AI has great potential to improve and accelerate software development and improve project quality, especially in improving software development efficiency.
For decades, artificial intelligence has proven its outstanding talent in a variety of industries. From robots to manufacturing, to inventory changes and currency traders predicted that artificial intelligence has become part of our lives. In today's era, businesses are using AI to automate the routine work, which makes what we previously thought impossible becomes possible. Here we detail artificial intelligence to agile project management to bring a variety of benefits.
In fact, the traditional software development will continue to exist, such as the main application components and data management software interfaces and the like will still use conventional software. Our concern is: how to use machine learning to expand the software development process? I think, in the following manner may be ML (machine learning) technology into SLDC (software development lifecycle):
a rapid prototyping:
Before AI appearance, development team spent a lot of time to convert the customer's business needs technology. Today, ML improve efficiency by helping developers lack the expertise to reduce development time and process.
Second, the risk assessment:
In software development, risk assessment is very complicated to make important decisions, and also consider the period and budget. After starting the project, internal and external environment of interdependence will produce a variety of possibilities and probabilities data. As humans, we have limited ability to store and replicate data.
AI can help you on-demand and data collection parameters. Use AI model, we can begin to collect project data from the date to the end. This way, you can get the actual schedule of projects currently under development.
Third, the analysis and error handling:
programming based AI can help developers easily identify historical patterns and common human error. During development, if we made such a mistake, then the coding assistant which will be marked. After the application is deployed, ML can be used to analyze logs and flag errors, or even fix the error. This allows application developers can take the initiative to correct the error. Maybe AI can separate application errors without human participation of the future.
Fourth, programming assistant:
In no AI software development, most of their time spent on the developer's code debugging and documentation. By using intelligent coding assistant ML implementation, developers can quickly get feedback and advice based on the code base, thus saving a lot of time. The best example is the Pythons code assist the Kite and Java Codota.
Five strategic decision:
Developers spend a lot of time to discuss the priority functions and products. AI model by using data from past development project training can be assessed application performance, helping business leaders and engineering team to develop methods to minimize risk and maximize impact.
Sixth, accurate estimates:
Software development projects are beyond schedules and budgets "repeat offenders." Therefore, to make a reasonable budget, must have a deep understanding of the team and the project background, you can use the data (such as user case, cost estimates and function definitions) past projects to train the ML model. This proved to be very helpful to predict the workload and budget.
Seven, automatic code refactoring:
It is also important to make clear the code, and then implement secure collaboration. Reconstruction of the maintenance of the code clean norms is necessary. To solve this problem, ML is used to easily analyze the code by identifying potential areas of reconstruction and optimize performance.
Eight, AI plan for the project:
the human brain is a very good knowledge engine, but each person's cognitive abilities vary. No two projects are the project manager will have exactly the same idea. By ML replication of human intelligence, AI can create all kinds of permutations and combinations similar to the situation of the human brain.
Nine, project resource management:
have the right to engage in the project depends on the delivery of software products. By AI integrated into the project, we can obtain real-time information developers are working on other projects, AI developers provide accurate information can be used for deployment. AI-based integration, we can reduce or increase the number of project developers.
● Why it is important to AI
AI can structure according to the project by providing the skills and knowledge required of developers, allowing developers to greatly enhance the efficiency of induction and delivery of the project.
If the project manager uses AI to achieve optimal allocation of workload, then believe me, you lazy developers who can not achieve 100% of the full output. In addition, by automating repetitive manual tasks, project managers can have more time for project-centric decisions.
● AI will change how software development?
In the AI system, the software developer does not provide any guidance steps or operations. Machine learning management system itself only in specific areas of data, and inputs the learning algorithm.
AI will identify patterns in the data, which is important for decision-making. Machine algorithm compares the data to its database, and make the right decisions. The best thing about AI is no established knowledge stereotype. In fact, the output of the AI usually reveal the peculiar and interesting model for humans to intuitively recognize.
Artificial intelligence by subverting human definition programming, program execution and to change the perception of the software development process. • Google's Pete Worden (Pete Warden) believe that after ten years, most of the IT work will no longer be involved in programming.
According to Andrej Karpathy former director of AI scientists OpenAI, the current Tesla say, future programmers will not maintain complex storage libraries, run-time analysis or create complex programs, they will be collecting, cleaning, marking, analysis and visualization of input data neural network.
Typically, in conventional methods, engineers use Java or C ++ programming languages provide clear steps for the computer: requirements definition - design - development - testing - deployment - Maintenance Code. In ML development model, developers only need to define the problem and lists the goals they want to achieve, data collection, data preparation, data input learning algorithm, deployment, integration and management models.
Since its 1956 inception, AI has become the key to business prosperity, many companies are using artificial intelligence to achieve automation of daily business. AI used in the agile development will bring more business benefits. These benefits include, but are not limited to: make a reliable assessment of the budget, with a utilization rate of 100% of the developers, timely access to production and development environment and error detection code refactoring advice.
About the author: Chandresh Patel is Bacancy CEO, agile coach and founder of Technology.
Note: This article is compiled from dzone.com (Source: Agile US)
