The AI Development Revolution: Tools, Shifts, and Management [For Beginners]

The road to becoming an AI creator | Article introduction: Will development change? AI will dramatically change the future of software development tools, platforms, and management! 🚀 #AIdevelopment #softwaredevelopment #DX

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AI is changing the future! What is the future of software development tools, platform shifts, and management?

Hey everyone! I'm John, a veteran blog writer. Today, I'll explain in an easy-to-understand way for beginners how "AI technology," which may sound difficult but is actually starting to have a major impact on our work and lives, is revolutionizing the three areas of "software development tools, platform shift, and management." In the past, I was a manager of a development team and had a really hard time with a huge software platform migration project. I can't help but think, if only we had the AI technology we have now back then. Let's take a look at the future together!

Basic information: How will AI change software development?

First of all, some people may not understand what I mean when I say "software development tools, platform shift, management." To put it simply, it's something like this.

Software Development Tools: Tools that programmers use to create apps, websites, etc. For example, editors for writing code and tools for finding bugs.
Platform Shift: Changing the foundation (OS, hardware, etc.) on which software runs to a new one. For example, you can think of it as a major migration, like making an old PC software work on the latest smartphone. This is a really time-consuming and costly task. The migration from Win16 to Win32 that I experienced a long time ago was a real nightmare (laughs).
management: Managing the entire development project well. Managing the schedule, assigning tasks to team members, checking progress, resolving problems, etc. are the keys to leading the project to success. This management is especially important for complex projects like platform shifts.

So what role does AI (artificial intelligence) play here? AI can help solve the problems these fields face: they are too time-consuming, too costly, too short-staffed, too prone to human error, and large-scale projects are too complex to manage.

AI brings unique features:

Efficiency and automation：AI can automatically write parts of the code, quickly find bugs, and automate testing. When shifting platforms, AI can also help with rewriting old code to suit the new environment, greatly reducing the burden on developers.
Prediction and optimization：AI can predict the risk of project delays and suggest optimal task allocations based on team members' skills and workload. This allows managers to make more accurate decisions. This is the same idea as employee shift management software being optimized by AI.
Advanced Analytics and Insights: AI analyzes large amounts of project data and code change history to identify the root cause of problems and find hints for improvement, enabling higher quality software development.

In the past, I was given an unreasonable demand by higher-ups to "support both Linux and Mac OS in just six months!" If we had had AI, we could have made more realistic plans and performed risk analysis, and that tragedy (which got me fired!) might have been avoided.

The popularity and importance of AI development tools and platforms

The title of this section is usually about crypto assets (virtual currencies) and "maximum supply and circulation," but today I'll look at it from a slightly different angle. I'm going to talk about how much AI technology is "supplied" and how much it is "distributed" in the world of software development, and how that affects the "price" (cost and success rate) of projects.

AI-based software development tools and management platforms are popping up like a new gold mine. There are AIs that help you write code, like GitHub Copilot, and project management tools like Jira and Asana are starting to incorporate AI features.

Current status of "supply"：Not only major IT companies (Google, Microsoft, Amazon, etc.) but also innovative startups are competing to develop AI development tools. Open source (blueprints are publicly available and anyone can use them) AI models and tools are also increasing, and technology is evolving throughout the developer community.
Status of "Population": Developers who were skeptical at first, asking "Can we really use it?" are gradually beginning to accept AI tools after experiencing their convenience. In particular, there are more and more cases where AI is used to automate repetitive tasks and get hints for solving complex problems. Management is also looking forward to AI's data-based decision-making support.
Impact on "price (cost/value)" :
- Cost reduction：If development and testing time can be shortened through automation using AI, it may be possible to reduce labor costs. Also, early detection of bugs can lead to significant reductions in the cost of fixing them later in the process.
- Value Enhancement: Increasing development speed means new features and services can be brought to market sooner. Improving quality through AI also increases customer satisfaction and strengthens product competitiveness. If large-scale projects such as platform shifts can be carried out more smoothly while reducing risks with the support of AI, the value will be immeasurable.

However, high-performance AI tools and platforms can be costly to implement, and it is also important to develop human resources who can use AI effectively. Therefore, examining the tools that are "supplied" and wisely "distributing (introducing)" the ones that are best suited to your project is the key to creating value that is commensurate with the "price" of success.

