Tech Enthusiast with expertise in Start ups
Artificial intelligence (AI) affects the majority of industries all over the world. There are a lot of definitions of what AI means, but we think that the most relevant is to consider artificial intelligence as the process of adaptation to a new environment, collection of new knowledge, and using it in new scenarios.
AI can be adapted to all spheres of human life; it can automate some processes or partly replace humans. AI systems and tools can demonstrate some of the human intelligence behavior - learning, planning, motion, creativity, manipulation, and many others. Generally, artificial intelligence is a form of adaptable intellect, which can inherit human behavior.
Let's make it clear how it can specifically influence the software development industry and how the industry can benefit more than others from adopting AI technologies. Almost every part of the process can be improved with the use of AI. However, it is important to consider that AI itself cannot fully replace humans in complex processes, that is why AI is used as the additional help and the automatization of the production process in the software development industry.
The software development industry is on the edge of technology and receives the most advanced and technologically complex solutions. Developers use these tools for such purposes as automated testing, planning and scheduling, decision making, code compilation, and many others. AI makes the job of software developers much easier. Let’s take a closer look at how AI is changing the software development industry.
AI-Based Programming Assistants
The first thing we want to discover is AI-based programming assistants. They are very popular among developers. Generally, they fulfil the developer’s need in code debugging, code-driven testing, and code completion. For example, Kite is a solution for code editors that allows developers to complete code. Another example is Codota, which can complete code lines in almost all programming languages. All these tools aim to fulfil the need of developers in clear and working code. They can both fix syntax and complete the code line, based on a library. Developers of this software created a model, which uses machine learning to complete the code lines. It allows typing less and increasing productivity.
Fully-Automated Software Testing
AI is important in software testing. It can make the testing process faster, less time-consuming, and easier. First of all, AI increases the accuracy of the whole testing process because a human can make some mistakes - they lose concentration while working with a big amount of data. Machine testing will come through various tests, while automatically finding bugs and creating reports.
It can give the tester more time for other important tasks. For example, Eggplant’s performance and load testing can give a company a significant opportunity for this type of testing and reduce a customer’s negative experiences. Another example of AI automated testing is Testsigma. This tool can help a tester to write and run automated tests that rapidly improves the efficiency of the QA department.
More Precise Planning and Cost Estimation
Nowadays AI is widely used in planning. Software development companies can develop their development roadmap with the use of these technologies. Generally, an operator sets the initial state of the company, a description of the desired goal, and a list of possible actions. Based on this model, the software calculates all possible results and outcomes that improve decision making and helps adjust plans on the fly. Cost estimation uses a similar model. The operator creates a variety of possible variants and sets the goal for the system. Machine learning algorithms calculate and find the most cost-efficient way to manage the business. The advantages of this approach are:
Precise planning, as well as cost estimation, can influence the software development industry. Not only does it help companies better manage financial assets, but it also allows them to plan ahead. However, machines cannot react to information changes and address complex issues. That’s why experts should analyze these results and develop reasonable conclusions.
Sophisticated Code Compiler
Artificial intelligence managed to get to code compilers. Software developers use them to transfer the code to machine language. An example of this type of technology is Glow. It utilizes machine learning to improve the quality and speed of compilation for software developers. This compiler is still in active development and developers are maintaining their product. Another example of a code compiler is compiler.ai, it also runs AI software to optimize programs.
Strategic Decision Making
Strategic decision-making algorithms help stakeholders to choose the right path for the development of the software development company. These algorithms can enhance this process with the use of machine learning and AI. You have to choose the major KPI of your business, as well as additional important indexes, and set up a framework.
By the use of complex processes and methodologies, the software will analyze and construct the best possible way of decision making in an IT team. This type of software produces efficient decisions, however, management should review these results and correct them. Even though AI transforms the decision-making process, the algorithms are not perfect and cannot adapt fast to changeable environments. Especially if this is the software development industry where trends are changing very fast.
To sum up, artificial intelligence software programs utilize machine learning, or more specifically, deep learning capabilities for adaptation in new environments, as well as autonomously collects new data for its work.
In other words, this type of software mimics human behavior. This type of technology is widely used in the software development industry. AI-powered tools are making software engineers and testers more productive and more effective that allows them to produce higher-quality software solutions. Not only does it help companies create and implement new software more efficiently, but software engineers can also learn to code more easily. With that in mind, technology leaders should opt for AI-powered tools to improve the software development process.
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