Artificial Intelligence (AI) has been making waves in many industries, and software engineering is no exception. AI has the potential to revolutionize the way software is developed, tested, and maintained, bringing a new level of automation and efficiency to the field. However, with this transformation comes new challenges and opportunities that software engineers will need to navigate. First, we will cover the advantages AI brings to this industry.
AI, The Good Parts
One of the most significant impacts of AI on software engineering is the automation of routine and repetitive tasks. These tasks often referred to as “plumbing” tasks, are essential to the development process but can be time-consuming and dull. Examples of these tasks include: code refactoring, bug fixing, and testing. With AI, developers can automate these tasks, enabling more time to focus on strategic and creative tasks. This results in higher productivity, better quality software, and reduced time-to-market.
Another impact of AI on software engineering is the ability to automate specific functions with precise inputs and outputs. For example, AI can be used to automatically generate code based on user requirements, freeing developers from having to write repetitive code from scratch. This can save time and reduce the risk of errors, resulting in better software quality.
The integration of AI into software engineering will also enable engineers to accomplish more innovations. By automating routine and repetitive tasks, developers will have more time and energy to focus on developing new and creative solutions, resulting in more innovation in the field and better customer solutions.
Previously, senior developers would have to perform repetitive tasks that a junior developer could have performed. Even when development teams have junior developers, the senior developers often need to switch their focus to explain the task and follow up, making the effort arduous and inefficient. Now with AI, many of these tasks can be automated.
AI Limitations Boost Human Strength
The reality is that AI still has limitations. Humans are needed to deliver context and nuance, while understanding how something solves for a specific business need. While AI can automate routine and repetitive tasks, it lacks a human software engineer’s creativity and critical thinking skills.
We are not in an era where a product manager, without a strong engineering background, can communicate with AI to fully create a working product. There are many ways to achieve the same goals and only someone that deeply understands the issue and the vast technical options can combine those to solve the business requirements.
For example, a senior developer can understand the context and the specific business needs of a project and make informed decisions about which technology and design pattern to use. They can also fine-tune the AI responses to ensure it aligns with business requirements. Additionally, they can integrate different technologies and create a product that meets the needs of the business.
On the other hand, AI can only perform tasks it has been trained to do and is limited by the data and algorithms it has been given. It cannot think critically and creatively, nor understand the context and complexities of real-world business issues.
Therefore, it is important for software engineers to develop their skills and expertise in AI. At the same time, they must expand their critical thinking and problem-solving abilities, as the ability to work with AI will be in high demand.
Moreover, senior developers can apply their experience and creativity to develop new and innovative solutions that can only come from human intuition and reasoning. In this sense, AI is a tool for senior developers to use, not a replacement for their role.
Raising the Bar: Deeper Understanding
Senior developers will need to have a deeper understanding of how AI works and its limitations. They will need to be able to communicate effectively with AI tools, fine-tune them, and ensure that they are aligned with business needs. They will also need to be able to interpret and analyze the results produced by AI, make informed decisions based on these results, and integrate with harmony all this work with existing technology.
AI brings new challenges and opportunities. The automation of routine tasks and specific functions means that the bar for entry into the field will be raised.
As AI becomes more integrated into software development, a deeper understanding of both technical and business implications will be required. This will result in a higher demand for engineers who can effectively leverage AI tools and understand the consequences of their solutions.
The increased demand for engineers who can work effectively with AI tools will have a profound impact on the software engineering industry. The initial bar to enter the field will grow, as a higher level of discipline and understanding will be required. This will result in a higher-quality talent pool and an increased emphasis on continuous learning.
To make the most out of AI in software engineering, engineers need to have a thorough understanding of all the technologies involved in creating the product they’re working on, not solely AI technologies. This was important even before AI became prevalent, but now it’s crucial for differentiating between a valuable software engineer and one that can be replaced. Engineers who possess the skills to effectively integrate AI into their work will be highly sought after as they bring a valuable combination of technical and business knowledge to the table.
