Tootfinder

Automated Code Review Using Large Language Models at Ericsson: An Experience Report
Shweta Ramesh, Joy Bose, Hamender Singh, A K Raghavan, Sujoy Roychowdhury, Giriprasad Sridhara, Nishrith Saini, Ricardo Britto
https://arxiv.org/abs/2507.19115

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…

Fine-Tuning Multilingual Language Models for Code Review: An Empirical Study on Industrial C# Projects
Igli Begolli, Meltem Aksoy, Daniel Neider
https://arxiv.org/abs/2507.19271

Fine-Tuning Multilingual Language Models for Code Review: An Empirical Study on Industrial C# Projects
Code review is essential for maintaining software quality but often time-consuming and cognitively demanding, especially in industrial environments. Recent advancements in language models (LMs) have opened new avenues for automating core review tasks. This study presents the empirical evaluation of monolingual fine-tuning on the performance of open-source LMs across three key automated code review tasks: Code Change Quality Estimation, Review Comment Generation, and Code Refinement. We fine-tun…

Automating Thematic Review of Prevention of Future Deaths Reports: Replicating the ONS Child Suicide Study using Large Language Models
Sam Osian, Arpan Dutta, Sahil Bhandari, Iain E. Buchan, Dan W. Joyce
https://arxiv.org/abs/2507.20786

Automating Thematic Review of Prevention of Future Deaths Reports: Replicating the ONS Child Suicide Study using Large Language Models
Prevention of Future Deaths (PFD) reports, issued by coroners in England and Wales, flag systemic hazards that may lead to further loss of life. Analysis of these reports has previously been constrained by the manual effort required to identify and code relevant cases. In 2025, the Office for National Statistics (ONS) published a national thematic review of child-suicide PFD reports ($\leq$ 18 years), identifying 37 cases from January 2015 to November 2023 - a process based entirely on manual c…

Beim #TagDerDigitalenFreiheit vom @… in #Tübingen am 26./27.07. gibts von mir einen Vortrag, wie man alles Mögliche mit :git:

Automated Code Review Using Large Language Models with Symbolic Reasoning
Busra Icoz, Goksel Biricik
https://arxiv.org/abs/2507.18476 https://arxiv.org/pdf…

Automated Code Review Using Large Language Models with Symbolic Reasoning
Code review is one of the key processes in the software development lifecycle and is essential to maintain code quality. However, manual code review is subjective and time consuming. Given its rule-based nature, code review is well suited for automation. In recent years, significant efforts have been made to automate this process with the help of artificial intelligence. Recent developments in Large Language Models (LLMs) have also emerged as a promising tool in this area, but these models ofte…

@… @… SmartBear researched this in depth in 2015 and demonstrated, with data, what you’re saying.

ByteDance's publishing imprint 8th Note Press began informing writers and agents in late May that it was closing and returning publication rights to authors (Alexandra Alter/New York Times)
https://www.

Buzzy Publisher Started by TikTok’s Owner Abruptly Shuts Down
8th Note Press informed writers and agents that it is abruptly shutting down and returning publication rights to authors.

Watching the frustratingly fruitless fights over the USEFULNESS of LLM-based coding helpers, I've come down to 3 points that explain why ppl seem to live in different realities:
Most programmers:
1) Write inconsequential remixes of trivial code that has been written many times before.
2) Lack the taste for good design & suck at code review in general (yours truly included).
3) Lack the judgement to differentiate between 1) & FOSS repos of nontrivial code, …

LLM-Driven Collaborative Model for Untangling Commits via Explicit and Implicit Dependency Reasoning
Bo Hou, Xin Tan, Kai Zheng, Fang Liu, Yinghao Zhu, Li Zhang
https://arxiv.org/abs/2507.16395

LLM-Driven Collaborative Model for Untangling Commits via Explicit and Implicit Dependency Reasoning
Atomic commits, each of which addresses a single development concern, are a best practice in software development. However, developers frequently produce tangled commits that mix unrelated changes due to practical constraints or unclear boundaries, negatively impacting code review and maintenance. Although prior commit untangling approaches: rule-based, feature-based, or graph-based, have made progress, they often rely on shallow signals and fail to distinguish between explicit dependencies (e.…

AI-Assisted Fixes to Code Review Comments at Scale
Chandra Maddila, Negar Ghorbani, James Saindon, Parth Thakkar, Vijayaraghavan Murali, Rui Abreu, Jingyue Shen, Brian Zhou, Nachiappan Nagappan, Peter C. Rigby
https://arxiv.org/abs/2507.13499

AI-Assisted Fixes to Code Review Comments at Scale
Aim. There are 10s of thousands of code review comments each week at Meta. We developed Metamate for Code Review (MetaMateCR) that provides AI-assisted fixes for reviewer comments in production at scale. Method. We developed an internal benchmark of 64k data points to fine-tune Llama models. Once our models achieve reasonable offline results, we roll them into production. To ensure that our AI-assisted fixes do not negatively impact the time it takes to do code reviews…

Tried GitHub Copilot code review, and it doesn’t understand the concept of future-dating blog posts. Worse, it suggests a random-ass date. This stuff sucks.

