Areas of application of AI in software development

Artificial intelligence and machine learning have been implemented in software products for a long time. In this article we explore the potential applications of artificial intelligence in the development of information systems.

Areas of application of AI in software development

Artificial intelligence technologies can be successfully used at all stages of software creation. Let's take a closer look at these features:

1. Collection of technical requirements.

Digital assistants analyze requirements documentation, identify inconsistencies in texts, and inconsistencies in data, such as units of measurement and amounts. They can also offer solutions to resolve these differences.

2. Rapid prototyping.

The process of translating business requirements into software code has traditionally been a time-consuming process. However, machine learning techniques significantly speed up this process, allowing less experienced developers to create prototypes using natural language or visual interfaces.

3. Coding.

As you write code, AI-powered autocompletion systems suggest ideas for completing strings. Intelligent assistants can cut development time in half, as well as recommend referencing related documentation and best practices by providing code examples.

4. Error analysis and handling.

Virtual assistants can learn from past experiences to find common mistakes and flag them during the development process. The use of machine learning allows you to effectively analyze system logs for timely detection and prevention of errors.

5. Automatic code refactoring.

Clean code is the key to collaborative development and maintenance. Machine learning helps analyze and optimize code to improve readability and performance.

6. Testing.

Automated test systems use AI not only to run tests but also to create test scripts.

7. Commissioning.

Some errors only appear after the software has been deployed. However, AI tools allow you to avoid such situations by conducting a preliminary analysis of previous releases and logs.

8. Project management.

Software development often faces budget and schedule overruns. Modern analytics systems can use data from multiple project assets to predict required resources, tasks, and implementation time.

Integrating AI/ML into DevSecOps

DevSecOps is a whole stage in the evolution of DevOps, changing the traditional understanding of the role of information security departments in ensuring code quality and reliability. Previously, security specialists were involved at the final stage of development before release, but now their direct participation is required at all stages. The DevSecOps methodology is popular today, and Gartner analysts point to growth in its productivity in 2022, which promises many opportunities for further customization.

Russian companies are increasingly moving to developing software in-house. This makes it possible to respond more quickly to changes in user requests and remain independent from external factors, maintaining flexibility and competitiveness. However, the growing need for software security is becoming urgent, especially with the increasing number of cyber attacks.

ASOC (Application Security Orchestration and Correlation) platforms offer extensive functional benefits, including centralized management capabilities. For intelligent management, it is necessary to create an additional module for data consolidation and analysis. This includes adjusting implementation steps to process raw data, metrics and KPIs using AI and machine learning technologies to generate dashboards based on the processed data.

Practical application of AI/ML technologies in DevSecOps

Automatic software quality check.

AI can dynamically generate quality control criteria for developed artifacts based on the collected data. This will allow you to automate the process of releasing assemblies through DevSecOps pipelines as much as possible.

CI/CD pipeline as code.

The approach to managing CI/CD pipelines as code simplifies the processes of their deployment, management and status tracking. ASOC class solutions allow you to quickly create CI/CD pipelines with precisely specified quality criteria.

Vulnerability Correlation (AVC).

AI/ML technologies help identify and group duplicates and similar security issues, which significantly reduces the time to resolve them and allows you to focus on the most critical vulnerabilities.

Automated guidance for resolving vulnerabilities.

AVC technologies can prioritize and recommend solutions for critical vulnerabilities, and ASOC platforms aggregate data from various security testing tools to provide secure code templates.

Information security compliance management.

Automation of requirements management processes greatly simplifies tasks, and continuous monitoring based on quality control points ensures software compliance with the necessary norms and standards.

Using AI and machine learning technologies in DevSecOps is a powerful tool for improving the quality of software development and ensuring software security.