6 citations · 6 across the 9 of their papers we have counts for
25 papers · 1 filter
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…
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…
Identifying and Prioritizing Generative AI Use Cases in an Organization: An Industrial Case Study
Malik Abdul Sami, Zeeshan Rasheed, Meri Olenius +4
Organisations are examining how generative AI can support their operational work and decision-making processes. This study investigates how employees in a energy company understand…
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…