A software company builds tools for customers. The company wants to use AI to increase software development productivity. Which solution will meet these requirements?

Question

Options

• AUse a binary classification model to generate code reviews.
• BInstall code recommendation software in the company's developer tools.
• CInstall a code forecasting tool to predict potential code issues.
• DUse a natural language processing (NLP) tool to generate code.

• B
  93% (25)
• C
  4% (1)
• D
  4% (1)

Explanation

Explanation

Installing code recommendation software directly into developer tools (like IDEs) is the most practical and targeted AI solution for increasing software development productivity, as it provides real-time, context-aware code suggestions that help developers write code faster and with fewer errors - tools like GitHub Copilot are a prime example of this approach.

Why the distractors are wrong:

• Option A is incorrect because binary classification models are designed to categorize data into two groups (yes/no, true/false) - they are not suited for generating nuanced, multi-faceted code reviews.
• Option C is incorrect because a "code forecasting tool" addresses predicting future issues rather than actively boosting productivity during the development process itself.
• Option D is partially tempting, but NLP tools are primarily designed for processing and understanding human language - while some NLP capabilities overlap with code generation, this is a less precise and direct solution compared to dedicated code recommendation software.

Memory Tip

Think "productivity = speed + accuracy at the point of work." Code recommendation software works inside the developer's existing tools in real time - it's like having an AI co-pilot always present. If the question mentions increasing developer productivity, look for solutions that integrate directly into the development workflow.

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