So if that data doesn’t contain information about a certain solution, AI can’t analyze and evaluate the solution accurately and safely. This requires you to hone critical thinking to judge different solutions and choose the best one based on the given requirements. 

Dependability and Attention to Detail: AI systems, albeit powerful, aren’t always reliable and secure. So with AI on the rise, you should ensure AI tools and algorithms are dependable and perform as expected. Also, attention to detail is needed to help you spot errors in code that could lead to bugs or system failures. 

Life-long Learning and Adaptation

AI is increasingly improving its capabilities and the ability to act independently. Apart from the hard and soft skills we mentioned, you should develop a continuous learning mindset to stay alive or even competitive in this AI era. 

You also need to update the latest AI research, understand complex math concepts, and use AI models responsibly. This will help you navigate the complexities of AI models and integrate them effectively into software development.

What 5 Sectors Will AI Affect in Software Development?

The global market for generative AI in software development is projected to grow at a CAGR of 35.3% during the period 2023-2030. Accordingly, it’s expected to reach an impressive value of almost 3 billion by 2030. 

This figure highlights the growing adoption of GenAI tools like Gemini or GitHub Copilot in the development process. But how will AI affect software development exactly? Let’s look at how AI benefits software development and how to overcome its challenges:

Code Generation and Completion

Code generation and completion

Most developers (60%) consider AI as their code assistant. It can analyze patterns in code to interpret the syntax, semantics, and structure of various programming languages. 

For this reason, when you provide a prompt or write code, these tools can predict what you want to achieve. Then, they may suggest the next line of code, complete a code snippet, or even craft an entire function that fulfills your prompt.

For instance, when you type def sort_array(arr): in Python, AI could recommend the following code to complete the function:

def sort_array(arr):

    return sorted(arr)

It’s because AI recognizes that “sort_array” is a common function name used for sorting arrays. Meanwhile, “sorted(arr)” is a popular method to organize arrays in Python.

Further, AI tools are trained on massive code repositories. This enables you to comprehend and work with complex or unfamiliar codebases. Also, this turns these tools into a diverse learning and tutorial source for developers.

Potential Cons

However, AI also exerts a negative impact on software development. 

First, not all AI code assistants produce your expected code outputs. So, fine-tuning is essential to align the code with your specific requirements or projects. 

Second, these tools can pose a risk of generating vulnerable or unintended code that less experienced developers may miss. Without rigorous code reviews, testing, and security audits, the software may confront unexpected risks. 

Testing and Quality Assurance

Testing and quality assurance

AI is widely adopted in testing and quality assurance (QA). So how will AI affect software development in this phase?

The State of Software Quality Report 2024 found that AI is mostly applied in generating test cases for manual testing (50%). Besides, AI tools also create test cases and scripts for automated testing (37%), generate test data (36%), and optimize testing (27%).

In addition to those QA activities, we at Designveloper also leverage these intelligent assistants to automate bug/defect detection and correction. This helps us focus more on developing new features instead of getting bogged down in repetitive debugging tasks. Also, we can reduce human errors, enhance code quality, and save costs.

Further, AI tools can schedule, analyze, and visualize testing on our behalf. This allows us to gain in-depth insights into possible issues, make data-backed decisions, and deliver software with high quality.

Potential Cons

Despite immense benefits, most development teams (45%) meet difficulty adopting AI in testing due to the lack of competent tools. Without capable solutions, testers may not automate important tasks or achieve the desired level of accuracy.

Second, implementing AI effectively also requires a team with expertise in both software testing and AI. Finding those with this combined skillset may be difficult, especially for smaller companies or those with tight budgets. 

Besides, one problem we encounter when adopting AI is security and privacy concerns. If AI tools aren’t secure, the data you feed them can be vulnerable to breaches. 

So, to mitigate this issue, we avoid using sensitive data related to our company or clients. Also, providing training and using validated AI tools is a must to help us achieve outcomes with high accuracy. 

Project Management

Project management

Today, many companies also embrace AI as a beneficial tool in project management (PM). Why? One 2023 Capterra reported that 93% of companies receive positive ROI when adopting AI to automate and streamline tasks. 

At Designveloper, we leverage such AI-powered tools as Jira to manage project schedules, track project progress, monitor team workload, and more. For this reason, we can meet deadlines, use resources more efficiently, and measure our project performance more accurately.

As AI continues to grow, the adoption of this tech in PM will increase accordingly. AI-enabled PM tools are expected to predict project risks and issues with high precision. Then, they can tailor suggestions that align with specific projects, requirements, and contexts. Also, they may support project management strategies and workflows more effectively.

Potential Cons

Most organizations (28%) admitted that the lack of a clear strategy is one of the major barriers to adopting AI in PM. So if your company has yet to fully-fledged AI strategy, start small by deploying AI within your team. 

Besides, you should understand what existing company policy relates to AI use. Then, talk to relevant people to understand who gets involved in any existing AI initiatives and how your project might fit it.

Further, we also recognize some AI tools aren’t immature and insecure enough. This can hinder the wide adoption of AI across projects. Therefore, we often evaluate the possible risks with AI tools, experiment with AI on a small scale, measure their performance, and establish ethical standards about AI use.

