41 citations · 79 across the 29 of their papers we have counts for
33 papers
Where Did the Repair First Go Wrong? Localizing the Origins of Silent Failures in Agentic Vulnerability Repair
Wenji Bai, Muhammad Waseem, Zeeshan Rasheed +2
Localizing where an LLM-based agent first fails to uphold security during a repair can show which stage of its workflow needs an additional safeguard. This is difficult for silent…
A Closed-Loop Control Architecture for Reliable Constraint Satisfaction in LLM Text Generation
Quan Zhou, Shahbaz Siddeeq, Mika Saari +1
Software systems increasingly embed a large language model in features that must satisfy a numeric output constraint, that is, a requirement expressible as a number or an interval…
An Autonomy Aware Metamodel for Human AI Collaboration in Software Engineering
Md Mahade Hasan, Md Toufique Hasan, Zheying Zhang +5
Artificial Intelligence (AI) is shifting software engineering from tool-supported processes towards AI-first collaboration, where authority is dynamically distributed across human…
Checkpoints Are Not Enough: Trust Calibration in CoSLR, a Human-AI System for Systematic Literature Reviews
MD Aidul Islam, Malik Abdul Sami, Muhammad Waseem +4
Systematic Literature Reviews (SLRs) are essential for evidence-based research but remain time-consuming, requiring researchers to manage large volumes of publications across plann…
When Passing Tests Hides Vulnerabilities: An Empirical Study of Silent Failures in Agentic Systems
Wenji Bai, Muhammad Waseem, Zeeshan Rasheed +2
LLM-based agents for automated code repair have received significant attention in recent years from both research and software engineering practice perspectives. However, limited a…
REFINE: A Multi-Agent LLM Approach for Evidence-Guided Code Refactoring
Muhammad Waseem, Aakash Ahmad, Pekka Abrahamsson
Large Language Models (LLMs) offer new opportunities for automated code refactoring. However, generated changes must reduce targeted quality problems without introducing new issues…
Trustworthy RAG: An Evaluation Agent for Detecting Misinformation and Knowledge Poisoning in Generative AI Systems
Balkrishna Giri, Md Toufique Hasan, Jussi Rasku +2
Retrieval-Augmented Generation (RAG) grounds Large Language Model (LLM) outputs in external knowledge, but RAG systems usually trust whatever they retrieve, creating a Security-Rel…
CodeAssay: A Multi-Metric Benchmark with Audited Ground Truth for LLM Code Generation
Shahbaz Siddeeq, Muhammad Waseem, Umar Subhan Malhi +1
Large Language Models are increasingly evaluated for code generation using test-based benchmarks. The validity of such evaluations depends on the reliability of their references an…
Vibe Coding in Software Development: A Multivocal Literature Review
Shahbaz Siddeeq, Muhammad Waseem, Kai-Kristian Kemell +3
Vibe coding is a software development practice in which developers state intent in natural language and large language models generate code. It is often framed as one-shot promptin…
Zero-Shot Heart Rate Variability Forecasting from Consumer Wearables Using Time Series Foundation Models
Luukas Peräkylä, Fahad Sohrab, Ville Hautamäki +3
Short-term Heart Rate Variability (HRV) forecasting could provide clinicians with actionable lead time for detecting autonomic dysfunction and adverse cardiac events. Consumer wear…
Epic-Organized vs. Requirement-Aligned Gherkin: An Empirical Evaluation of LLM-Based Acceptance Criteria Generation
Shahbaz Siddeeq, Mateen Abbasi, Jussi Rasku +4
Automated authoring of Gherkin Behavior-Driven Development (BDD) acceptance criteria remains a manual bottleneck in requirements engineering. This study investigates whether epic-o…
Context Before Code: An Experience Report on Vibe Coding in Practice
Md Nasir Uddin Shuvo, Md Aidul Islam, Md Mahade Hasan +2
Code-generating tools are increasingly used in software development, yet experience reports on conversational "vibe coding" under production constraints remain limited. This paper…
Adopt-an-AI-Researcher: Bootstrapping Industry-Academia Collaboration to Identify and Prioritise Generative AI Use Cases
Malik Abdul Sami, Zeeshan Rasheed, Meri Olenius +4
Organisations want to use generative AI (GenAI), but many do not know where to begin. Researchers understand AI capabilities, while employees understand their organisation's proces…
Vibe Coding in Practice: Flow, Technical Debt, and Guidelines for Sustainable Use
Muhammad Waseem, Aakash Ahmad, Kai-Kristian Kemell +4
Vibe Coding (VC) is a form of software development assisted by generative AI, in which developers describe the intended functionality or logic via natural language prompts, and the…
From Specification to Service: Accelerating API-First Development Using Multi-Agent Systems
Saurabh Chauhan, Zeeshan Rasheed, Malik Abdul Sami +6
This paper presents a system that uses Large Language Models (LLMs)-based agents to automate the API-first development of RESTful microservices. This system helps to create an Open…
VAPU: System for Autonomous Legacy Code Modernization
Valtteri Ala-Salmi, Zeeshan Rasheed, Abdul Malik Sami +5
In this study, we present a solution for the modernization of legacy applications, an area of code generation where LLM-based multi-agent systems are proving essential for complex…
AI and Agile Software Development: From Frustration to Success -- XP2025 Workshop Summary
Tomas Herda, Victoria Pichler, Zheying Zhang +2
The full-day workshop on AI and Agile at XP 2025 convened a diverse group of researchers and industry practitioners to address the practical challenges and opportunities of integra…
Assessing Small Language Models for Code Generation: An Empirical Study with Benchmarks
Md Mahade Hasan, Muhammad Waseem, Kai-Kristian Kemell +3
The recent advancements of Small Language Models (SLMs) have opened new possibilities for efficient code generation. SLMs offer lightweight and cost-effective alternatives to Large…
LLM-based Multi-Agent System for Intelligent Refactoring of Haskell Code
Shahbaz Siddeeq, Muhammad Waseem, Zeeshan Rasheed +7
Refactoring is a constant activity in software development and maintenance. Scale and maintain software systems are based on code refactoring. However, this process is still labor…
Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation
Md Toufique Hasan, Muhammad Waseem, Kai-Kristian Kemell +3
Retrieval-Augmented Generation (RAG) systems are emerging as a key approach for grounding Large Language Models (LLMs) in external knowledge, addressing limitations in factual accu…
LLM-Generated Microservice Implementations from RESTful API Definitions
Saurabh Chauhan, Zeeshan Rasheed, Abdul Malik Sami +4
The growing need for scalable, maintainable, and fast-deploying systems has made microservice architecture widely popular in software development. This paper presents a system that…
Distributed Approach to Haskell Based Applications Refactoring with LLMs Based Multi-Agent Systems
Shahbaz Siddeeq, Zeeshan Rasheed, Malik Abdul Sami +6
We present a large language models (LLMs) based multi-agent system to automate the refactoring of Haskell codebases. The multi-agent system consists of specialized agents performin…
Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach
Thomas Debelle, Fahad Sohrab, Pekka Abrahamsson +1
Anomaly detection in smart power grids is a critical challenge due to the complexity, heterogeneity, and dynamic nature of sensor data streams. Existing one-class classification me…
Autonomous Legacy Web Application Upgrades Using a Multi-Agent System
Valtteri Ala-Salmi, Zeeshan Rasheed, Abdul Malik Sami +6
The use of Large Language Models (LLMs) for autonomous code generation is gaining attention in emerging technologies. As LLM capabilities expand, they offer new possibilities such…