Human-driven software development era means writing a rule-based code to use logic to solve the problem of uncertainty. The era of machine learning here, artificial intelligence for software development will forever change the programming. It is no longer about the definition of if-then-else loop, but more to choose the right data to train the neural network, which will solve the given problem without human intervention.
This is the way to solve problems, use tools, programmer-defined mode of thinking and even made the revolution. We will examine some of the methods of artificial intelligence can enhance software development, some of the pitfalls and ultimately why this method is valuable.
How to promote artificial intelligence software development?
Even artificial intelligence software development around the obvious hype, the technology is still in its infancy, and it will continue for several years to use on a larger scale. In addition, in some respects even better managed through traditional software. Here are some portions of artificial intelligence and machine learning can play a role.
MVP quickly create
traditional programming require months of planning and preparation to start the project. Promote it to the prototype level to get more money is another difficult step requires the necessary resources. Now, through machine learning, this cycle can be shortened to a few lines of code, or just drag and drop. A good example is to create a chat robot by using a predefined library or use natural language user-friendly codeless platform. Imagine standard languages such as C ++ how long it takes.
Project management
experienced project managers understand the lead time from the past cases of possible delays, the most common pitfalls and other help to keep time and money budget details. If you store all this data, you can use it to train the automated system to produce an accurate estimate. Since these are actually working mode detection, the use of deep learning is the best choice. You just need a detailed log of past projects, including error, estimated and actual values, and even your stories and comments.
It's estimated delivery schedule and a good way to ensure the fulfillment of obligations under the initial contract. Since the plans to spend more time to study the performance of the team and obstacles, so it can understand the personal habits, and can create a personalized work schedules, including the mode of operation of each member in order to achieve maximum efficiency.
Auto debug
mode can detect more deeply identify and classify the type of error. Depth learning algorithm can mark known errors and speed up the debugging process. It can even affect programmers to learn how to fix them. After sufficient training, the machine can automatically correct all errors, just like the same AutoCorrect on the smartphone. The only problem with this approach is correct things need to change in a similar annoying effect.
Smart Assistant
Most modern programming environments have some embedded help, such as auto-complete suggestions or other types of interactive documents. We have an intelligent assistant can speed up the development process and help newcomers understand the environment than by trial and error more quickly.
Artificial intelligence can act as a trainer, make recommendations, code samples or simply prevent coding errors, such as closing parenthesis. A good example of this is the development of Python assistant is Kite.
Automatically generate code
just completed recommended code, once it is necessary to understand the model, artificial intelligence system the code may be generated by a combination of some predetermined module (e.g., LEGO member). At some point in the future, the development of artificial intelligence software will replace some junior programmers work, since this is the first step in programming the machine.
Automated testing
in the development cycle, testing is an important part of high-quality software products. One of the challenges of software testing is to create some extreme cases contains a comprehensive list of the most likely case, and may have a significant impact on program performance. AI can automatically generate test cases to run the system by looking at the list of past logs to complete this operation.
It can even predict the test results, without the actual test, the test results are only concerned with potential problems, thus saving time in the process has been the case of late.
Strategy
When creating a software product, generally talking about what features to include and what features you want to keep later. Artificial intelligence can generate analog output and the hierarchy of the best features in order to obtain a successful product or by analyzing retrieved from product reviews and social media to the voice of the customer based on usage of similar products.
Black box effect
While many algorithms based on artificial intelligence provides a good prediction and automation, but they have some drawbacks. Learning algorithm way completely opaque to the outside observer. The only way to repair algorithm is to provide a new data set and view the output. In terms of fine-tuning this way is inefficient. This is not trivial, because it can lead to very dangerous consequences and prejudices, like a child unsupervised in a person to understand the world just by browsing the Internet.
Programmers will disappear?
Applications listed above can accept the idea: within a few years, software developers will slowly become obsolete, self-programming machines will be replaced. Because artificial intelligence system has just started to become more reliable, this situation does not occur in the foreseeable future. However, we can safely assume that artificial intelligence will increase the importance of, and provide explicit support role for developers. New tools will shorten the development cycle of new products, will serve as training and support, overall production will contribute to better, more affordable software.
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