UX/UI Design & Prototyping

UX/UI design and prototyping

Most designers today use AI within UI design (over 12%) and prototyping (6%) to automate and streamline their design process. So how will AI affect software development at this stage? 

AI can analyze large amounts of user data, like user behavior patterns and feedback from surveys. This helps you understand user needs and interests better, resulting in more user-centric designs. 

Besides, AI can automate several repetitive tasks such as creating different layout options based on your design principles and user research data. It also generates basic interactive elements (e.g., menus or buttons). This leaves you more time for strategic thinking, creative aspects, or complex designs.

AI-powered tools can even analyze user interactions with UI designs and prototypes and detect areas of difficulty or confusion. This allows you to pinpoint usability issues early on, hence leading to more effective iterations.

Potential Cons

While AI tools can improve the design process, AI still has limited creativity. It can generate designs that are safe based on our prompts but unoriginal. This might lead to generic user interfaces. 

Besides, AI-generated designs, albeit efficient, can lack emotional aspects of user experience or contain biased elements. This could result in unequal user experiences for demographics. 

Therefore, to ensure UX/UI designs are aligned with specific requirements and user needs, there’s a need to develop principles or strategies to use AI responsibly.

Also, don’t depend solely on AI-generated ideas and designs. Use them as a springboard for creative exploration without human oversight and have experienced designers refine and add their unique touch.



As technology advances, software is increasingly prone to cyber threats. However, with the development of AI, security concerns are likely to be decreased. Here’s how AI affects the security aspect of software development:

Automated Threat Detection: AI can analyze big data to detect patterns or signs that can lead to cyber-attacks. Thanks to that, AI algorithms can instantly manage, analyze, identify, and respond to attacks.

Endpoint Security: AI can analyze and interpret the normal user behaviors and usage patterns of endpoints (e.g., laptops or smartphones), assets (e.g., databases), and network services (e.g., web services). Then it can detect anomalies and limit access to those abnormal activities to your software.

Data Protection: AI can recognize shadow data, manage abnormal signs in data access, and alert security specialists about potential risks. This saves much time in discovering and handling the threats instantly. 

With these benefits, the global market for AI in cybersecurity is estimated to reach around USD 134 billion by 2030.

Potential Cons

Despite potential advantages, AI, especially generative AI, negatively affects cybersecurity. Accordingly, almost half of respondents indicated that GenAI exacerbates adversarial capabilities like phishing, malware, or even deepfakes. Meanwhile, 20% felt worried about data leaks and exposure to sensitive information induced by AI.

So, although AI tools can help recognize potential threats, human security specialists still need to grasp the implications of these threats and take the best action. Also, AI tools should be properly configured and monitored to ensure their security. This demands ongoing effort and expertise from your security team.

Designveloper’s Case Study: How is AI Adopted in Software Development

So, how has AI been deployed in real-world software development projects? Let’s take a deep look at our work at Designveloper.

What does Designveloper Use AI for?

Today, Designveloper’s developers have been using such AI tools as ChatGPT, Gemini, and Microsft Copilot to optimize their daily tasks. These tasks include summarizing documents, drafting specs/docs, interpreting code, assisting us in acquiring knowledge during the coding process, generating test cases, or automating tasks that require extensive, repetitive text editing.

For example, Lumin’s web ops team used LLM to generate code to extract sitemaps for SEO optimization of static pages. Recently, the Lumin team has also experimented with GitHub Copilot to support their coding tasks.

Besides, we’ve leveraged AI in two specific situations:

First, AI-powered chatbots support us in bug-fixing by providing information for our each separate use case. Compared to traditional methods (e.g., using search engines or finding solutions from StackOverflow and redditm), this method of using AI proves much more effective.

In particular, it helps us understand a client’s requirements better and get relatively accurate answers. Meanwhile, the traditional methods only allow us to find relevant answers to our questions based on the information of previous clients.

Second, we customize a Q&A chatbot application for a specific domain by writing instruction prompts.

Challenges & Valuable Lessons

Despite its wide application in our development work, we can’t deny that AI still has some limitations. Apart from what we detailed in the previous section, another biggest concern is its accuracy. Particularly, AI-generated outcomes can be inaccurate, improbable, or outdated. This can lead to misusing these outcomes for the development process.

Here’s an example:

Problem of AI in software development

In the example above, the bot instructs users to change the “=” sign to a “:” sign in the local_settings table. But the bot’s suggested code still uses the “=” sign as before. The bot still gets this mistake even when users require fixing.

Therefore, you need to be careful with the results that AI returns to solve your inquiries (e.g., information extraction or image recognition).


While some may fear AI completely replacing software developers, the reality we found is far more collaborative. AI tools have transformed software development by augmenting human capabilities across five key sectors, from code generation to security.

To thrive in this evolving landscape, you should use AI as a powerful tool while focusing on areas where human strengths (e.g., creativity or empathy) remain irreplaceable. Follow our blog to stay at the forefront of this exciting new era in software development.

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