software development. It enhances the traditional processes using AI-enabled solutions and tools. However, the impact of AI and software development integration poses doubt among stakeholders. This article goes in-depth on how AI transforms traditional processes into smart programming. Let’s get started!
Software development is a technology that drives product innovation. The traditional software development lifecycle (SDLC) presents incredibly complex components as the size of the project gets bigger.
Developers deal with multiple dependencies, integrations, interfaces, and many more. Despite the use of development tools, these components are manually managed and updated.
Software Development Process with AI Integration
First, let’s discuss the traditional Software Development Lifecycle in a nutshell. At the start of the process is the requirement analysis that includes technical specifications, user expectations, etc.
The design follows, then moves to the development. Quality Assurance testing is next as soon as a viable prototype is available. As it passes the QA inspection, the production follows, and lastly, executes continuous maintenance.
On the other hand, the machine learning model development process includes:
- Problem and goals definition
- Data collection
- Data preparation
- Model learning
- Model deployment and integration
- Model management
The problem and goals definition still requires human intervention. This phase utilizes natural language processing. It makes the machine understand the user’s requirements, and it automatically converts natural language into high-level software models.
As you notice, data plays a vital role in applying ML techniques to software development. These are called ‘machine data’ in the form of logs, metrics, etc.
Data collection consists of techniques such as knowing the valuable data to collect, collection mechanisms, data format, and others.
Data preparation is quite difficult because of the notion of big data. However, make sure to establish data reduction, complete data cleaning, data decomposition, and data rescaling. In short, the main goal here is how to gather and prepare the right data.
Model learning is when the model learns from data rather than human instructions(i.e., code, functions). To achieve model learning, it must be an iterative and less predictable process.
It must identify the appropriate performance goal of a project. Also, it includes processes and several techniques if the model’s performance didn’t meet the requirements.
Model deployment and integration select a model to deploy. It consists of techniques such as:
- Feature engineering – develop new features that make sense from the existing ones and select features to include into the model.
- Algorithm selection – choose the model that works best with the available data.
- Optimization – fine-tune the most important hyperparameters for each model.
- Model deployment – deploy the best-performing model into action.
Model management is the counterpart of maintenance in SDLC. But, the codes here are written in the form of “neural network weights” by machine learning methods—i.e., stochastic gradient descent. This process interprets important features and patterns of the data.
Benefits of Artificial Intelligence in Software Development
It is critical to establish the difference between traditional and AI-enabled software development. Remember that the traditional process is not going away.
However, let’s examine how artificial intelligence benefits several areas of development that are too complex for humans to comprehend.
Business requirements in software development need plenty of time and planning. Machine learning helps the developers shorten this process by developing algorithms that lessen the documentation time.
Such technologies include visual interfaces of natural languages that recognize and track historical patterns.
Intelligent Development Assistants
Intelligent development assistants help save time in coding preparation. They provide in-time support and recommendations. This reduces the vast amount of time spent on reading the project’s documentation.
Also, they help with code debugging and provide insights like code examples, needed libraries, dependencies, and others.
Analytics Automation and Error Handling
Developer assistants help the team save time as they analyze the system logs. It allows early error detection for a much more rapid and proactive flagging of errors.
In the future, there is a huge possibility that these tools will not only detect errors but will enable the software to execute error correction without any human intervention.
Automatic Code Refactoring
Developers always want to have simple and clean code. AI-enabled tools provide ease in code refactoring as it promotes efficient team collaboration. This is beneficial for huge upgrades with large-scale refactoring and long-term maintenance.
Forecast Precise Estimates
AI and ML improve price forecasting through data analysis from past projects. The accuracy of estimates involves training data—user stories, feature definitions, etc.—to predict cost, time, and effort. To achieve predictive analytics, use data to find correlations and statistics.
Artificial intelligence analyzes the performance of past and existing projects. Thus, AI-enabled platforms gain experience from the past mistakes of the project.
This is called smart programming. It aids development teams to identify design loopholes and maximize risk assessment.
AI Powered Tools for Software Development
Many enterprises use various tools to develop AI-based software. It helps streamline the development process.
On that note, when you hire software developers, ensure that they are knowledgeable of these tools:
- Google ML Kit – This tool is for mobile applications and optimization. Take advantage of the ML expertise powered by Google with its easy-to-use and powerful package. It helps you customize models according to the project’s specifications.
- TensorFlow – It helps developers deploy ML-enabled applications with its end-to-end open-source platform. It aids researchers with its comprehensive tools and libraries coupled with community resources for faster development.
- MxNet – It is a tool for deep learning and has a rich ecosystem of libraries and tools. An open-source framework that is ideal for both professional and novice developers. It provides distributed training and deep integration with other languages such as Python, C++, and others.
- Infosys Nia – It helps developers to transition from artificial intelligence experimentation to production. It is a good tool for decision making as it derives insights from the project’s documentation. It can manage siloed data assets generated from legacy systems.
- H2O – It is an intuitive and straightforward framework with autoML functionality. It runs algorithms and hyperparameters to produce the best model. It supports statistical and machine learning algorithms to create accurate predictions.