AI software development: Novel solutions to automate software developer jobs

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  Quantumrun Foresight
• April 6, 2022

Insight summary

The integration of artificial intelligence (AI) into software development is reshaping the industry, enabling faster development, enhancing cybersecurity, and fostering entrepreneurial activity. However, this shift also brings challenges such as an increased carbon footprint, potential job displacement, and ethical dilemmas in AI-driven decision-making. Balancing these opportunities and challenges will require thoughtful regulation, workforce adaptation, and a commitment to ethical principles.

AI software development context

Numerous software platforms have been developed in recent years to assist software developers in interacting with multiple interfaces, integrations, and dependencies. But even with these tools available, software developers are often tasked with manually managing and updating many components within a given program. Fortunately, AI systems can improve traditional processes within the software industry by applying AI-enabled solutions to automate ever more aspects of traditional software development.

Software development is an important facet of modern technology development processes as it supports rapid product innovation and iteration. In particular, software development advances made during the 2010s have enabled AI systems to leverage deep learning, machine learning (ML), and natural language processing (NLP) for a variety of novel applications. In recent years, some of these applications have explored using AI to enhance and automate elements of the software development process and ultimately deliver more sophisticated customer-driven products. 

For example, ML can optimize the software development process by speeding up problem and goal definition, data collection, data preparation, model learning, model deployment and integration, and model management. Among these different stages, problem and goal definition requires human involvement and uses NLP so that the machine's algorithm understands the user's requirements when it is communicated via text or even speech. 

Disruptive impact

The support AI can offer software developers includes automated quality assurance where code is reviewed and optimized in real-time, while control tasks can be deployed quicker through automated DevOps. Furthermore, AI can improve the quality of security applied to different forms of software through measures such as static and dynamic security reviews, vulnerability assessments, and providing improved codebase security. 

Developing software is a complex task that requires skilled professionals. However, AI-enabled software development can lower the skill threshold required to develop software and allow developers to perform specific tasks faster, lowering development costs as a result. An example is ML enabling the rapid prototyping of a piece of software, which helps developers devise algorithms tested in different scenarios. Automated software development leveraging AI tools can also enhance a software professional's ability to leverage data analytics and make sound development decisions. It can improve an organization's strategic ability by quickly processing important information that leads to insights being generated and consumed by critical organization decision-makers.

As AI usage becomes more influential within the software development industry, software developers will need to increasingly learn to leverage AI tools in their day-to-day work; for instance, using AI to eliminate syntax errors as they code. By the 2030s, however, AI-enabled software will further assist developers by providing coding suggestions, following a system's requirement or use case, generating new code that can allow a piece of software to function, and even auto-building cases to test the developer’s software.

Implications of AI software development

Wider implications of AI software development may include:

• Allowing for different types of software to be developed faster, moving forward development dates by lowering error rates and increasing coding efficiency and leading to more accessible technology for small businesses and individual developers.


• Reducing cybersecurity incidents as AI can reduce the quantity of errors, bugs, and vulnerabilities that hackers can take advantage of to compromise a system, leading to a more secure online environment for consumers and businesses alike.


• A boon in new entrepreneurial activity as AI-supported software development reduces the costs and expertise barriers associated with startups requiring software solutions, leading to a more vibrant and diverse business landscape.


• Improved data utilization as data plays a pivotal role in the application of machine learning within software development, leading to more personalized and efficient services for consumers and better decision-making for businesses.


• The government establishing new laws to balance technological advancement and data privacy protection, leading to enhanced consumer trust and a more ethical framework for AI development.


• An increase in the software development industry's carbon footprint due to the longer computation times required to train AI systems that would be used for software development augmentation, leading to environmental concerns and potential regulatory challenges.


• A greater push towards developing and implementing AI in various sectors, leading to potential job displacement in traditional roles and a need for workforce retraining.


• The rapid development of AI-powered software, leading to potential biases and ethical dilemmas in decision-making algorithms, which may affect marginalized communities disproportionately.


• The increased reliance on AI in software development, leading to potential monopolies by large tech companies with access to vast resources, possibly stifling competition and limiting opportunities for smaller players.

Questions to consider

• Do you believe AI assisting in and developing software will lead to job losses and the lesser involvement of humans within the software development process and industry?


• How can AI-assisted coding solutions support global startup ecosystems?