Leveraging LLMs for Software Complexity Metrics Analysis
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Chapter 1: The Intersection of Software Complexity and AI
Throughout my PhD journey, I developed a keen interest in Software Complexity Metrics. I even took the initiative to create and expand various metrics during that time.
During my doctoral research, I conducted extensive experiments, particularly analyzing Maven to assess the utilization of Domain-specific Languages (DSLs) within the Java ecosystem.
Upon the release of ChatGPT, I was fascinated by its capability to comprehend programming concepts effectively. It became evident that it possessed substantial knowledge about complexity metrics as well.
One of the significant challenges in employing these metrics to evaluate software quality is the lack of reliable measurement tools. Each tool tends to have different implementations, and in our diverse programming environments, finding comprehensive solutions can be daunting.
Wouldn't it be ideal to have a tool that understands both (a) software complexity metrics and (b) a wide array of programming languages (or at least the most widely used ones)?
Fortunately, there are several LLMs available, and for this discussion, I will refer to ChatGPT.
Consider the following program:
The name suggests it implements a visitor pattern. Let's attempt to compute the Halstead metrics for this method:
ChatGPT delivered a detailed analysis of each metric prior to calculating the overall value. Quite impressive, isn't it?
Indeed! It can perform this task across multiple languages (without me needing to specify that it was Java). However, there are three challenges you might face when using such models:
- Occasionally, the LLM may provide inaccurate or incorrect answers.
- It's not straightforward to input an entire program into the LLM for calculations; you often need to adjust your approach.
- The cost can be prohibitive. :)
I am optimistic that time will address these three challenges, and that the tools related to software engineering will evolve into AI-centric processes.
Section 1.1: Exploring LLM Performance in Domain-Specific Scenarios
Understanding how to evaluate the performance of large language models within specialized domains is crucial. The following video provides insights into this topic:
This video discusses strategies for assessing LLM performance tailored for specific use cases, highlighting the practical implications of these evaluations.
Subsection 1.1.1: Video Insights on AI in Software Development
Section 1.2: Maximizing AI with Large Token Counts
As we delve deeper into the capabilities of AI, it's essential to understand how to optimize LLMs, particularly in terms of handling larger token counts. The following video explores this concept further:
This video elaborates on techniques to enhance productivity and efficiency when working with LLMs that manage extensive token inputs, showcasing their potential in software development.