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5th International Workshop on Quantum Software Engineering (Q-SE)

作者： Guo, Xiaoyu Zhao, Jianjun Zhao, Pengzhan Kyushu Univ Fukuoka Japan Kyuhu Univ Fukuoka Japan

ISBN: (纸本)9798400705700

Automated program repair (APR) is a vital area in software engineering that generates automatic patches for vulnerable programs. While numerous techniques have been proposed for repairing classical programs, quantum programming lacks a comparable automated repair technique. In this initial exploration, we investigate the use of ChatGPT for quantum program repair and evaluate its performance on Bugs4Q, a benchmark suite of quantum program bugs. Our findings demonstrate the feasibility of employing ChatGPT for quantum program repair. Specifically, we assess ChatGPT's ability to address bugs within the Bugs4Q benchmark, revealing its success in repairing 29 out of 38 bugs. This research represents a promising step towards automating the repair process for quantum programs.

关键词： automatic program repair Quantum programming Debugging

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Go the Extra Mile: Fixing Propagated Error-Handling Bugs 32

Go the Extra Mile: Fixing Propagated Error-Handling Bugs

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32nd ACM International Conference on the Foundations of Software Engineering (FSE)

作者： Liu, Haoran Jia, Zhouyang Zhou, Huiping Zhou, Haifang Li, Shanshan Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9798400706585

Error handling bugs are widespread in software, compromising its reliability. In C/C++ environments, error-handling bugs are often propagated to multiple functions through return values. This paper introduces EH-Fixer, a conversation-based automated method for fixing propagated error-handling (PEH) bugs. EH-Fixer employs LLM in a conversation style, utilizing information retrieval to address PEH bugs. We constructed a dataset containing 30 PEH bugs and evaluated EH-Fixer against two state-of-the-art approaches. Preliminary results indicate that EH-Fixer successfully fixed 19 more PEH bugs than existing approaches.

关键词： Error-Handling Bug automatic program repair

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AutoCodeRover: Autonomous program Improvement 2024

AutoCodeRover: Autonomous Program Improvement

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33rd ACM SIGSOFT International Conference on Software Testing and Analysis (ISSTA)

作者： Zhang, Yuntong Ruan, Haifeng Fan, Zhiyu Roychoudhury, Abhik Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9798400706127

Researchers have made significant progress in automating the software development process in the past, decades. Automated techniques for issue summarization, bug reproduction, fault localization, and program repair have been built to ease the workload of developers. Recent progress in Large Language Models (LLMs) has significantly impacted the development process, where developers can use LLM-based programming assistants to achieve automated coding. Nevertheless, software engineering involves the process of program improvement apart from coding, specifically to enable software maintenance (e.g. program repair to fix bugs) and software evolution (e.g. feature additions). In this paper, we propose an automated approach for solving Github issues to autonomously achieve program improvement. In our approach called AUTOCODEROVER, LLMs are combined with sophisticated code search capabilities, ultimately leading to a program modification or patch. In contrast to recent LLM agent approaches from AI researchers and practitioners, our outlook is more software engineering oriented. We work on a program representation (abstract syntax tree) as opposed to viewing a software project as a mere collection of files. Our code search exploits the program structure in the form of classes/methods to enhance LLM's understanding of the issue's root cause, and effectively retrieve a context via iterative search. The use of spectrum-based fault localization using tests, further sharpens the context, as long as a test-suite is available. Experiments on the recently proposed SWE-bench-lice (300 real-life Github issues) show increased efficacy in solving Github issues (19% on SWE-bench-lite), which is higher than the efficacy of the recently reported SWE-AGENT. Interestingly, our approach resolved 57 GitHub issues in about 4 minutes each (pass@1), Whereas developers spent more than 2.68 days on average. In addition, AUTOCODEROVER achieved this efficacy with significantly lower cost (on average,

关键词： large language model automatic program repair autonomous software engineering autonomous software improvement

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repairing Infrastructure-as-Code using Large Language Models

Repairing Infrastructure-as-Code using Large Language Models

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IEEE Secure Development Conference (IEEE SecDev)

作者： Low, En Cheh, Carmen Chen, Binbin Singapore Univ Technol & Design Singapore 487372 Singapore Singapore Ltd Illinois Adv Res Ctr Singapore 138602 Singapore

