7 papers
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…
Quo Vadis, Code Review? Exploring the Future of Code Review
Michael Dorner, Andreas Bauer, Darja Å mite +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…
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…
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…