Code-Switching in End-to-End Automatic Speech Recognition: A Systematic Literature Review
Maha Tufail Agro, Atharva Kulkarni, Karima Kadaoui, Zeerak Talat, Hanan Aldarmaki
https://arxiv.org/abs/2507.07741

Code-Switching in End-to-End Automatic Speech Recognition: A Systematic Literature Review
Motivated by a growing research interest into automatic speech recognition (ASR), and the growing body of work for languages in which code-switching (CS) often occurs, we present a systematic literature review of code-switching in end-to-end ASR models. We collect and manually annotate papers published in peer reviewed venues. We document the languages considered, datasets, metrics, model choices, and performance, and present a discussion of challenges in end-to-end ASR for code-switching. Our …

"The waste of having processes that create defects and then use inspection to catch them is certainly something to avoid. A significant part of the effort in code reviews should be geared toward capturing learning that can be applied to current processes to improve them so fewer bugs are created in the future.
..."

"The process of coding with an “agentic” LLM appears to be the process of carefully distilling all the worst parts of code review, and removing and discarding all of its benefits."
Very insightful post on #GenAI by Glyph
(Original title: I Think I’m Done Thinking About genAI For Now)

Socio-Technical Smell Dynamics in Code Samples: A Multivocal Review on Emergence, Evolution, and Co-Occurrence
Arthur Bueno, Bruno Cafeo, Maria Cagnin, Awdren Font\~ao
https://arxiv.org/abs/2507.13481

Socio-Technical Smell Dynamics in Code Samples: A Multivocal Review on Emergence, Evolution, and Co-Occurrence
Code samples play a pivotal role in open-source ecosystems (OSSECO), serving as lightweight artifacts that support knowledge transfer, onboarding, and framework adoption. Despite their instructional relevance, these samples are often governed informally, with minimal review and unclear ownership, which increases their exposure to socio-technical degradation. In this context, the co-occurrence and longitudinal interplay of code smells (e.g., large classes, poor modularity) and community smells (…

"the new review of the earlier assessment does not dispute the conclusion that Russia favored the election of Donald J. Trump." #GiftLink
C.I.A. Says Its Leaders Rushed Report on Russia Interference in 2016 Vote - The New York Times
https://www.nytimes.com/2025/07/02/us/politics/russia-trump-2016-election.html?unlocked_article_code=1.Tk8.1oh0.obRdrVnArZxp&smid=url-share

Looks like github copilot PR review now supports all the languages in public preview. This will be useful for me as I commit #fsharp code a lot. In fact I had a PR today that it reviewed, found a few decent suggestions actually.

A Review of Cloud Computing in Seismology
Yiyu Ni, Marine A. Denolle, Jannes Munchmeyer, Yinzhi Wang, Kuan-Fu Feng, Carlos Garcia Jurado Suarez, Amanda M. Thomas, Chad Trabant, Alex Hamilton, David Mencin
https://arxiv.org/abs/2506.11307

A Review of Cloud Computing in Seismology
Seismology has entered the petabyte era, driven by decades of continuous recordings of broadband networks, the increase in nodal seismic experiments, and the recent emergence of Distributed Acoustic Sensing (DAS). This review explains how commercial clouds - AWS, Google Cloud, and Azure - by providing object storage, elastic compute, and managed databases, enable researchers to "bring the code to the data," thereby overcoming traditional HPC solutions' bandwidth and capacity limitations. After …

Today I deployed an #AI backed code review system as part of the @… #monorepo PR review checks.
Is it going to replace coders? No. Is it throwing up amazing ins…

Retrieval-Augmented Code Review Comment Generation
Hyunsun Hong, Jongmoon Baik
https://arxiv.org/abs/2506.11591 https://arxiv.org/pdf…

Retrieval-Augmented Code Review Comment Generation
Automated code review comment generation (RCG) aims to assist developers by automatically producing natural language feedback for code changes. Existing approaches are primarily either generation-based, using pretrained language models, or information retrieval-based (IR), reusing comments from similar past examples. While generation-based methods leverage code-specific pretraining on large code-natural language corpora to learn semantic relationships between code and natural language, they oft…

Leveraging Reward Models for Guiding Code Review Comment Generation
Oussama Ben Sghaier, Rosalia Tufano, Gabriele Bavota, Houari Sahraoui
https://arxiv.org/abs/2506.04464

Leveraging Reward Models for Guiding Code Review Comment Generation
Code review is a crucial component of modern software development, involving the evaluation of code quality, providing feedback on potential issues, and refining the code to address identified problems. Despite these benefits, code review can be rather time consuming, and influenced by subjectivity and human factors. For these reasons, techniques to (partially) automate the code review process have been proposed in the literature. Among those, the ones exploiting deep learning (DL) are able to …

On The Impact of Merge Request Deviations on Code Review Practices
Samah Kansab, Francis Bordeleau, Ali Tizghadam
https://arxiv.org/abs/2506.08860 https://…