ISBN: (纸本)9798350391947;9798350391930

Infrastructure-as-Code (IaC) is the practice of provisioning and managing cloud resources using machine-readable code. IaC is seeing increased adoption because it enhances transparency and reliability of infrastructure operations. However, as any software code, IaC can also contain misconfigurations, which can lead to insecure infrastructure, which may result in data breaches. Existing IaC scanning tools are able to detect common misconfigurations in IaC but they require IaC developers to manually repair the code. Recent advances in Large Language Models (LLMs) have led to promising results in applying LLMs to automatic program repair (APR) tasks for code written in different languages. In this work, we propose an LLM-based approach to fix misconfigurations in IaC code. After misconfigurations in IaC code are identified by scanning tools, we feed the LLMs with the IaC code, details about the misconfigurations, and additional context provided by a human-in-the-loop and prompt the LLM to generate the repaired IaC code. We tested our approach on several vulnerable IaC repositories and found that the GPT-4 model from OpenAI suggests fixes that reduce up to 84.7% of the misconfiguration alarms produced by the scanners and our two-pass solution significantly improves the performance over a one-pass only approach. However, of the fixes suggested, we manually determined that only 79.6% actually solve the problem, while the remaining 20.4% are hallucinated fixes. Specifically, LLM hallucinations in the generated outputs pass checks for misconfigurations but fail other syntax and schema validation checks or do not address the underlying security issue. We propose a few potential approaches to tackle this challenge.

关键词： infrastructure as code large language model automatic program repair

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VarFix: Balancing Edit Expressiveness and Search Effectiveness in Automated program repair 2021

VarFix: Balancing Edit Expressiveness and Search Effectivene...

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29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)

作者： Wong, Chu-Pan Santiesteban, Priscila Kastner, Christian Le Goues, Claire Carnegie Mellon Univ Pittsburgh PA 15213 USA Coe Coll Cedar Rapids IA 52402 USA

ISBN: (纸本)9781450385626

automatically repairing a buggy program is essentially a search problem, searching for code transformations that pass a set of tests. Various search strategies have been explored, but they either navigate the search space in an ad hoc way using heuristics, or systemically but at the cost of limited edit expressiveness in the kinds of supported program edits. In this work, we explore the possibility of systematically navigating the search space without sacrificing edit expressiveness. The key enabler of this exploration is variational execution, a dynamic analysis technique that has been shown to be effective at exploring many similar executions in large search spaces. We evaluate our approach on INTROCLASSJAVA and DE-FECTS4J, showing that a systematic search is effective at leveraging and combining fixing ingredients to find patches, including many high-quality patches and multi-edit patches.

关键词： automatic program repair variational execution

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Automated Classification of Overfitting Patches With Statically Extracted Code Features

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2022年第8期48卷 2920-2938页

作者： Ye, He Gu, Jian Martinez, Matias Durieux, Thomas Monperrus, Martin KTH Royal Inst Technol S-11428 Stockholm Sweden KTH Royal Inst Technol Software Technol S-11428 Stockholm Sweden Univ Polytech Hauts de France F-59300 Famars France

automatic program repair (APR) aims to reduce the cost of manually fixing software defects. However, APR suffers from generating a multitude of overfitting patches, those patches that fail to correctly repair the defect beyond making the tests pass. This paper presents a novel overfitting patch detection system called ODS to assess the correctness of APR patches. ODS first statically compares a patched program and a buggy program in order to extract code features at the abstract syntax tree (AST) level, for the single programming language Java. Then, ODS uses supervised learning with the captured code features and patch correctness labels to automatically learn a probabilistic model. The learned ODS model can then finally be applied to classify new and unseen program repair patches. We conduct a large-scale experiment to evaluate the effectiveness of ODS on patch correctness classification based on 10,302 patches from Defects4J, *** and Bears benchmarks. The empirical evaluation shows that ODS is able to correctly classify 71.9 percent of program repair patches from 26 projects, which improves the state-of-the-art. ODS is applicable in practice and can be employed as a post-processing procedure to classify the patches generated by different APR systems.

关键词： Feature extraction Maintenance engineering Training Tools Syntactics Software Predictive models automatic program repair patch assessment overfitting patch code features

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A large-scale study on human-cloned changes for automated program repair 18

A large-scale study on human-cloned changes for automated pr...

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29th IEEE/ACM International Conference on program Comprehension (ICPC) / 18th IEEE/ACM International Conference on Mining Software Repositories (MSR)

作者： Madeiral, Fernanda Durieux, Thomas KTH Royal Inst Technol Stockholm Sweden

ISBN: (纸本)9781728187105

Research in automatic program repair has shown that real bugs can be automatically fixed. However, there are several challenges involved in such a task that are not yet fully addressed. As an example, consider that a test-suite-based repair tool performs a change in a program to fix a bug spotted by a failing test case, but then the same or another test case fails. This could mean that the change is a partial fix for the bug or that another bug was manifested. However, the repair tool discards the change and possibly performs other repair attempts. One might wonder if the applied change should be also applied in other locations in the program so that the bug is fully fixed. In this paper, we are interested in investigating the extent of bug fix changes being cloned by developers within patches. Our goal is to investigate the need of multi-location repair by using identical or similar changes in identical or similar contexts. To do so, we analyzed 3,049 multi-hunk patches from the ManySStuBs4J dataset, which is a large dataset of single statement bug fix changes. We found out that 68% of the multi-hunk patches contain at least one change clone group. Moreover, most of these patches (70%) are strictly-cloned ones, which are patches fully composed of changes belonging to one single change clone group. Finally, most of the strictly-cloned patches (89%) contain change clones with identical changes, independently of their contexts. We conclude that automated solutions for creating patches composed of identical or similar changes can be useful for fixing bugs.