Technological mechanisms: How does AI aid development?

You may be wondering, "How does AI help us?" Here, I will explain as simply as possible the representative AI technologies used in software development and management.

The main AI technologies in use are:

Machine Learning :
This is a technology that allows AI to automatically learn patterns and rules from large amounts of data. In software development, AI is trained to learn from a huge amount of past code, bug reports, project progress data, etc.

Example of use:
- Code generation and completion: It learns from good code in the past and predicts and suggests the next step in the code as the developer is writing it (e.g. GitHub Copilot).
- Bug prediction and detectionIt learns code patterns where past bugs occurred and points out possible bugs in new code.
- Automatic test case generation: Analyzes software specifications and existing code and automatically creates the necessary test cases.
- Project Delay Forecast: It learns from past project data and, based on current progress, warns, "If you continue like this, you may not be able to meet the deadline!"
Natural Language Processing (NLP) :
This is a technology that allows AI to understand and generate the words (natural language) we use every day. This will enable smoother communication between humans and AI.

Example of use:
- Support for requirements definition: AI analyzes customer requests (spoken and written) and organizes the specifications necessary for development.
- Automatic document generation: The AI understands the contents of the code and automatically creates an explanatory text (documentation).
- Chatbot Support: When developers are in trouble, an AI chatbot can answer their questions and provide relevant information.
Reinforcement Learning :
A technology in which an AI learns the actions that will give the best results (reward) through repeated trial and error. The Go AI AlphaGo is a famous example.

Example of use:
- Optimizing resource allocation: AI learns and suggests the most efficient way to allocate resources such as developers and equipment depending on the status of the project. This idea can also be applied to complex employee shift scheduling.
- Optimizing platform shift strategies: Through simulations, AI will find strategies such as where to start the migration work and in what order to proceed in the most efficient way with the least risk.

The combination of these AI technologies will make software development tools smarter, platform shifts smoother, and management more data-based and accurate. In the past, much of what was done relied on the experience and intuition of developers, but the future will see AI as a powerful partner.

Development team and AI technology community

For AI technology to be effective in the world of software development, it is essential to have people who create it (the development team) and people who support and nurture it (the technical community).

The reliability of the team developing the AI tools :
Currently, AI-based development support tools and management platforms are being developed by a variety of organizations, from IT giants such as Google, Microsoft, and IBM, to specialized companies specializing in specific fields, and startups with innovative ideas. They are competing to provide reliable AI solutions backed by the technical capabilities and abundant data they have cultivated over many years.

For example, "Copilot" provided by GitHub, a subsidiary of Microsoft, is based on OpenAI's powerful language model, and its code suggestion ability has amazed many developers. Projects with such proven track records and transparent development processes can be said to be highly reliable.
Active AI technology community :
The field of AI, especially machine learning, has a very active open source culture (source code is publicly available, and anyone can improve and redistribute it). Famous machine learning libraries (a collection of components that make development easier) such as TensorFlow and PyTorch are improved every day by communities of developers and researchers from all over the world.

In these communities, new algorithms (calculation methods) are proposed, ways to improve AI models are discussed, and ideas for applying AI to software development are shared. Knowledge is spread through blog posts, forums, academic papers, online courses, etc., which leads to raising the overall level of technology. Even beginners can get the latest information and improve their skills by participating in these communities.

It can be said that the existence of such an active open source community, in addition to commercial tools developed by companies, is accelerating the evolution and spread of AI technology. For us users, the benefits are more options and easier access to better tools and information.

In the past, large-scale platform shift projects like the ones I was involved in relied on the wisdom and teamwork of experienced developers. But now, in addition to that, we can leverage the wisdom and tools of the global AI developer community. This is really encouraging.

Use cases and future prospects: How far will AI evolve?

Let's talk about the exciting future of AI, including how it is being used specifically in software development tools, platform shifts, and management, and what the future holds!

