arXiv subjects

Nishrith Saini

Publications and source records attributed to Nishrith Saini.

5 recordsLinked to original sources

arXiv2026

Using Agentic AI for contextualized and multifaceted code review at Ericsson

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. LLM-based approaches for code reviews have shown promising results in identifying defects and improving code quality. However, existing approaches rarely consider project-specific contextualized knowledge, and few have been evaluated in industrial settings. Objective: In this study, we propose a multi-agent-based solution that provides multifaceted assessments of code changes. Method: Following the Design Science Research Process, we developed and evaluated our solution in an industrial setting. Our solution combines specialized agent skills with context-specific knowledge to identify antipatterns in code changes across four dimensions: readability, maintainability, reliability, and performance. Using our solution, we generated reviews for several code commits and identified more than 200 issues. These issues were then manually validated by the developers of the case company for their correctness and importance. Results: The evaluation results show that our solution achieves 96% accuracy in correctly identifying issues in the investigated code commits. Furthermore, around 69% of the correctly identified issues were rated as important, with approximately 33% rated as severe issues that must be fixed and 36% as important issues that should be fixed. Qualitative feedback from developers corroborates these findings and highlights the usefulness of the generated reviews. Conclusion: Our findings provide empirical evidence from an industrial evaluation that combining specialized agent skills with context-specific knowledge yields accurate, practically useful code reviews.

cs.SE↗

arXiv2026

The AI-Native Large-Scale Agile Software Development Manifesto

Despite the widespread adoption of agile methods, achieving true agility at scale remains elusive. Large-scale agile frameworks remain largely human-centric and manual, relying on coordination meetings, artifact synchronization, and role-based handoffs that inhibit real-time adaptation. Meanwhile, rapid advances in AI, particularly large language models, have begun transforming software engineering, yet their potential for organizational-level agility remains underexplored. We present the AI-Native Large-Scale Agile Software Development Manifesto: a set of values and principles that redefine how large-scale software development is organized when AI becomes a first-class participant rather than a peripheral tool. The manifesto is grounded in six principles, parallel processes, intent-driven teams, living knowledge, verification-first assurance, orchestrated agent workforces, and reusable blueprints, that together shift development from a meeting-driven, document-heavy, sequential process to an intelligent, adaptive, continuously learning system.

cs.SE↗

arXiv2026

Validating Threat Modeling Results with the Help of Vulnerable Test Applications

Validating threat modeling results remains difficult because completeness is hard to judge without an external oracle. Existing studies often rely on expert-produced reference models and other human baselines, but these can contain omissions or disagreements. This paper evaluates a complementary, vulnerability-grounded validation approach. We apply threat modeling to intentionally vulnerable applications with a known vulnerability set to measure the number of related vulnerabilities that can be discovered. We compare ThreMoLIA, an LLM-assisted threat modeling solution developed by our team, with the Microsoft Threat Modeling Tool (MTMT) across two vulnerable applications: AzureGoat and the Vulnerable Bank Application (VulnBank). The inputs to both tools are limited to architecture, data flow diagrams, and their descriptions. The results show that ThreMoLIA achieved higher vulnerability coverage on both systems. We show that vulnerable test applications provide a practical benchmark for assessing threat coverage and complement expert-based validation.

cs.CR↗

arXiv2025

Automated Code Review Using Large Language Models at Ericsson: An Experience Report

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 who may not always have the time to perform an in-depth review of code. Thus, automating code review can help alleviate the cognitive burden on experienced software developers allowing them to focus on their primary activities of writing code to add new features and fix bugs. In this paper, we describe our experience in using Large Language Models towards automating the code review process in Ericsson. We describe the development of a lightweight tool using LLMs and static program analysis. We then describe our preliminary experiments with experienced developers in evaluating our code review tool and the encouraging results.

cs.SE↗

arXiv2021

Using Machine Intelligence to Prioritise Code Review Requests

Modern Code Review (MCR) is the process of reviewing new code changes that need to be merged with an existing codebase. As a developer, one may receive many code review requests every day, i.e., the review requests need to be prioritised. Manually prioritising review requests is a challenging and time-consuming process. To address the above problem, we conducted an industrial case study at Ericsson aiming at developing a tool called Pineapple, which uses a Bayesian Network to prioritise code review requests. To validate our approach/tool, we deployed it in a live software development project at Ericsson, wherein more than 150 developers develop a telecommunication product. We focused on evaluating the predictive performance, feasibility, and usefulness of our approach. The results indicate that Pineapple has competent predictive performance (RMSE = 0.21 and MAE = 0.15). Furthermore, around 82.6% of Pineapple's users believe the tool can support code review request prioritisation by providing reliable results, and around 56.5% of the users believe it helps reducing code review lead time. As future work, we plan to evaluate Pineapple's predictive performance, usefulness, and feasibility through a longitudinal investigation.

cs.SE↗