arXiv subjects

Carol Hanna

Publications and source records attributed to Carol Hanna.

8 recordsLinked to original sources

arXiv2026

Hot Fixing in the Wild

Despite the operational importance of hot fixes, large-scale evidence on how they reshape routine maintenance workflows, particularly in the era of autonomous coding agents, remains limited. We analyse hot fixes present in over 61,000 GitHub repositories from the Hao-Li/AIDev dataset and find consistent patterns of urgency: reduced collaboration (typically a single contributor), smaller and more targeted changes (median 2-3 commits and files, with <10 line modifications), limited review (often fewer than two reviewers), and substantially fewer test file modifications than regular bug fixes, consistent with their urgency-driven character. Leveraging the same urgency contexts, we examine differences between human- and AI-agent-authored hot fixes, revealing over 10 distinct repair behaviours, thus offering insights into future human-automation collaboration for hot fixing. Our study is the first to empirically analyse hot fix code changes at scale using a repository-level operationalisation of urgency. The comparison of human and agentbehaviours delineates their distinct characteristics, providing a foundation for understanding hot fixing in real-world practice

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arXiv2025

HotBugs.jar: A Benchmark of Hot Fixes for Time-Critical Bugs

Hot fixes are urgent, unplanned changes deployed to production systems to address time-critical issues. Despite their importance, no existing evaluation benchmark focuses specifically on hot fixes. We present HotBugs$.$jar, the first dataset dedicated to real-world hot fixes. From an initial mining of 10 active Apache projects totaling over 190K commits and 150K issue reports, we identified 746 software patches that met our hot-fix criteria. After manual evaluation, 679 were confirmed as genuine hot fixes, of which 110 are reproducible using a test suite. Building upon the Bugs$.$jar framework, HotBugs$.$jar integrates these 110 reproducible cases and makes available all 679 manually validated hot fixes, each enriched with comprehensive metadata to support future research. Each hot fix was systematically identified using Jira issue data, validated by independent reviewers, and packaged in a reproducible format with buggy and fixed versions, test suites, and metadata. HotBugs$.$jar has already been adopted as the official challenge dataset for the Search-Based Software Engineering (SBSE) Conference Challenge Track, demonstrating its immediate impact. This benchmark enables the study and evaluation of tools for rapid debugging, automated repair, and production-grade resilience in modern software systems to drive research in this essential area forward.

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arXiv2025

LLM-Guided Genetic Improvement: Envisioning Semantic Aware Automated Software Evolution

Genetic Improvement (GI) of software automatically creates alternative software versions that are improved according to certain properties of interests (e.g., running-time). Search-based GI excels at navigating large program spaces, but operates primarily at the syntactic level. In contrast, Large Language Models (LLMs) offer semantic-aware edits, yet lack goal-directed feedback and control (which is instead a strength of GI). As such, we propose the investigation of a new research line on AI-powered GI aimed at incorporating semantic aware search. We take a first step at it by augmenting GI with the use of automated clustering of LLM edits. We provide initial empirical evidence that our proposal, dubbed PatchCat, allows us to automatically and effectively categorize LLM-suggested patches. PatchCat identified 18 different types of software patches and categorized newly suggested patches with high accuracy. It also enabled detecting NoOp edits in advance and, prospectively, to skip test suite execution to save resources in many cases. These results, coupled with the fact that PatchCat works with small, local LLMs, are a promising step toward interpretable, efficient, and green GI. We outline a rich agenda of future work and call for the community to join our vision of building a principled understanding of LLM-driven mutations, guiding the GI search process with semantic signals.

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arXiv2024

Exploring LLM-Driven Explanations for Quantum Algorithms

Background: Quantum computing is a rapidly growing new programming paradigm that brings significant changes to the design and implementation of algorithms. Understanding quantum algorithms requires knowledge of physics and mathematics, which can be challenging for software developers. Aims: In this work, we provide a first analysis of how LLMs can support developers' understanding of quantum code. Method: We empirically analyse and compare the quality of explanations provided by three widely adopted LLMs (Gpt3.5, Llama2, and Tinyllama) using two different human-written prompt styles for seven state-of-the-art quantum algorithms. We also analyse how consistent LLM explanations are over multiple rounds and how LLMs can improve existing descriptions of quantum algorithms. Results: Llama2 provides the highest quality explanations from scratch, while Gpt3.5 emerged as the LLM best suited to improve existing explanations. In addition, we show that adding a small amount of context to the prompt significantly improves the quality of explanations. Finally, we observe how explanations are qualitatively and syntactically consistent over multiple rounds. Conclusions: This work highlights promising results, and opens challenges for future research in the field of LLMs for quantum code explanation. Future work includes refining the methods through prompt optimisation and parsing of quantum code explanations, as well as carrying out a systematic assessment of the quality of explanations.