Current main use cases:

AI Pair Programmer：Like a buddy who sits next to the developer and works together with them, AI will suggest code in real time, point out bugs, and give advice on the best way to write. It's like having an experienced programmer supporting you.
Platform Migration Assistant: When porting code from an old system to a new system, AI can automatically detect incompatible parts, suggest fixes, and even perform automatic conversion in some cases. This will greatly streamline the time-consuming and labor-intensive task of platform shifting.
Intelligent Project Manager: AI constantly monitors and analyzes project progress, member workload, potential risks, etc., and suggests specific improvement and countermeasures to managers. For example, "This task is likely to be delayed, so let's add resources," or "It seems that cooperation between Person A and Person B is not going well." This can be called advanced "shift management" or "workforce management" that utilizes AI.
Automation Test Engineer: Testing is essential to ensure the quality of developed software. AI can automatically identify items to be tested, generate test code, execute the tests, and analyze the results. It may even be able to find small bugs that humans tend to overlook.
Code generation from specifications: AI can understand software specifications and requirements documents written in natural language and automatically generate basic program code based on them. This could significantly reduce the time required for the initial development stage.

Outlook: The future of software development with AI

A world like this may be waiting for us in a few years, or even in the near future.

Self-healing software：Even if a bug occurs during operation, AI may be able to automatically identify the cause and apply a patch to repair the software itself. It's like a dream to have a system that can continue to run without stopping.
The evolution of no-code/low-code development：Even without specialized programming knowledge, it may be possible to develop advanced applications simply by interacting with AI or following simple instructions. The time may come when anyone with an idea can become a developer.
Fully autonomous development teams?: It may still seem like science fiction that once you set the project goals, AI will autonomously handle everything from design, development, testing, deployment, and operation and maintenance, but there is a good chance that AI will take over a large part of the development process.
Establishing AI ethics and governanceEthical issues, such as how to ensure the quality, safety, and fairness of software created by AI, as well as the creation of rules (governance) for AI development, will become increasingly important as technology evolves.

When I was in the development field, working all night and fighting bugs with unknown causes were commonplace. But in the future, developers may be able to focus on more creative and essential work with a powerful partner called AI. Of course, some people may be worried that AI will take away their jobs, but I believe that by mastering AI, human potential will be further expanded.

AI-based vs. traditional approaches: what's the difference?

What are the specific advantages of using AI to develop and manage software compared to the old-fashioned way? Here we compare the AI-enabled approach with the traditional approach to clearly see the differences.

1. Software Development Tools

conventional :
- The focus is on basic tools such as text editors, compilers (software that translates code written by humans into language that machines can understand), and debuggers (software that finds bugs).
- A large part of it relies on the developer's knowledge, experience, and intuition.
- Code quality and productivity are highly dependent on the skills of individual developers.
AI-based :
- New features include automatic code generation and completion using AI, real-time bug detection and correction suggestions, and suggestions for optimal code structures.
- AI provides developers with insights learned from vast amounts of past data.
- It will be easier to maintain a certain level of quality and productivity regardless of the developer's skill level. By leaving repetitive tasks to AI, developers can focus on more creative work.

2. Platform Shift

conventional :
- Requires extensive manual code analysis, rewriting, and testing.
- Compatibility issues are easy to overlook, which can lead to projects being lengthened and expensive.
- As I experienced, this puts extreme pressure on the development team and increases the risk of failure.
AI-based :
- AI automatically analyzes and identifies code dependencies and compatibility issues.
- AI automates some code rewriting and test case generation.
- Dramatically reduce migration time, costs, and risks, allowing developers to focus on strategic decisions and solving complex problems that are difficult to solve with AI.

3. Management (project management/team management)

conventional :
- Progress management and resource allocation rely on managers' experience, intuition, and general-purpose tools such as Excel.
- Problems are easily discovered late, and misunderstandings occur due to lack of communication.
- It is difficult to manage shifts (task allocation) taking into account employee skills and workload.
AI-based :
- AI analyzes project data (progress, issues, member work status, etc.) in real time and predicts future risks.
- Supports managers' decision-making with objective, data-based information.
- Taking into account members' skill sets, current workload, task priorities, etc., it proposes optimal task assignments (shift management by AI) and resource allocation, contributing to improving the productivity of the entire team.
- Many of the "employee shift scheduling software" and "workforce management software" that appear in Apify's search results also use AI to improve efficiency. This same idea is being applied to development project management.

In this way, we can see that AI-based approaches have the potential to solve many of the problems that traditional approaches have, and to contribute greatly to software development productivity, quality, and reducing the burden on workers. Of course, AI will not solve everything, but it is important to use it as a powerful "tool."

Risks and Cautions: Is AI Implementation All Roses?

