Enterprise AI Analysis: Decomposing Complex Bug Reports with LLMs

Executive Summary: From Academic Insight to Enterprise Action

The study by Chen et al. provides a critical empirical look into whether Large Language Models (LLMs) can tackle a persistent bottleneck in software development: overly complex bug reports. These reports, often containing multiple intertwined issues, create significant drag on developer productivity and extend issue resolution times. The research systematically tested leading LLMs, ChatGPT and DeepSeek, on their ability to decompose 127 real-world bug reports from the Apache Jira repository into smaller, actionable tasks.

The key takeaway for enterprises is twofold. First, off-the-shelf LLMs, when used with simple, generic prompts (a "zero-shot" approach), perform poorly, achieving a success rate below 10%. This highlights the risk of naively integrating generic AI tools into critical workflows. Second, with moderately improved, context-aware prompts that include examples (a "few-shot" approach), performance increased by over 140%. While still not perfect, this dramatic improvement proves that the true value of LLMs is unlocked not by the model itself, but by the expertise in how it's prompted, guided, and integrated. This is the core value proposition of a custom AI solutions provider. The paper identifies key failure pointssuch as "over-decomposition" and "over-analysis"which serve as a precise roadmap for building robust, enterprise-grade AI systems for bug triage and management.

The Enterprise Challenge: The Hidden Cost of Bug Complexity

The research begins by quantifying a problem every software engineering leader feels: the immense manual effort required to resolve bugs. The study's analysis of 127 bug reports from Apache Jira revealed a sobering reality.

Based on the paper's findings (Table I), it takes an average of 175 days from a bug's creation to its resolution. Even more tellingly, it takes an average of 141 days from the *first developer comment* to resolution. This lag represents a massive drain on resources, delays product roadmaps, and directly impacts software quality and customer satisfaction. The core issue, as the paper explores, is the initial complexity. When a single report bundles multiple issues, developers must first spend precious time untangling the report before they can even begin to code a fix.

Key Finding 1: The Pitfall of "Plug-and-Play" AI

The first research question (RQ1) in the study serves as a crucial cautionary tale for any enterprise looking to adopt AI. The researchers tested the baseline capability of ChatGPT and DeepSeek using simple, direct prompts to decompose bug reports. The results were starkly ineffective.

ChatGPT correctly decomposed only 10 out of 127 reports (a 7.9% success rate), and DeepSeek managed 11 (an 8.7% success rate). This demonstrates that simply "plugging in" a powerful, general-purpose LLM to a specialized, nuanced task like bug report analysis is a recipe for failure. The models struggled to understand the context, often splitting single issues into multiple confusing fragments or misinterpreting technical details.

Key Finding 2: The Power of Customization and Context

The second research question (RQ2) is where the path to enterprise value becomes clear. The researchers moved from simple prompts to a "few-shot" strategy, where they provided the LLMs with a few high-quality examples of correct bug decomposition. The impact was immediate and dramatic.

• ChatGPT's performance jumped from 10 to 24 correct decompositions, a 140% increase.
• DeepSeek's performance soared from 11 to 29 correct decompositions, a 163.6% increase.

This finding is the cornerstone of our philosophy at OwnYourAI. The value isn't just in having a powerful model; it's in the expert-led process of tailoring its application to your specific data and workflow. While the overall success rate (around 20-23%) still requires a human-in-the-loop, the massive performance gain proves that expert prompt engineering is the key to unlocking ROI from LLMs.

Interactive Error Analysis: A Blueprint for Building Robust Systems

Perhaps the most valuable part of the study for enterprise implementation is its detailed breakdown of *why* the LLMs failed. Understanding these failure modes allows us to build custom solutions with safeguards and targeted tuning to prevent them. The paper identifies four primary causes of incorrect decomposition.

Enterprise ROI: A Hypothetical Case Study

Let's translate these findings into tangible business value. Consider "GlobalTech," a software company with 200 developers. They process around 400 complex bug reports per month. Based on industry averages and the paper's findings, let's assume each complex report takes a developer 2 hours just to analyze and decompose before coding begins.

Manual Analysis Cost: 400 reports/month * 2 hours/report = 800 developer hours/month.

By implementing a custom AI solution from OwnYourAI, trained and tuned using the principles derived from this research, GlobalTech can target a 70% accuracy rate for automated decomposition. This doesn't eliminate human oversight but transforms the workflow. Developers no longer start from scratch; they review and validate AI-generated sub-tasks, a much faster process.

Implementation Roadmap: Your Path to AI-Powered Development

Adopting an LLM for bug decomposition isn't a single switch to flip. It's a strategic, phased process. Drawing from the paper's methodology, we propose a four-phase roadmap for successful enterprise implementation.

Conclusion: The Future is Custom-Tuned AI

The research by Chen et al. provides invaluable, data-backed evidence for a fundamental truth in enterprise AI: out-of-the-box solutions provide out-of-the-box results. True transformation and ROI come from expert-led, custom implementations that understand the nuances of a specific business problem. The study's journey from poor baseline performance to significant improvement via prompt engineering is a microcosm of the value OwnYourAI delivers.

By understanding LLM failure modes and leveraging proven techniques to enhance performance, enterprises can turn the chaotic, time-consuming task of bug triage into a streamlined, AI-assisted workflow. This frees up senior developer time from administrative overhead to focus on what they do best: building innovative, high-quality software.