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arXiv2024

Hot Fixing Software: A Comprehensive Review of Terminology, Techniques, and Applications

A hot fix is an unplanned improvement to a specific time-critical issue deployed to a software system in production. While hot fixing is an essential and common activity in software maintenance, it has never been surveyed as a research activity. Thus, such a review is long overdue. In this paper, we conduct a comprehensive literature review of work on hot fixing. We highlight the fields where this topic has been addressed, inconsistencies we identified in the terminology, gaps in the literature, and directions for future work. Our search concluded with 91 articles on the topic between the years 2000 and 2022. The articles found encompass many different research areas such as log analysis, runtime patching (also known as hot patching), and automated repair, as well as various application domains such as security, mobile, and video games. We find that many directions can take hot fix research forward such as unifying existing terminology, establishing a benchmark set of hot fixes, researching costs and frequency of hot fixes, and researching the possibility of end-to-end automation of detection, mitigation, and deployment. We discuss these avenues in detail to inspire the community to systematize hot fixing as a software engineering activity.

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arXiv2024

Reinforcement Learning for Mutation Operator Selection in Automated Program Repair

Automated program repair techniques aim to aid software developers with the challenging task of fixing bugs. In heuristic-based program repair, a search space of program variants, created via mutations on software, is explored to find potential patches for bugs. Most commonly, every selection of a mutation operator during search is performed uniformly at random, whcih can generate many buggy, even uncompilable program variants. Our goal is to reduce the generation of variants that do not compile or break intended functionality which waste considerable resources. In this paper, we investigate the feasibility of a reinforcement learning-based approach for the selection of mutation operators in heuristic-based program repair. Our proposed approach is programming language, granularity-level, and search strategy agnostic and allows for easy augmentation into existing heuristic-based repair tools. We conduct an extensive empirical evaluation of four operator selection techniques, two reward types, two credit assignment strategies, two integration methods, and three sets of mutation operators using 30,080 independent repair attempts. We evaluate our approach on 353 real-world bugs from the Defects4J benchmark.The reinforcement learning-based mutation operator selection results in a higher number of test-passing variants, but does not exhibit a noticeable improvement in the number of bugs patched in comparison with the baseline, which uses random selection. While reinforcement learning has been previously shown to be successful in improving the search of evolutionary algorithms, often used in heuristic-based program repair, it has not shown such improvements when applied to this area of research.

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arXiv2023

Enhancing Genetic Improvement Mutations Using Large Language Models

Large language models (LLMs) have been successfully applied to software engineering tasks, including program repair. However, their application in search-based techniques such as Genetic Improvement (GI) is still largely unexplored. In this paper, we evaluate the use of LLMs as mutation operators for GI to improve the search process. We expand the Gin Java GI toolkit to call OpenAI's API to generate edits for the JCodec tool. We randomly sample the space of edits using 5 different edit types. We find that the number of patches passing unit tests is up to 75% higher with LLM-based edits than with standard Insert edits. Further, we observe that the patches found with LLMs are generally less diverse compared to standard edits. We ran GI with local search to find runtime improvements. Although many improving patches are found by LLM-enhanced GI, the best improving patch was found by standard GI.

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arXiv2023

An Analysis of the Automatic Bug Fixing Performance of ChatGPT

To support software developers in finding and fixing software bugs, several automated program repair techniques have been introduced. Given a test suite, standard methods usually either synthesize a repair, or navigate a search space of software edits to find test-suite passing variants. Recent program repair methods are based on deep learning approaches. One of these novel methods, which is not primarily intended for automated program repair, but is still suitable for it, is ChatGPT. The bug fixing performance of ChatGPT, however, is so far unclear. Therefore, in this paper we evaluate ChatGPT on the standard bug fixing benchmark set, QuixBugs, and compare the performance with the results of several other approaches reported in the literature. We find that ChatGPT's bug fixing performance is competitive to the common deep learning approaches CoCoNut and Codex and notably better than the results reported for the standard program repair approaches. In contrast to previous approaches, ChatGPT offers a dialogue system through which further information, e.g., the expected output for a certain input or an observed error message, can be entered. By providing such hints to ChatGPT, its success rate can be further increased, fixing 31 out of 40 bugs, outperforming state-of-the-art.

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