4 citations · 8 across the 9 of their papers we have counts for
11 papers
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…
Validating Threat Modeling Results with the Help of Vulnerable Test Applications
Oleksandr Adamov, Davide Fucci, Felix Viktor Jedrzejewski +2
Validating threat modeling results remains difficult because completeness is hard to judge without an external oracle. Existing studies often rely on expert-produced reference mode…
Machine Learning-Based Detection of MCP Attacks
Tobias Mattsson, Samuel Nyberg, Anton Borg +1
The Model Context Protocol (MCP) is a new and emerging technology that extends the functionality of large language models, improving workflows but also exposing users to a new atta…
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…
Icing on the Cake: Automatic Code Summarization at Ericsson
Giriprasad Sridhara, Sujoy Roychowdhury, Sumit Soman +2
This paper presents our findings on the automatic summarization of Java methods within Ericsson, a global telecommunications company. We evaluate the performance of an approach cal…
Help Me to Understand this Commit! -- A Vision for Contextualized Code Reviews
Michael Unterkalmsteiner, Deepika Badampudi, Ricardo Britto +1
Background: Modern Code Review (MCR) is a key component for delivering high-quality software and sharing knowledge among developers. Effective reviews require an in-depth understan…
Telecom AI Native Systems in the Age of Generative AI -- An Engineering Perspective
Ricardo Britto, Timothy Murphy, Massimo Iovene +3
The rapid advancements in Artificial Intelligence (AI), particularly in generative AI and foundational models (FMs), have ushered in transformative changes across various industrie…