关键词： automatic program repair patch change clone

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Exception-Driven Fault Localization for Automated program repair 21

Exception-Driven Fault Localization for Automated Program Re...

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21st IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Ginelli, Davide Riganelli, Oliviero Micucci, Daniela Mariani, Leonardo Univ Milano Bicocca Milan Italy

ISBN: (纸本)9781665458139

Automated program repair (APR) techniques typically exploit spectrum-based fault localization (SBFL) to identify the program locations that should be patched, making the effectiveness of APR techniques dependent on the effectiveness of fault localization. Indeed, results show that SBFL often does not localize faults accurately, hindering the effectiveness of APR. In this paper, we propose EXCEPT, a technique that addresses the localization problem by focusing on the semantics of failures rather than on the correlation between the executed statements and the failed tests, as SBFL does. We focus on failures due to exceptions and we exploit their type and source to localize and guess the faults. Experiments with 43 exception-raising faults from the Defects4J benchmark show that EXCEPT can perform better than Ochiai and ssFix.

关键词： automatic program repair fault localization SBFL exceptions

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Multi-objective Optimization-based Bug-fixing Template Mining for Automated program repair 22

Multi-objective Optimization-based Bug-fixing Template Minin...

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Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering

作者： Misoo Kim Youngkyoung Kim Kicheol Kim Eunseok Lee SungKyunKwan University Republic of Korea Sungkyunkwan University Republic of Korea

ISBN: (纸本)9781450394758

Template-based automatic program repair (T-APR) techniques depend on the quality of bug-fixing templates. For such templates to be of sufficient quality for T-APR techniques to succeed, they must satisfy three criteria: applicability, fixability, and efficiency. Existing template mining approaches select templates based only on the first criteria, and are thus suboptimal in their performance. This study proposes a multi-objective optimization-based bug-fixing template mining method for T-APR in which we estimate template quality based on nine code abstraction tasks and three objective functions. Our method determines the optimal code abstraction strategy (i.e., the optimal combination of abstraction tasks) which maximizes the values of three objective functions and generates a final set of bug-fixing templates by clustering template candidates to which the optimal abstraction strategy is applied. Our preliminary experiment demonstrated that our optimized strategy can improve templates’ applicability and efficiency by 7% and 146% over the existing mining technique, respectively. We therefore conclude that the multi-objective optimization-based template mining technique effectively finds high-quality bug-fixing templates.

关键词： Bug-fixing template mining automatic program repair NSGA-II Multi-objective optimization

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Exploring automatic Specification repair in Dafny programs 38

Exploring Automatic Specification Repair in Dafny Programs

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38th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Abreu, Alexandre Macedo, Nuno Mendes, Alexandra Univ Porto Fac Engn Dept Informat Engn Porto Portugal INESC TEC Porto Portugal

ISBN: (纸本)9798350330328

Formal verification has become increasingly crucial in ensuring the accurate and secure functioning of modern software systems. Given a specification of the desired behaviour, i.e. a contract, a program is considered to be correct when all possible executions guarantee the specification. Should the software fail to behave as expected, then a bug is present. Most existing research assumes that the bug is present in the implementation, but it is also often the case that the specified expectations are incorrect, meaning that it is the specification that must be repaired. Research and tools for providing alternative specifications that fix details missing during contract definition, considering that the implementation is correct, are scarce. This paper presents a preliminary tool, focused on Dafny programs, for automatic specification repair in contract programming. Given a Dafny program that fails to verify, the tool suggests corrections that repair the specification. Our approach is inspired by a technique previously proposed for another contract programming language and relies on Daikon for dynamic invariant inference. Although the tool is focused on Dafny, it makes use of specification repair techniques that are generally applicable to programming languages that support contracts. Such a tool can be valuable in various scenarios, such as when programmers have a reference implementation and need to analyse their contract options, or in educational contexts, where it can provide students with hints to correct their contracts. The results of the evaluation show that the approach is feasible in Dafny and that the overall process has reasonable performance but that there are stages of the process that need further improvements.

关键词： contract programming automatic program repair contract repair Dafny

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