Not all revolutions had the same impact. Some of them almost completely hid from us the need to understand and know more than we did before. The revolution of modern compilers and programming languages has greatly simplified the process of coding for developers and has made it easier for people to understand and write complex programs without having to delve into the intricacies of low-level code.
The AI revolution in our field is different. This revolution is more complex because it involves the development of systems that can learn and make decisions on their own, without being explicitly programmed to do so. This requires a deeper understanding of the algorithms and techniques that underlie technology and business needs, as well as a deeper understanding of the data that is being used to train these systems.
Augmented Software Engineering
To see this in action, let’s take a look at a typical day for a software engineer. The day-to-day tasks of a software engineer can vary based on the company and the individual, but there are some common coding-related activities that many software engineers engage in. Here is a general idea of what a typical day for a software engineer might entail:
- Check and respond to emails: Software engineers often start their day by checking and responding to emails. We can utilize AI to an extent to sort and prioritize emails, or even generate responses. However, we still need to review and approve the responses before they’re sent.
- Review code: Software engineers spend a significant amount of time reviewing code written by themselves or their colleagues. This can include reviewing pull requests, providing feedback, and ensuring the code adheres to coding standards. AI can be utilized in some cases for code analysis and quality assurance, but a software engineer needs to review the code and make final decisions.
- Meetings: Software engineers may attend team meetings, project meetings, or client meetings. These can be in-person or virtual, covering topics such as project status updates, team collaboration, and problem-solving. AI cannot fully participate in meetings, but engineers can use it to schedule, remind, and recap meeting takeaways.
- Documentation: Software engineers may also spend time documenting their code, writing technical reports, or updating project documentation. Companies can utilize AI in some cases for document generation and summarization. However, the software engineer would still need to review and approve the final documentation.
- Learning and development: Software engineers are constantly learning and growing their skills. They may read industry articles, experiment with new technologies, or take online courses. AI can assist in finding and recommending learning resources, but the software engineer still performs the learning and skill development necessary.
It is difficult to determine an exact figure for the reduction in time spent on “side tasks” by software engineers, as this varies considerably based on the individual and the company. However, based on my experience, I estimate that using AI could lead to a 25-50% decrease in the time spent on such tasks. For example, a software engineer who previously spent 60% of their time on full hands-on valuable work for the company and clients, 35% on “side tasks”, and 5% on innovation could potentially increase their innovation time to 20%, or more, with the help of AI.
Just as augmented reality enhances our perception of the world, AI can enhance our ability to perform tasks by adding an extra layer of intelligence.
Augment Your Team
As managers, it’s crucial to facilitate the shift towards Augmented Software Engineering by guiding developers in understanding the role of AI and providing them with the necessary tools to enhance their daily work through these cutting-edge technologies. Developers’ proficiency in incorporating AI should also be a factor in their performance evaluation, as it will reflect their improved efficiency and innovation. It’s important to note that those who don’t embrace AI may eventually be replaced by the next generation of developers who are fearless in embracing this new reality.
Integrating AI into software engineering will bring a new layer of intelligence that will result in greater efficiency, leading to increased innovation. AI has the potential to revolutionize the way software is developed, tested, and maintained by freeing developers from repetitive and routine tasks. By automating these tasks, developers will have more time and energy to focus on developing new and creative solutions, resulting in higher productivity, better quality software, and reduced time-to-market. Further, AI will enable engineers to focus on more innovation projects and spend less time on repetitive tasks. In this sense, AI is a tool for software engineers to use, not a replacement for their role, and will result in more innovation in the field and better solutions for customers.
* This article represents my thoughts on this matter. I utilized ChatGPT to check for grammar mistakes, improve some paragraphs to clarify ideas, and fact-check information regarding today’s AI capabilities. To write this article I spent one-third of the time I usually need, while the rest was dedicated to an innovation project aimed at bringing value to one of our biggest customers.
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