### 2. Smell Detection Strategies
Tools like **SonarQube** help identify anti-patterns, but traditional smell detection is optimized for monolithic repositories. In microservices, anti-patterns can extend across various components.
**The key insight**: Smells often only signal *symptoms* of deeper problems. Dependencies provide a foundational view of system interrelations without requiring pre-defined rules. Comparing system versions makes the impact of changes more quantifiable than merely relying on anti-pattern catalogs.
### 3. Architecture Reconstruction Strategies
Architecture reconstruction maps services and their interactions while delving into underlying dependencies and architectural logic. Recent advances enable automation of extracting and analyzing both static and dynamic aspects of codebases.
**Challenge**: Focus on explicit dependencies and control flows often overlooks subtler, implicit connections between components. Enriching reconstructed models with comprehensive dependency mapping enables more robust analysis.
### 4. Architectural Rule Violation Detection
Rules dictate that service layers communicate solely through specified interfaces, avoiding unauthorized access patterns. Effectiveness hinges on comprehensive cataloging of all dependencies—missed dependencies diminish rule effectiveness.
---
## A Practical Example: Fraud Detection System
Consider a fraud detection system monitoring transactions for fraudulent activities:
### Data Dependencies & Solutions
Data dependencies arise when microservices share or access the same data entities. Changes in data schemas must be coordinated across services.
**Mitigation patterns:**
- **Tolerant Reader**: Design services to handle unknown or extra data gracefully
- **Request Mapper**: Transform incoming requests to decouple internal logic from external data formats
### Control Dependencies & Solutions
Control dependencies arise when one service's execution depends on the control flow or state of another.
**Tools:**
- **TraceNet**: Analyzes tracing data to pinpoint root causes of problems
- **GDC-DVF**: Maps invocation relationships and extracts dependencies for monolith-to-microservice transitions
### Communication Dependencies & Solutions
Communication dependencies involve service interactions through synchronous or asynchronous mechanisms.
**Tools:**
- **ChainsFormer**: Analyzes communication patterns to identify critical paths for resource provisioning
- **GSMART**: Creates service dependency graphs (SDGs) for tracing relationships and regression testing
### Resource Dependencies & Solutions
Resource dependencies cause contention and performance degradation when services share computational resources.
**Tools:**
- **DeepScaler**: Uses affinity matrices to scale microservices dynamically based on usage patterns
- **SYMBIOTE**: Monitors coupling metrics to detect architectural degradation and optimize allocation
---
## Maintainability: The Five Quality Attributes
Dependencies directly impact the five ISO 25010 maintainability attributes:
with questions or to discuss the research!
---
### AI for Microservice Log Analysis: Key Insights from 82 Research Studies
**URL:** https://arfanu.com/blog/ai-microservice-log-analysis-slr
**Date:** 2026-01-20
**Author:** Arfan Uddin
**Description:** A comprehensive analysis of AI techniques for microservice log analysis, covering anomaly detection, root cause analysis, and the research-practice gap affecting enterprise adoption.
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info: **—analyzed **82 primary studies** from 2,208 papers published between 2018 and 2025, examining how AI is being applied to microservice log analysis. The findings reveal both exciting progress and significant gaps between academic research and enterprise needs.
---
## The Big Picture
Before diving into details, here's the landscape of AI-powered log analysis research at a glance:
### Why Different Techniques Excel
---
## The Anomaly Detection Bias
Not all log analysis tasks receive equal attention. Here's where researchers focus their efforts:
---
## The Dataset Problem
Perhaps the most significant finding is the disconnect between research environments and real-world conditions. **79.3% of studies** use synthetic or private datasets—raising serious questions about generalizability.
Models trained on clean, well-structured synthetic data may struggle with the noise, inconsistency, and scale of production logs. Only **20.7%** of studies use public benchmarks.
### Public Datasets Available Today
For researchers looking to improve reproducibility:
---
## Tools You Can Use Today
Our review identified numerous open-source implementations. Here are the most notable tools across different categories:
### For Anomaly Detection (Sequence-Based)
- **** — LSTM-based approach that learns sequential log patterns. The foundational work that many later tools build upon.
- **** — Combines attention-based GRU with hierarchical classification. Great for scenarios with limited labeled data.
### For Anomaly Detection (Transformer/LLM-Based)
- **** — Template-free log analysis using pre-trained BERT. Eliminates the need for manual log parsing.
- **** — Uses GPT-4 for in-context reasoning to explain and classify anomalies. Shows promise for one-shot root cause analysis.