On The Impact of Merge Request Deviations on Code Review Practices
Code review is a key practice in software engineering, ensuring quality and collaboration. However, industrial Merge Request (MR) workflows often deviate from standardized review processes, with many MRs serving non-review purposes (e.g., drafts, rebases, or dependency updates). We term these cases deviations and hypothesize that ignoring them biases analytics and undermines ML models for review analysis. We identify seven deviation categories, occurring in 37.02% of MRs, and propose a few-sh…

Teaching Programming in the Age of Generative AI: Insights from Literature, Pedagogical Proposals, and Student Perspectives
Clemente Rubio-Manzano, Jazna Meza, Rodolfo Fernandez-Santibanez, Christian Vidal-Castro
https://arxiv.org/abs/2507.00108

Teaching Programming in the Age of Generative AI: Insights from Literature, Pedagogical Proposals, and Student Perspectives
Computer programming is undergoing a true transformation driven by powerful new tools for automatic source code generation based on large language models. This transformation is also manifesting in introductory programming courses at universities around the world, generating an in-depth debate about how programming content should be taught, learned, and assessed in the context of generative artificial intelligence. This article aims, on the one hand, to review the most relevant studies on thi…

Not One to Rule Them All: Mining Meaningful Code Review Orders From GitHub
Abir Bouraffa, Carolin Brandt, Andy Zaidmann, Walid Maalej
https://arxiv.org/abs/2506.10654

Not One to Rule Them All: Mining Meaningful Code Review Orders From GitHub
Developers use tools such as GitHub pull requests to review code, discuss proposed changes, and request modifications. While changed files are commonly presented in alphabetical order, this does not necessarily coincide with the reviewer's preferred navigation sequence. This study investigates the different navigation orders developers follow while commenting on changes submitted in pull requests. We mined code review comments from 23,241 pull requests in 100 popular Java and Python repositorie…

LLMs in Coding and their Impact on the Commercial Software Engineering Landscape
Vladislav Belozerov, Peter J Barclay, Askhan Sami
https://arxiv.org/abs/2506.16653

LLMs in Coding and their Impact on the Commercial Software Engineering Landscape
Large-language-model coding tools are now mainstream in software engineering. But as these same tools move human effort up the development stack, they present fresh dangers: 10% of real prompts leak private data, 42% of generated snippets hide security flaws, and the models can even ``agree'' with wrong ideas, a trait called sycophancy. We argue that firms must tag and review every AI-generated line of code, keep prompts and outputs inside private or on-premises deployments, obey emerging safet…

This https://arxiv.org/abs/2503.16167 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models
State-of-the-art large language models (LLMs) have demonstrated impressive code generation capabilities but struggle with real-world software engineering tasks, such as revising source code to address code reviews, hindering their practical use. Code review comments are often implicit, ambiguous, and colloquial, requiring models to grasp both code and human intent. This challenge calls for evaluating large language models' ability to bridge both technical and conversational contexts. While exis…

CRScore : Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review
Manav Nitin Kapadnis, Atharva Naik, Carolyn Rose
https://arxiv.org/abs/2506.00296

CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review
Reinforcement learning (RL) to improve code review comment generation requires handling unstructured outputs, making reinforcement learning (RL) feedback challenging. The two main RL approaches, namely RL with Verifiable Feedback (RLVR) and RL with AI Feedback (RLAIF), offer trade-offs: RLVR provides reliable feedback for structured tasks like code generation, while RLAIF works for unstructured outputs but is subjective. We bridge this gap with CRScore++, an RL framework that leverages both LLM…

AI-Based Software Vulnerability Detection: A Systematic Literature Review
Samiha Shimmi, Hamed Okhravi, Mona Rahimi
https://arxiv.org/abs/2506.10280 https:…

AI-Based Software Vulnerability Detection: A Systematic Literature Review
Software vulnerabilities in source code pose serious cybersecurity risks, prompting a shift from traditional detection methods (e.g., static analysis, rule-based matching) to AI-driven approaches. This study presents a systematic review of software vulnerability detection (SVD) research from 2018 to 2023, offering a comprehensive taxonomy of techniques, feature representations, and embedding methods. Our analysis reveals that 91% of studies use AI-based methods, with graph-based models being th…

This https://arxiv.org/abs/2401.16310 has been replaced.
link: https://scholar.google.com/scholar?q=a

An Insight into Security Code Review with LLMs: Capabilities, Obstacles, and Influential Factors
Security code review is a time-consuming and labor-intensive process typically requiring integration with automated security defect detection tools. However, existing security analysis tools struggle with poor generalization, high false positive rates, and coarse detection granularity. Large Language Models (LLMs) have been considered promising candidates for addressing those challenges. In this study, we conducted an empirical study to explore the potential of LLMs in detecting security defect…

This https://arxiv.org/abs/2411.03079 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Utilizing Precise and Complete Code Context to Guide LLM in Automatic False Positive Mitigation
Static Application Security Testing (SAST) tools are critical to software quality, identifying potential code issues early in development. However, they often produce false positive warnings that require manual review, slowing down development. Thus, automating false positive mitigation (FPM) is essential. The advent of Large Language Models (LLMs), with their strong abilities in natural language and code understanding, offers promising avenues for FPM. Yet current LLM-based FPM method faces tw…