While AI technology brings great benefits to software development and management, there are also some risks and points to be aware of. It is not an all-purpose magic wand. In order to use it wisely, it is important to understand the disadvantages as well.

Concerns over overdependence and declining skills :
If AI becomes too good, there is a possibility that developers will stop thinking for themselves and will not acquire basic skills. If you think, "It's fine because AI will do everything," you may run into trouble when you are faced with a problem that AI cannot handle.
Bias and Fairness in AI Models :
AI makes decisions based on training data. If the training data is biased, the AI's decisions may also be biased. For example, an AI that has only learned a certain programming style may judge other styles to be inappropriate. This could reduce the diversity and innovation of code.
Security risk :
There is a non-zero possibility that AI-generated code may contain unintended vulnerabilities (security weaknesses). There is also a risk that the AI development tools themselves may become targets of cyber attacks. It is still important not to place too much trust in AI and to have human checks and security measures in place.
Responding to changes in employment :
As automation by AI advances, some routine tasks may no longer be the work of humans. This means that developers and managers will need to acquire new skills or shift to more creative roles. It's important not to be afraid of change, but to be on the side of using AI.
Cost and complexity of implementation and operation :
Introducing high-performance AI tools and platforms often requires initial costs and monthly fees. In addition, to effectively use AI, you may need personnel with specialized knowledge or you may need to review your existing business processes. It's important to carefully assess the cost-effectiveness.
Black box problem :
AI, especially models that use deep learning, can sometimes be difficult for humans to understand why they made certain decisions or produced certain results. This is called the "black box problem." When entrusting important decisions to AI, if the basis for those decisions is unclear, it may be difficult to identify the cause of problems or to assign responsibility.
Data Privacy and Ethics :
When AI uses confidential corporate information (such as source code or customer data) as learning data, it must be handled with the utmost care. Appropriate data management and ethical considerations are required from the perspective of the risk of information leaks and privacy protection.

Understanding these risks and adopting AI technology carefully and systematically will be the key to maximizing its benefits. As in my past mistakes, it's important to think about how to live with new technology (and the demands of management!) rather than simply resisting it.

Expert opinions and analysis (general opinion)

Many experts and industry analysts have released various opinions and analyses about the future of AI and software development. Here are some of the most common views we hear:

"AI will become the developer's co-pilot" :
Many experts predict that AI will not completely replace human developers, but rather will be a powerful assistant, or "copilot." By taking over tedious tasks and information gathering, AI will allow developers to focus on more creative, high-level problem solving. This is becoming a reality with the emergence of tools like GitHub Copilot.
"Dramatic improvement in software quality and productivity" :
It is expected that AI will greatly improve the quality of software through early bug detection, automated testing, and optimal code suggestions. In addition, many analysts believe that productivity will also increase dramatically as many parts of the development process are automated and made more efficient. This will enable companies to bring new services to market more quickly.
"Expectations for reducing the complexity of platform shifts" :
It is believed that large-scale, complex platform shift projects, such as those I struggled with in the past, will be significantly less difficult with the assistance of AI. As AI-based code analysis, compatibility checks, automatic conversion, and other technologies advance, it may be possible to complete migrations in a much shorter time and at a lower cost than before. This is good news for many companies that are facing the major challenge of modernizing their legacy systems.
"Standardization of Data-Driven Management" :
AI-based data analysis and prediction is expected to become standard practice in project management as well. Managers will make decisions based on objective data provided by AI, rather than just experience and intuition. This is expected to improve project success rates and reduce risks. Just as AI is being used in employee performance management and shift optimization tools, development project management will also become more scientific.
"But a human-centered approach is key." :
On the other hand, experts emphasize the importance of a "human-centered" approach when introducing AI technology. AI is merely a tool, and how we use it is up to humans. There are also warnings that we should not blindly accept AI's judgments, but should always maintain a critical perspective and take ethical aspects into consideration when operating AI.

These opinions suggest that while there is hope that AI technology will bring about major changes in the future of software development, its introduction requires careful consideration and preparation. It seems that it will become increasingly important in the future to keep up with technological evolution and have the ability to discern its essence.

Latest News and Roadmap Highlights

The world of AI and software development is evolving by leaps and bounds. Here are some highlights from recent headlines and the roadmap for future technological developments.