### For Dependency-Aware Analysis (GNN-Based)
- **** — Models spatial-temporal trace event graphs for systems with complex service dependencies.
- **** — Unifies invocation path and response time analysis using deep Bayesian networks.
### Supporting Infrastructure
- **** — Pre-trained models for building custom log analysis solutions
- **** — GNN implementations for graph-based analysis
- **** — Collection of system log datasets for benchmarking
---
## Performance: The Good and The Concerning
### What's Working
When AI techniques work, they work impressively well:
### What's Challenging
However, deployment challenges remain significant:
- **56% of studies** report data limitations (label scarcity, quality issues, log heterogeneity)
- **51% of studies** face reliability concerns (false positives, concept drift, model degradation)
- **50% of studies** encounter resource constraints (GPU requirements, training time, inference latency)
---
## Recommendations
### If You're a Researcher
1. **Prioritize realistic datasets** — 79.3% of studies use synthetic data. Work with enterprise partners to access production logs.
2. **Address efficiency alongside accuracy** — 50% of studies report resource constraints. Production systems need lightweight solutions.
3. **Explore the gaps** — Fault diagnosis (6%) and dependency modeling (4%) are underserved but critical for practitioners.
4. **Design for drift** — Production logs evolve continuously. Online learning approaches are needed.
### If You're a Practitioner
1. **Start with anomaly detection** — It's the most mature area with many open-source tools available.
2. **Evaluate resource requirements first** — LLMs require significant GPU resources. Consider your infrastructure constraints.
3. **Consider hybrid approaches** — 53.9% of studies use hybrid methods for good reason—they combine multiple strengths.
4. **Invest in log standardization** — reduces the "instrumentation tax" and makes AI adoption easier.
---
## Looking Ahead
Several trends point toward the next generation of log analysis AI:
**LLM Integration** — Large language models show promise for semantic log understanding, but computational costs need optimization. Tools like demonstrate potential for one-shot root cause analysis.
**Hybrid Architectures** — Combining GNNs (for dependency modeling) with transformers (for sequence understanding) addresses both structural and semantic understanding needs.
**Enterprise-Realistic Benchmarks** — The community needs new datasets that capture production complexity—including noise, schema drift, and multi-tenant scenarios.
**Federated Learning** — Privacy-preserving techniques like could enable cross-organization learning without exposing sensitive log data.
---
## Methodology
Our systematic review followed PRISMA guidelines, searching across Scopus, IEEE Xplore, ACM Digital Library, and SpringerLink.
**. The full paper includes detailed methodology, complete technique taxonomies, and extended analysis of each primary study. The is available on Zenodo.
---
## Appendix: Complete Tool Reference
For practitioners looking for a comprehensive reference, here's the full catalog of tools and techniques identified in our review.
### Sequence-Based Tools (LSTM/RNN)
|
### Transformer and LLM-Based Tools
### Graph Neural Network Tools
|
### Active Learning and Human-in-the-Loop Tools
|
### Supporting Frameworks and Libraries
|
---
### Winning 1st Runner Up at IEEE CISOSE: Graph-Based LLM Prompting for Microservice API Testing
**URL:** https://arfanu.com/blog/graph-based-llm-microservice-testing
**Date:** 2025-07-23
**Author:** Arfan Uddin
**Description:** How I used Interprocedural Control Flow Graphs to make LLM-based API test generation scalable for microservices - 1st runner up IEEE CISOSE 2025 SRC.
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---
## Research Overview
---
## The Problem: Why Current LLM-Based Testing Struggles
Large Language Models have demonstrated impressive code understanding capabilities. Naturally, researchers have explored using them for automated test generation. The idea is simple: feed the LLM your source code, and let it generate comprehensive test cases.
But for microservices, this approach breaks down:
---
## The Solution: ICFG-Based Prompting
Instead of feeding entire source code to the LLM, this approach uses **Interprocedural Control Flow Graphs (ICFGs)** to extract precisely the code paths relevant to each API endpoint.
### What is an ICFG?
An ICFG represents program execution flow across function boundaries. Unlike a simple call graph that just shows which functions call which, an ICFG captures:
- **Control flow within functions** (branches, loops, conditions)
- **Data dependencies** between functions
- **Complete execution paths** from entry point to exit
For API testing, this means we can trace exactly what code gets executed for any given endpoint request—and nothing more.
---
## How It Works
---
## Future Directions
This work opens several research directions:
**Async Behavior Handling** — Current ICFG construction focuses on synchronous execution paths. Microservices heavily use async patterns (message queues, event-driven architectures) that require extended graph models.
