4 citations · 8 across the 9 of their papers we have counts for
9 papers · 1 filter
Using Agentic AI for contextualized and multifaceted code review at Ericsson
Muhammad Laiq, Ricardo Britto, Muhammad Usman +2
Context: Conducting effective code reviews is increasingly challenging due to the growing complexity of software systems and the accelerated code generation by AI coding agents. LL…
Towards Assurance Closure in AI-Native Large-Scale Agile Software Development
Ricardo Britto
The AI-Native Manifesto envisions large-scale agile software development in which humans increasingly govern intent, risk, and exceptions while agents execute more of the engineeri…
Automatic Identification of Machine Learning-Specific Code Smells
Peter Hamfelt, Ricardo Britto, Lincoln Rocha +1
Machine learning (ML) has rapidly grown in popularity, becoming vital to many industries. Currently, the research on code smells in ML applications lacks tools and studies that add…
Quo Vadis, Code Review? Exploring the Future of Code Review
Michael Dorner, Andreas Bauer, Darja Šmite +6
Context: Code review has long been a core practice in collaborative software engineering. As automation becomes increasingly embedded in development workflows, the role and functio…
Automated Code Review Using Large Language Models at Ericsson: An Experience Report
Shweta Ramesh, Joy Bose, Hamender Singh +5
Code review is one of the primary means of assuring the quality of released software along with testing and static analysis. However, code review requires experienced developers wh…
Static Program Analysis Guided LLM Based Unit Test Generation
Sujoy Roychowdhury, Giriprasad Sridhara, A K Raghavan +5
We describe a novel approach to automating unit test generation for Java methods using large language models (LLMs). Existing LLM-based approaches rely on sample usage(s) of the me…