The evolution and expanding applications of large-scale language models (LLMs) :
Large language models (LLMs) like ChatGPT are rapidly being applied in the field of software development. They are no longer simply generating code, but are now able to handle a wider range of tasks, such as creating design documents, automatically generating documentation, and naturally responding to user questions. It is expected that more advanced LLMs will appear in the future and support the entire development process.
The spread of AI-enabled IDEs (integrated development environments) :
The trend of incorporating AI functions as standard into major IDEs (integrated software used by developers to write and test programs) such as Visual Studio Code and IntelliJ IDEA is accelerating. This will allow developers to seamlessly receive AI support in a familiar environment. This will make AI more familiar to developers and further improve development efficiency.
Growing interest in "Responsible AI" :
The idea of "responsible AI" to ensure the quality, security, fairness, and transparency of code generated by AI is becoming increasingly important. Companies that provide AI development tools are also increasingly formulating guidelines to address ethical issues related to AI and developing technologies to reduce bias. This is a very important trend for the sustainable development of AI technology.
Platform Engineering and AI :
The idea of "platform engineering" is gaining attention as a way to automate and streamline infrastructure construction and operations so that developers can focus on application development. By combining this with AI, there is a movement to realize resource optimization, failure prediction, self-repair functions, and more, and to build a more efficient and stable development and operation infrastructure. This will play an important role in supporting complex platform shifts and cloud-native development.
Use of AI in low-code/no-code platforms :
AI is also increasingly being used in low-code/no-code platforms that allow users to develop applications without specialized programming skills. It is expected that AI will be able to understand the user's intent and suggest application designs, as well as automatically incorporate necessary functions, making it easier for non-experts to create more advanced software.

Looking at these trends, we can see that AI is deeply penetrating every aspect of software development and is trying to fundamentally change the way it is done. Things that were unthinkable a few years ago are becoming a reality one after another. I can't take my eyes off future technological innovations!

Summary: The future of software development evolving with AI

Well, today I explained from my perspective what potential AI technology has in the areas of "software development tools, platform shift, and management." What did you think?

AI is making things that were unimaginable when I was young and struggling with platform migration projects a reality. AI is becoming a powerful "partner" for developers and managers, such as automatic code generation, bug prediction, and optimization of complex projects.

Of course, we can't leave everything to AI, and new technologies always come with risks and challenges. But if we understand these and use them wisely, the world of software development should become more creative, more efficient, and more exciting. Instead of suffering under unreasonable demands like I did in the past, let's take AI as a tool and pave the way to the future!

FAQ: Frequently Asked Questions

Q1: Will AI take over software development jobs?: A1: I understand your concerns. However, many experts believe that AI will play a role in "assisting" human work, rather than completely taking over human jobs. Repetitive tasks and time-consuming analysis that AI is good at will be left to AI, and humans will be more valuable in areas such as more creative design, complex problem solving, and communication. If you are willing to learn new skills, AI will be a powerful ally, not an enemy.
Q2: Do I need a lot of specialized knowledge to introduce AI development tools?: A2: Depending on the tool, many are designed to be usable even without specialized knowledge of AI. In particular, AI functions built into IDEs are often designed to be used naturally in the flow of everyday development work. Of course, if you have a deeper understanding of how AI works, you can use it in a more advanced way, but I think it's best to start by "trying to use it" and gradually get used to it.
Q3: Is platform shifting really that hard? Will AI really make it easier?: A3: Yes, it's really hard (laughs). Remaking an existing huge piece of software to work in a completely different environment is like rebuilding a house from the ground up. You have to find and fix each incompatible part one by one, and repeat a huge amount of testing. AI can greatly reduce the burden on developers by automating and assisting in code analysis, identifying incompatible parts, proposing some modifications, generating test cases, etc. It may not be completely easy, but I have high hopes that it will be much smoother than before.
Q4: What are the specific benefits of project management tools that use AI?: A4: Traditional project management relied heavily on the manager's experience and intuition, but AI can analyze large amounts of data and provide more objective information for decision-making. For example, it can predict the risk of project delays early on, identify bottlenecks in tasks, visualize the workload of team members, and suggest optimal task allocation. This is expected to help deal with problems before they become serious and improve the productivity of the entire team. In a sense, AI can act as an excellent project advisor.

AI is revolutionizing the future of software development, platform migration, and management