**Cross-Service Integration Testing** — The current approach tests individual microservices. Extending ICFGs across service boundaries could enable integration test generation that covers end-to-end workflows.
**API Contract Enrichment** — Combining ICFG-derived paths with OpenAPI specifications could provide even richer context for test generation, including request/response schema validation.
---
## Award Recognition
I'm grateful to my advisor and the research group at the University of Arizona for their guidance and support throughout this project.
---
## Citation
If you find this work useful, please cite:
```bibtex
@inproceedings{uddin2025graph,
title={Graph-Based LLM Prompting for Scalable Microservice API Testing},
author={Uddin, Md Arfan},
booktitle={2025 IEEE International Conference on Service-Oriented System Engineering (SOSE)},
year={2025},
organization={IEEE},
doi={10.1109/SOSE67019.2025.00034}
}
```
---
## About This Research
This research was conducted at the **University of Arizona** as part of my graduate studies in software engineering. The work focuses on improving developer productivity through intelligent tooling that leverages modern AI capabilities.
The code is available on . Feel free to if you have questions or want to collaborate on related research.
---
### Hello, I'm Arfan 👋
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# 👋 Meet Arfan
Hey there! I'm **Md Arfan Uddin**, and I'm excited you've found your way here.
I'm currently a **Ph.D. student in Software Engineering** at the University of Arizona. My research lies at the intersection of **AI and microservices**, focusing on using **Large Language Models (LLMs)** to improve **Root Cause Analysis (RCA)** in distributed systems. Imagine systems that can automatically analyze logs and traces to tell you what went wrong and how to fix it—this is the future I'm building toward.
This isn't just theory for me. My work has been **published in the _Journal of Systems and Software_ and presented at IEEE conferences** (SOSE, CLOSER), and I was honored as **1st runner-up at the IEEE CISOSE Student Research Competition 2025**. If you're curious, I unpack several of these papers in plain language [elsewhere on this blog](/blog).
But my journey didn't start in research labs.
### From Code to Cloud to Research
With over **4 years of professional experience** as a full-stack and mobile developer, I've worked across multiple domains—from research platforms to real-time apps. I led engineering for **KMap**, a campus-wide research visualization tool, where migrating the platform to **Next.js drove a 6x jump in click-through rate**. Today I build full-stack systems for the **International Collegiate Programming Contest (ICPC)**, supporting **50,000+ participants worldwide**, and earlier I co-created **ScholarPaw**, a scholarship management platform. I've always been passionate about building systems that don't just work—but scale, evolve, and empower.
Along the way, I've adopted tools like **Next.js**, **Spring Boot**, **Kafka**, **Redis**, and **Supabase**, and embraced principles of DevSecOps, microservice architecture, and automation. These experiences have deeply shaped how I approach both system design and research.
### Building Connecto: From Frustration to Founding
In 2024, I founded [**Connecto**](https://getconnecto.app)—a real-time networking app built to solve a common but overlooked problem: people attending the same event often miss opportunities to meet others around them.
Connecto helps users **find and connect with people nearby at events, meetups, or even coffee shops**—all in real time. Whether you're looking to expand your network, meet fellow attendees, or simply find someone with shared interests, Connecto makes that happen effortlessly. It's a startup born out of personal frustration, refined by user feedback, and built for human connection in physical spaces.
The traction has been encouraging: Connecto was selected as the **official check-in and identity platform for Hack Arizona 2026**, was used at **IEEE CISOSE 2025**, and earned **Startup Wildcats milestone funding**.
### BDStudents: Connecting Bangladeshi Students Worldwide
I'm also the **founder and president** of [**BDStudents**](https://bdstudents.org)—a global community platform on a mission to connect the **Bangladeshi student diaspora**. Studying far from home can be isolating, and finding your people on an unfamiliar campus is harder than it should be.
BDStudents bridges that gap. It has already reached **106 institutions across 71 cities and 17 countries**, giving students a place to **find their campus chapter, discover student clubs, share through a social feed, and learn from one another**. Under the hood, it's a multi-tenant platform with role-based access for university chapters and clubs, and a two-layer organization model—ambassadors, club leaders, and moderators—so the community can govern itself at scale. It grew out of my own experience as an international student, and a belief that no one should have to navigate that journey alone.
That same instinct shows up offline, too: I served as **interim president of the Bangladeshi Student Association at the University of Arizona**, where a small team helped rebuild a demoralized 150-member chapter and grow election participation from a single nomination to 39.
