4 citations · 4 across the 9 of their papers we have counts for
11 papers
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 Evaluation of AI-Supported Evidence-Based Learning for Public Speaking Skill Development
Sashini Hettiarachchi, Shahbaz Siddeeq, Mika Saari +1
Public speaking is an essential skill in academic and professional contexts, but it often causes anxiety. Although several AI-based speech coaching tools exist, they typically prov…
From Prompting to Engineering: A Research Agenda for Prompt Engineering in Software Engineering
Vincenzo De Martino, Giovanna Broccia, Fabiano Pecorelli +6
Prompt engineering is increasingly used across Software Engineering (SE) activities, including requirements analysis, coding, testing, documentation, repository analysis, and plann…
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…
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…
TDD Governance for Multi-Agent Code Generation via Prompt Engineering
Tarlan Hasanli, Shahbaz Siddeeq, Bishwash Khanal +3
Large language models (LLMs) accelerate software development but often exhibit instability, non-determinism, and weak adherence to development discipline in unconstrained workflows…
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…
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…
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…
TimeLess: A Vision for the Next Generation of Software Development
Zeeshan Rasheed, Malik Abdul Sami, Jussi Rasku +16
Present-day software development faces three major challenges: complexity, time consumption, and high costs. Developing large software systems often requires battalions of teams an…