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The Impact of AI: How Future Software Engineers Can Adapt [2024 and Beyond]

Remember the phrase “Artificial Intelligence (AI) will take the world”? How about in the world of software engineering? If you’re considering becoming a software engineer, it’s never too late to make a career change today – there are massive opportunities that await you.

Written by
Kai Yuan Neo
Last updated
August 18, 2023
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 The Impact of AI: How Future Software Engineers Can Adapt [2024 and Beyond]

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The Future of Software Engineering

By 2040, artificial intelligence (AI) could potentially replace software developers, as per a study by researchers at the US Department of Energy’s Oak Ridge National Laboratory. 

The researchers predict a significant shift in software development, driven by advancements in machine learning, artificial intelligence, natural language processing, and code generation technologies. 

According to the researchers, these innovations may lead machines to write most of their code within two decades. Understandably, software developers fear the prospect of AI taking over coding tasks. 

A California-based market research firm specialising in software development, Evans Data Corporation, found that nearly 30% of the 550 software developers surveyed expressed fears of their roles being replaced by AI soon.

How AI Changed the Software Engineering World

AI has revolutionised software development with its automation capabilities, boosting productivity and efficiency. Tasks such as code generation, refactoring, and bug detection are now automated, allowing developers to focus on more complex aspects.

In testing and quality assurance, AI-based tools analyse code, predict vulnerabilities, and generate test cases, ensuring higher-quality software with early issue detection.

Meanwhile, Natural language processing (NLP), an AI subset, has enabled human-like interactions with software through chatbots and virtual assistants, enhancing user experiences and expanding development possibilities. 

AI algorithms also provide intelligent recommendations and personalisation by analysing user data to tailor user experiences and improve user engagement.

Data-driven decision-making is facilitated by AI's ability to analyse vast datasets, providing insights for predicting user behaviour, optimising performance, and enhancing security. 

In addition, DevOps practices have been augmented through code changes and performance metrics analysis performed by AI.

The following is a diagram of AI’s possible role in the software development process:

Will AI Replace Software Engineers in the Future?

Although incorporating AI technologies is becoming more common, it is not a substitute for human creativity and problem-solving skills. It cannot replace human software engineers as a whole. The same answer applies to the “Will AI replace programmers?” question. 

Future software engineering demands complex problem-solving, algorithm design, and system development.

AI cannot replace the creativity and innovation that are integral to software engineering, encompassing UI design, user experience creation, and algorithm innovation.

Similarly, ethical considerations, such as privacy protection and bias mitigation, necessitate human judgment. This falls hand in hand with adaptability and contextual understanding, which are key strengths of human software engineers who engage with stakeholders, understand business needs, and tailor solutions to specific contexts.

Software engineering also requires collaboration and communication are vital in software engineering, which emulates interpersonal skills and domain expertise from human engineers.

Aruna Pattam, who has over two decades of experience with data, analytics, AI and data science wrote that there are a few challenges that generative AI can pose.

“The reliability of the AI-generated code remains questionable, occasionally necessitating human oversight for verification. Ethically, concerns arise from potential job displacements and inherent AI biases that might creep into applications.

“An over-reliance on AI recommendations can lead to stagnated human innovation and critical thinking,” she says.

While AI will impact software engineering, its role is to augment rather than replace human programmers by assisting in automation, code optimisation, and specific tasks while human expertise remains essential for complex system design, quality assurance, ethical adherence, and innovation in the field.

What AI Can’t Do: Important Skills Software Engineers Should Have For The Future

Executing basic commands like writing code snippets and simple automations won’t suffice. Software Engineers who wish to stay relevant amidst technological advancements should focus on bridging the skill gap that exists within the industry. 

Here are the few notable skills we consider as important for the future: 

Proficiency in Machine Learning (ML) and Data Science 

It's crucial to grasp machine learning models’ principles and techniques. 

Developers need to familiarise themselves with various ML algorithms, data pre-processing, feature engineering, model evaluation, and deployment. 

Knowledge in data science, covering data visualisation, exploratory data analysis, and statistical analysis, is also valuable for building a strong foundation in working with AI systems.

Mastery of Natural Language Processing (NLP) 

NLP is about grasping and handling human language. 

Developers can delve into methods like text preprocessing, sentiment analysis, named entity recognition, and language generation. Familiarity with NLP tools proves useful for tackling NLP tasks.

Expertise in Data Engineering and Management 

AI work frequently entails managing extensive datasets. 

Developers must grasp data engineering methods, data preprocessing, and data cleaning to uphold data quality. Moreover, understanding databases, and distributed computing frameworks proves invaluable in effectively managing and processing data for AI applications.

Domain-Specific Knowledge and Problem-Solving Abilities 

AI is applied across diverse domains including healthcare, finance, robotics, and autonomous systems. 

Developers need domain-specific knowledge to grasp the challenges, requirements, and constraints unique to each industry. Strong problem-solving skills, critical thinking, and the capacity to dissect complex problems into manageable parts are pivotal for crafting AI solutions.

Ethical Considerations and Responsible AI Practices

As AI becomes more integrated into society, ethical concerns become paramount. 

Developers must grasp the ethical implications of AI, including fairness, transparency, privacy, and bias. They should aim to create AI systems that align with ethical standards and minimise potential risks.

Interpersonal Networking Skills

Being familiar with AI and staying up-to-date with upcoming developments and trends can help one have a leg up in the industry. 

Through partaking in communities, a software engineer can engage with open-source software and teams, as well as follow relevant sources and networks to know the ins and outs of AI development. 

Creative Thinking

As AI begins automating repetitive tasks, a software engineer can instead focus on the more creative aspects of the role. This can spell for an evolution of the role itself, even if it is still being explored.

A Software Engineer who has developed the skills to work with AI assistance will be more freed up to focus on the creative aspects of a project.

A Software Engineer should have the ability to put together different ideas from various industries. Despite its roots in technological advancement, AI is not purely technical. According to an Assistant Professor of Computer Science at Baylor University, AI’s nature is inherently interdisciplinary. 

“Don't just focus on the technical aspects; understand the ethical, social, and business implications,” he said.

Tools that Every Software Engineer Should Know

Aside from the various hard and soft skills, there are Software Engineering AI-powered tools and frameworks that Software Engineers should be aware of. Below, we compiled and categorised a list of the most popular tools:

Deep Learning Frameworks:

  • TensorFlow
  • PyTorch
  • Keras

Machine Learning Libraries:

  • Sci-kit-learn
  • Natural Language Toolkit (NLTK)

Reinforcement Learning Development:

  • OpenAI Gym

Big Data Processing and Analytics:

  • Apache Spark

Development and Collaboration Tools:

  • Jupyter Notebooks
  • GitHub Copilot

Cloud-based AI Services:

  • Microsoft Azure Cognitive Services
  • IBM Watson
  • Amazon SageMaker
  • Google Cloud AI Platform

If this felt a little overwhelming, that’s alright! Software Engineering is constantly evolving and it always helps to have guidance. If you’re interested in learning Software Engineering, consider joining our online flagship Coding Bootcamp.

We constantly update our syllabus to match the latest industry needs and have an amazing team of instructors to guide you through every step of the way.

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