### Why This Blog Exists
This blog is a journal of my journey—part research log, part builder's notebook, and part personal reflection. You'll find posts on:
- 🚀 AI and LLM applications in software engineering
- 🧠 Research insights on debugging microservices
- 🛠 Building scalable systems and side projects
- 💼 Lessons from transitioning into tech entrepreneurship
- 🧑🏫 Life as a PhD + MBA student juggling research, coding, and startups
### Let's Connect
If you're a fellow researcher, engineer, builder, or just someone curious—I'd love to connect. You can find me on [LinkedIn](https://www.linkedin.com/in/rfn/), check out my [GitHub](https://github.com/arfan-rfn), explore [Connecto](https://getconnecto.app), or join the community at [BDStudents](https://bdstudents.org).
Thanks for stopping by. Here's to learning, building, and making meaningful things together. 💫
---
### From Pitch to Funding: How Connecto Won $500 at Startup Night
**URL:** https://arfanu.com/blog/startup-wildcats-connecto-funding
**Date:** 2025-04-15
**Author:** Arfan
**Description:** The story of pitching Connecto at Startup Wildcats' Startup Night and winning $500 in milestone funding—from nervous preparation to the moment of validation.
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. I stood near the stage, rehearsing my pitch one last time in my head, palms slightly sweaty, heart beating just a bit faster than usual.
This was the moment I'd been working toward.
**, part of , provided exactly the environment I needed. Through their program, I connected with mentors who challenged my assumptions, received feedback that sharpened my pitch, and joined a community of student founders who understood the hustle.
The milestone funding program was particularly motivating. It wasn't just about the money—$500 is meaningful, but what it represents matters more. Milestone funding is validation. It says: *we see what you're building, and we believe it's worth supporting.*
In the weeks leading up to Startup Night, I practiced my pitch obsessively. In front of mirrors, to friends who would give honest feedback, during late nights when I should have been sleeping. I focused on three things:
1. **The problem**—make it relatable and real
2. **The solution**—keep it clear and compelling
3. **The traction**—show that this isn't just an idea, it's actually being built
## The Pitch Night Experience
April 15, 2025. I arrived early, checking in at the venue and greeting familiar faces from the Startup Wildcats community. The energy was electric—a mix of nervous excitement and genuine support. We were all there cheering each other on, even as we competed for the same funding.
When my name was called, I walked to the stage, took the microphone, and looked out at the audience. Time seemed to slow down for just a moment. Then I began.
I talked about the problem—how we've all felt lost at networking events, how much potential goes unrealized when people don't connect. I demonstrated Connecto's GPS-based check-in, showed how attendees can discover who's nearby, explained the multiple profile modes that let users control exactly how they present themselves.
I emphasized privacy—no background tracking, user controls their visibility, professional networking that respects boundaries.
And then I shared the traction: Connecto was already live. Not just a prototype, but a real app being used at **IEEE CISOSE 2025**.
The audience leaned in. I could feel the energy shifting from polite attention to genuine interest.
## The Moment of Truth
After all the pitches were delivered, the judges deliberated. The wait felt eternal.
Then the announcements began. Names were called—Natalie R., Cole Withers, Manglam Srivastav, Becca Sanders—all incredible student founders with amazing projects. Over **$5,000** was being distributed across five of us.
And then: "**Connecto—Arfan Uddin.**"
I walked up to receive the check, feeling a wave of validation wash over me. This wasn't just $500. This was a signal that what I was building mattered to people beyond just me.
**.
## For Other Student Founders
If you're a student with an idea—whether it's a fully functioning app or just a spark of an insight—I can't recommend **** enough. The mentorship, the community, the opportunity to pitch and receive funding—it all adds up to something genuinely valuable.
You don't need to have everything figured out. You just need to start.
**Thank you** to Startup Wildcats, Tech Launch Arizona, and everyone who believed in Connecto. This milestone is just the beginning.
---
### HackAZ 2025: Reigniting a Legacy
**URL:** https://arfanu.com/blog/hackaz-2025
**Date:** 2025-03-22
**Author:** Arfan
**Description:** My experience bringing back HackAZ, one of the Southwest's most iconic hackathons.
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** finally came back in 2025—and I feel incredibly grateful to have been part of it as an **Advisor** and **Organizer**.
