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How to apply AI for software development: an inside look from JEVERA engineers

92% of U.S.-based developers use AI coding tools to optimise their work. GitHub came to this conclusion after polling 500 engineers. AI democratisation became a driver of a new era of increased dev velocity and faster digital transformation processes.

The JEVERA tech team decided to shed light on 5 FAQs developers might have regarding AI usage in programming.

If you need to explore how AI can make a dev’s life easier and discover more about data security concerns the technology leads to, this piece is for you. Scroll down to find comprehensive answers.


AI in software development cycle: 5 FAQs to cover

What software development tasks AI helps optimise

Given the multitude of mundane tasks that software developers contend with, AI assistance emerges as a lifesaver, preventing professionals from succumbing to burnout. The JEVERA team meticulously examined numerous ways Artificial Intelligence can streamline product development to create a comprehensive and categorised list of processes.

AI as coding assistant allows engineers to accelerate:

  • quartz, regex, JSON/XPath, SQLs, bash/CI scripting

  • migration of code snippets to newer versions of external dependencies

  • refactoring

  • generation of code snippets documentation

  • code snippets analysis and explanation

  • finding defects in code snippets

  • conversion between data formats

  • POJO generation according to some specs like (JSON, XML, YAML, etc.)

  • search for documentation of libs/frameworks, etc.

Artificial Intelligence supports testing processes by performing:

  • basic tests coverage generation with mocking

  • API tests coverage and test cases generation (via OpenAPI specs)


AI simplifies project management activities by:

  • retrospective facilitation



What AI tools are best suited for software development?

For code generation and autocompletion:

  • TabNine, an AI-powered autocompletion tool that suggests code completions as you type. It learns from your codebase and offers context-aware suggestions.

  • Kite, an AI-powered developer tool that provides intelligent code completions and documentation right in your code editor.

For code review & bug detection:

  • DeepCode, an AI-based product to find and fix coding issues in your codebase by providing suggestions and automated fixes.

  • Sentry, an environment that offers real-time error tracking and crash reporting with AI-powered insights to help you prioritise and resolve issues efficiently.

For test automation:

  • Applitools, a software solution that employs visual AI to automatically validate the visual appearance of your web and mobile applications across different devices and screen sizes.

For Natural Language Processing:

  • GPT-3/GPT-4 software capabilities can be used for generating documentation, writing code comments, and assisting with natural language queries related to your codebase.

For version control and collaboration:

  • GitHub Copilot, an AI tool that assists you by providing code suggestions and documentation right in your code editor.

Top AI tools for software development

To expedite the development process and handle routine tasks, the JEVERA team primarily utilises GPT-3/GPT-4, which produces accurate and understandable code. Concurrently, our developers are in the process of becoming proficient with Copilot, another tool that aids in delivering top-notch software quickly and enhances engagement.

Should AI be compulsory to use for each developer?

When considering whether AI should be a mandatory tool for every developer, just like an Integrated Development Environment (IDE), the answer is no. Like other professionals, engineers should have the autonomy to decide when and if AI tools are best suited for their tasks.

AI can streamline tasks and simplify the implementation of algorithms, making life easier. However, it's crucial to understand that artificial intelligence is not a replacement for human expertise but a supplementary tool for accomplishing specific objectives.

Furthermore, AI can sometimes be counterproductive for aspiring developers. Overreliance on technology can limit the opportunities for skill development. If a novice relies on AI to write basic code, it's unlikely that they will progress further in their software development journey.

Sensitive data: What concerns AI leads to?

Amazon, Anthropic, Google, Inflection, Meta, Microsoft, and OpenAI. What do they all have in common, except that we're talking about top AI providers and drivers of AI democratisation?

In July 2023, the White House published a Fact Sheet according to which these giants secured voluntary commitments to ensure their products are safe. One of the obligations companies took was developing robust tech mechanisms showing AI-generated content, such as a watermarking system. This approach aims to prevent the core concerns developers and businesses have regarding AI generative tools - data accuracy, privacy & security.

The legal aspects of using AI are one of the biggest pitfalls when applying tools for the software development process. Sometimes, developers receive pieces of code for refactoring and try to complete the task with Artificial Intelligence solutions. However, after a final check, it may turn out that AI-generated code could contain sensitive data that should remain confidential.

This way, developers face the problem of AI usage to work with sensitive data covered by the NDA. To respond to this challenge, reputable vendors started offering extra layers to prevent unintentional data leaks. For example, recently, Salesforce introduced the Einstein GPT Trust Layer to separate sensitive customer data from the Large Language Models used to create generative AI.



What best practices are applicable in terms of cybersecurity for AI tools?

Data Governance

  • Establish stringent access restrictions

  • Employ encryption

  • Implement data anonymization methods throughout the lifecycle of sensitive data, from acquisition and storage to processing

Robust AI Safeguarding

Utilise secure coding methodologies, conduct thorough testing, and maintain ongoing vigilance through monitoring for AI systems to detect and remediate potential vulnerabilities.

Contingency Strategies for Security Incidents

Formulate a comprehensive incident response strategy detailing the precise actions to undertake in case of a security breach or data exposure.

Employee Awareness

Consistently educate developers on the significance of data privacy and security and establish well-defined protocols for managing sensitive data while leveraging AI.


Afterword

AI is a powerful technology that can drive innovation and efficiency in software development. However, it should be viewed as a complementary force, not a replacement for human expertise. With the right balance and a commitment to best practices, Artificial Intelligence can empower developers to create better software faster and contribute to the ongoing digital transformation of industries worldwide.



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