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

• URL: http://arxiv.org/abs/2507.19115v2
• Date: Thu, 31 Jul 2025 14:34:00 GMT
• Title: Automated Code Review Using Large Language Models at Ericsson: An Experience Report
• Authors: Shweta Ramesh, Joy Bose, Hamender Singh, A K Raghavan, Sujoy Roychowdhury, Giriprasad Sridhara, Nishrith Saini, Ricardo Britto,
• Abstract summary: We describe our experience in using Large Language Models towards automating the code review process in Ericsson.<n>We then describe our preliminary experiments with experienced developers in evaluating our code review tool and the encouraging results.
• Score: 3.82053496282075
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• MERA Code: A Unified Framework for Evaluating Code Generation Across Tasks [56.34018316319873]
  We propose MERA Code, a benchmark for evaluating code for the latest code generation LLMs in Russian.<n>This benchmark includes 11 evaluation tasks that span 8 programming languages.<n>We evaluate open LLMs and frontier API models, analyzing their limitations in terms of practical coding tasks in non-English languages.
  arXiv  Detail & Related papers  (2025-07-16T14:31:33Z)
• Deep Learning-based Code Reviews: A Paradigm Shift or a Double-Edged Sword? [14.970843824847956]
  We run a controlled experiment with 29 experts who reviewed different programs with/without the support of an automatically generated code review.<n>We show that reviewers consider valid most of the issues automatically identified by the LLM and that the availability of an automated review as a starting point strongly influences their behavior.<n>The reviewers who started from an automated review identified a higher number of low-severity issues while, however, not identifying more high-severity issues as compared to a completely manual process.
  arXiv  Detail & Related papers  (2024-11-18T09:24:01Z)
• Understanding Code Understandability Improvements in Code Reviews [79.16476505761582]
  We analyzed 2,401 code review comments from Java open-source projects on GitHub. 83.9% of suggestions for improvement were accepted and integrated, with fewer than 1% later reverted.
  arXiv  Detail & Related papers  (2024-10-29T12:21:23Z)
• Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion? [60.84912551069379]
  We present the Code-Development Benchmark (Codev-Bench), a fine-grained, real-world, repository-level, and developer-centric evaluation framework. Codev-Agent is an agent-based system that automates repository crawling, constructs execution environments, extracts dynamic calling chains from existing unit tests, and generates new test samples to avoid data leakage.
  arXiv  Detail & Related papers  (2024-10-02T09:11:10Z)
• Predicting Expert Evaluations in Software Code Reviews [8.012861163935904]
  This paper presents an algorithmic model that automates aspects of code review typically avoided due to their complexity or subjectivity. Instead of replacing manual reviews, our model adds insights that help reviewers focus on more impactful tasks.
  arXiv  Detail & Related papers  (2024-09-23T16:01:52Z)
• Leveraging Reviewer Experience in Code Review Comment Generation [11.224317228559038]
  We train deep learning models to imitate human reviewers in providing natural language code reviews. The quality of the model generated reviews remain sub-optimal due to the quality of the open-source code review data used in model training. We propose a suite of experience-aware training methods that utilise the reviewers' past authoring and reviewing experiences as signals for review quality.
  arXiv  Detail & Related papers  (2024-09-17T07:52:50Z)
• Improving Automated Code Reviews: Learning from Experience [12.573740138977065]
  This study investigates whether higher-quality reviews can be generated from automated code review models. We find that experience-aware oversampling can increase the correctness, level of information, and meaningfulness of reviews.
  arXiv  Detail & Related papers  (2024-02-06T07:48:22Z)
• A Static Evaluation of Code Completion by Large Language Models [65.18008807383816]
  Execution-based benchmarks have been proposed to evaluate functional correctness of model-generated code on simple programming problems. static analysis tools such as linters, which can detect errors without running the program, haven't been well explored for evaluating code generation models. We propose a static evaluation framework to quantify static errors in Python code completions, by leveraging Abstract Syntax Trees.
  arXiv  Detail & Related papers  (2023-06-05T19:23:34Z)
• CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing [139.77117915309023]
  CRITIC allows large language models to validate and amend their own outputs in a manner similar to human interaction with tools. Comprehensive evaluations involving free-form question answering, mathematical program synthesis, and toxicity reduction demonstrate that CRITIC consistently enhances the performance of LLMs.
  arXiv  Detail & Related papers  (2023-05-19T15:19:44Z)
• CodeReviewer: Pre-Training for Automating Code Review Activities [36.40557768557425]
  This research focuses on utilizing pre-training techniques for the tasks in the code review scenario. We collect a large-scale dataset of real world code changes and code reviews from open-source projects in nine of the most popular programming languages. To better understand code diffs and reviews, we propose CodeReviewer, a pre-trained model that utilizes four pre-training tasks tailored specifically for the code review senario.
  arXiv  Detail & Related papers  (2022-03-17T05:40:13Z)
• Measuring Coding Challenge Competence With APPS [54.22600767666257]
  We introduce APPS, a benchmark for code generation. Our benchmark includes 10,000 problems, which range from having simple one-line solutions to being substantial algorithmic challenges. Recent models such as GPT-Neo can pass approximately 15% of the test cases of introductory problems.
  arXiv  Detail & Related papers  (2021-05-20T17:58:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.