My journey with HackAZ started back in **2017**, when I first joined as a **volunteer**, helping with logistics and supporting participants throughout the event. That experience introduced me to the incredible community of builders, learners, and leaders that HackAZ brings together. In **2018**, I stepped up as a **core organizer**, where I focused on sponsorship outreach and helped bring in the funding that made the event possible. I learned how much effort and collaboration goes into running a large-scale student hackathon—and how rewarding it can be.
Then in **2019**, I had the opportunity to **lead two technical workshops**: one on Android app development with Java, and another introducing students to Dart and Flutter. Teaching those sessions was one of the most fulfilling parts of my HackAZ journey—watching students dive in, ask questions, and walk away with something they built themselves. Those moments deepened my connection to the event and reaffirmed my passion for helping others grow through technology.
So when the pandemic brought HackAZ to a halt, it felt deeply personal. I had seen firsthand what HackAZ meant to so many students—including myself—and it was hard to watch it go silent for so long.
But this year, **WE MADE IT HAPPEN**.
HackAZ 2025 took place from March 22 to March 24, a full weekend of non-stop hacking, learning, and connection. With a passionate and dedicated team, we brought HackAZ back to the University of Arizona—and the energy was unreal. We had **nearly 250 participants**, and seeing the event space buzzing with creativity and collaboration again was honestly emotional. Students stayed up through the night, pushing boundaries, turning wild ideas into real prototypes, and supporting each other like they'd known one another for years.
As an advisor, I got to work alongside some of the most hardworking and talented student organizers I've met. I was there to guide, mentor, and cheer them on—but truthfully, I learned a lot from them too. Their determination to revive this tradition and make it inclusive, impactful, and fun reminded me of why events like these matter so much.
### Looking Ahead
HackAZ 2025 wasn't just a restart. It was a **statement**. That even after setbacks and uncertainty, our community is still here—still building, still dreaming, still showing up.
To everyone who participated, organized, mentored, or sponsored—**thank you**. And a **special thanks to Kian**, whose leadership and initiative made this revival possible. Your vision brought us all together.
HackAZ is back, and I can't wait to see where we take it from here.
---
### Understanding Microservice Dependencies: A Complete Taxonomy from 2,659 Sources
**URL:** https://arfanu.com/blog/microservice-dependencies-taxonomy
**Date:** 2025-01-21
**Author:** Arfan Uddin
**Description:** A comprehensive taxonomy of microservice dependencies synthesized from academic and industry sources, covering 28 dependency types across 6 categories and their impact on system quality.
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info: **—synthesized **2,659 sources** (1,733 academic papers + 926 grey literature articles) to create the first comprehensive taxonomy of microservice dependencies. We identified **28 distinct dependency types** across **6 categories**, plus **11 symptom patterns** that indicate underlying dependency issues.
---
## The Big Picture
Before diving into details, here's how the 90 synthesized studies map across dependency types, tools, and quality impacts:
---
## Understanding Each Dependency Category
### D1. Data Dependency
Data dependencies arise when microservices share or interact with common data entities.
**Data Schema Dependency** occurs when multiple services interact with overlapping database schemas or data transfer objects (DTOs). Changes to schema design can cascade across all dependent services.
**Parametric Dependency** describes how input parameters influence service behavior and inter-service communication. Configuration changes can silently break downstream services.
### D2. Control Dependency
Control dependencies govern how services interact and coordinate their execution.
### D3. Resource Dependency
Resource dependencies (also called Technology Dependencies) occur when services share infrastructure or code resources.
- **Source Code Dependency**: Shared libraries between services
- **Database Dependency**: Multiple services accessing the same database instance
- **Environment Dependency**: Configuration settings, credentials, environment variables
- **Cluster Dependency**: Services relying on specific containerized environments (Docker, Kubernetes pods)
- **Network Dependency**: Reliance on network protocols (HTTP, gRPC, message queues)
### D4. Semantic Dependency
Semantic dependencies emerge from functional similarities between services. Services performing similar tasks may need coordinated updates to maintain consistency across the system.
### D5. Quality Attributes Dependency
These dependencies relate to non-functional requirements:
- **Security**: Authentication, authorization, encryption dependencies
- **SLA Requirements**: Performance, availability, and reliability constraints
### D6. Requirement Dependency
Functional dependencies based on business process flows, often mapped using Domain-Driven Design (DDD) principles.
---
## Dependency Symptoms (S1-S4)
Beyond the primary categories, we identified **symptom patterns**—manifestations of underlying dependencies that indicate potential issues.
---
## Tools for Industry Practitioners
Our review identified **20+ tools** for dependency detection and management. Here are the most useful ones organized by use case:
### Dependency Analysis & Visualization